diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/_meta.json b/sdk/machinelearning/azure-mgmt-machinelearningservices/_meta.json
index 216b79b265701..de899912e7b1a 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/_meta.json
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/_meta.json
@@ -1,11 +1,11 @@
 {
-  "commit": "b32e1896f30e6ea155449cb49719a6286e32b961",
+  "commit": "db6f7414f95b0925dca0b5cc23619d3b3760e3cb",
   "repository_url": "https://github.com/Azure/azure-rest-api-specs",
   "autorest": "3.9.2",
   "use": [
-    "@autorest/python@6.2.7",
+    "@autorest/python@6.2.16",
     "@autorest/modelerfour@4.24.3"
   ],
-  "autorest_command": "autorest specification/machinelearningservices/resource-manager/readme.md --generate-sample=True --include-x-ms-examples-original-file=True --python --python-sdks-folder=/home/vsts/work/1/azure-sdk-for-python/sdk --use=@autorest/python@6.2.7 --use=@autorest/modelerfour@4.24.3 --version=3.9.2 --version-tolerant=False",
+  "autorest_command": "autorest specification/machinelearningservices/resource-manager/readme.md --generate-sample=True --include-x-ms-examples-original-file=True --python --python-sdks-folder=/mnt/vss/_work/1/s/azure-sdk-for-python/sdk --use=@autorest/python@6.2.16 --use=@autorest/modelerfour@4.24.3 --version=3.9.2 --version-tolerant=False",
   "readme": "specification/machinelearningservices/resource-manager/readme.md"
 }
\ No newline at end of file
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_configuration.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_configuration.py
index 12c43638a412f..d0603e787826e 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_configuration.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_configuration.py
@@ -35,14 +35,14 @@ class MachineLearningServicesMgmtClientConfiguration(Configuration):  # pylint:
     :type credential: ~azure.core.credentials.TokenCredential
     :param subscription_id: The ID of the target subscription. Required.
     :type subscription_id: str
-    :keyword api_version: Api Version. Default value is "2022-10-01". Note that overriding this
-     default value may result in unsupported behavior.
+    :keyword api_version: Api Version. Default value is "2022-12-01-preview". Note that overriding
+     this default value may result in unsupported behavior.
     :paramtype api_version: str
     """
 
     def __init__(self, credential: "TokenCredential", subscription_id: str, **kwargs: Any) -> None:
         super(MachineLearningServicesMgmtClientConfiguration, self).__init__(**kwargs)
-        api_version: Literal["2022-10-01"] = kwargs.pop("api_version", "2022-10-01")
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop("api_version", "2022-12-01-preview")
 
         if credential is None:
             raise ValueError("Parameter 'credential' must not be None.")
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_machine_learning_services_mgmt_client.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_machine_learning_services_mgmt_client.py
index feffb8cfc3ed8..c3f13fc736182 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_machine_learning_services_mgmt_client.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_machine_learning_services_mgmt_client.py
@@ -29,6 +29,7 @@
     EnvironmentContainersOperations,
     EnvironmentVersionsOperations,
     JobsOperations,
+    LabelingJobsOperations,
     ModelContainersOperations,
     ModelVersionsOperations,
     OnlineDeploymentsOperations,
@@ -37,6 +38,15 @@
     PrivateEndpointConnectionsOperations,
     PrivateLinkResourcesOperations,
     QuotasOperations,
+    RegistriesOperations,
+    RegistryCodeContainersOperations,
+    RegistryCodeVersionsOperations,
+    RegistryComponentContainersOperations,
+    RegistryComponentVersionsOperations,
+    RegistryEnvironmentContainersOperations,
+    RegistryEnvironmentVersionsOperations,
+    RegistryModelContainersOperations,
+    RegistryModelVersionsOperations,
     SchedulesOperations,
     UsagesOperations,
     VirtualMachineSizesOperations,
@@ -75,6 +85,30 @@ class MachineLearningServicesMgmtClient:  # pylint: disable=client-accepts-api-v
     :ivar workspace_connections: WorkspaceConnectionsOperations operations
     :vartype workspace_connections:
      azure.mgmt.machinelearningservices.operations.WorkspaceConnectionsOperations
+    :ivar registry_code_containers: RegistryCodeContainersOperations operations
+    :vartype registry_code_containers:
+     azure.mgmt.machinelearningservices.operations.RegistryCodeContainersOperations
+    :ivar registry_code_versions: RegistryCodeVersionsOperations operations
+    :vartype registry_code_versions:
+     azure.mgmt.machinelearningservices.operations.RegistryCodeVersionsOperations
+    :ivar registry_component_containers: RegistryComponentContainersOperations operations
+    :vartype registry_component_containers:
+     azure.mgmt.machinelearningservices.operations.RegistryComponentContainersOperations
+    :ivar registry_component_versions: RegistryComponentVersionsOperations operations
+    :vartype registry_component_versions:
+     azure.mgmt.machinelearningservices.operations.RegistryComponentVersionsOperations
+    :ivar registry_environment_containers: RegistryEnvironmentContainersOperations operations
+    :vartype registry_environment_containers:
+     azure.mgmt.machinelearningservices.operations.RegistryEnvironmentContainersOperations
+    :ivar registry_environment_versions: RegistryEnvironmentVersionsOperations operations
+    :vartype registry_environment_versions:
+     azure.mgmt.machinelearningservices.operations.RegistryEnvironmentVersionsOperations
+    :ivar registry_model_containers: RegistryModelContainersOperations operations
+    :vartype registry_model_containers:
+     azure.mgmt.machinelearningservices.operations.RegistryModelContainersOperations
+    :ivar registry_model_versions: RegistryModelVersionsOperations operations
+    :vartype registry_model_versions:
+     azure.mgmt.machinelearningservices.operations.RegistryModelVersionsOperations
     :ivar batch_endpoints: BatchEndpointsOperations operations
     :vartype batch_endpoints:
      azure.mgmt.machinelearningservices.operations.BatchEndpointsOperations
@@ -107,6 +141,8 @@ class MachineLearningServicesMgmtClient:  # pylint: disable=client-accepts-api-v
      azure.mgmt.machinelearningservices.operations.EnvironmentVersionsOperations
     :ivar jobs: JobsOperations operations
     :vartype jobs: azure.mgmt.machinelearningservices.operations.JobsOperations
+    :ivar labeling_jobs: LabelingJobsOperations operations
+    :vartype labeling_jobs: azure.mgmt.machinelearningservices.operations.LabelingJobsOperations
     :ivar model_containers: ModelContainersOperations operations
     :vartype model_containers:
      azure.mgmt.machinelearningservices.operations.ModelContainersOperations
@@ -120,6 +156,8 @@ class MachineLearningServicesMgmtClient:  # pylint: disable=client-accepts-api-v
      azure.mgmt.machinelearningservices.operations.OnlineDeploymentsOperations
     :ivar schedules: SchedulesOperations operations
     :vartype schedules: azure.mgmt.machinelearningservices.operations.SchedulesOperations
+    :ivar registries: RegistriesOperations operations
+    :vartype registries: azure.mgmt.machinelearningservices.operations.RegistriesOperations
     :ivar workspace_features: WorkspaceFeaturesOperations operations
     :vartype workspace_features:
      azure.mgmt.machinelearningservices.operations.WorkspaceFeaturesOperations
@@ -129,8 +167,8 @@ class MachineLearningServicesMgmtClient:  # pylint: disable=client-accepts-api-v
     :type subscription_id: str
     :param base_url: Service URL. Default value is "https://management.azure.com".
     :type base_url: str
-    :keyword api_version: Api Version. Default value is "2022-10-01". Note that overriding this
-     default value may result in unsupported behavior.
+    :keyword api_version: Api Version. Default value is "2022-12-01-preview". Note that overriding
+     this default value may result in unsupported behavior.
     :paramtype api_version: str
     :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
      Retry-After header is present.
@@ -169,6 +207,30 @@ def __init__(
         self.workspace_connections = WorkspaceConnectionsOperations(
             self._client, self._config, self._serialize, self._deserialize
         )
+        self.registry_code_containers = RegistryCodeContainersOperations(
+            self._client, self._config, self._serialize, self._deserialize
+        )
+        self.registry_code_versions = RegistryCodeVersionsOperations(
+            self._client, self._config, self._serialize, self._deserialize
+        )
+        self.registry_component_containers = RegistryComponentContainersOperations(
+            self._client, self._config, self._serialize, self._deserialize
+        )
+        self.registry_component_versions = RegistryComponentVersionsOperations(
+            self._client, self._config, self._serialize, self._deserialize
+        )
+        self.registry_environment_containers = RegistryEnvironmentContainersOperations(
+            self._client, self._config, self._serialize, self._deserialize
+        )
+        self.registry_environment_versions = RegistryEnvironmentVersionsOperations(
+            self._client, self._config, self._serialize, self._deserialize
+        )
+        self.registry_model_containers = RegistryModelContainersOperations(
+            self._client, self._config, self._serialize, self._deserialize
+        )
+        self.registry_model_versions = RegistryModelVersionsOperations(
+            self._client, self._config, self._serialize, self._deserialize
+        )
         self.batch_endpoints = BatchEndpointsOperations(self._client, self._config, self._serialize, self._deserialize)
         self.batch_deployments = BatchDeploymentsOperations(
             self._client, self._config, self._serialize, self._deserialize
@@ -191,6 +253,7 @@ def __init__(
             self._client, self._config, self._serialize, self._deserialize
         )
         self.jobs = JobsOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.labeling_jobs = LabelingJobsOperations(self._client, self._config, self._serialize, self._deserialize)
         self.model_containers = ModelContainersOperations(
             self._client, self._config, self._serialize, self._deserialize
         )
@@ -202,6 +265,7 @@ def __init__(
             self._client, self._config, self._serialize, self._deserialize
         )
         self.schedules = SchedulesOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.registries = RegistriesOperations(self._client, self._config, self._serialize, self._deserialize)
         self.workspace_features = WorkspaceFeaturesOperations(
             self._client, self._config, self._serialize, self._deserialize
         )
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_serialization.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_serialization.py
index 2c170e28dbca2..f17c068e833e8 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_serialization.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_serialization.py
@@ -38,7 +38,22 @@
 import re
 import sys
 import codecs
-from typing import Optional, Union, AnyStr, IO, Mapping
+from typing import (
+    Dict,
+    Any,
+    cast,
+    Optional,
+    Union,
+    AnyStr,
+    IO,
+    Mapping,
+    Callable,
+    TypeVar,
+    MutableMapping,
+    Type,
+    List,
+    Mapping,
+)
 
 try:
     from urllib import quote  # type: ignore
@@ -48,12 +63,14 @@
 
 import isodate  # type: ignore
 
-from typing import Dict, Any, cast
-
 from azure.core.exceptions import DeserializationError, SerializationError, raise_with_traceback
+from azure.core.serialization import NULL as AzureCoreNull
 
 _BOM = codecs.BOM_UTF8.decode(encoding="utf-8")
 
+ModelType = TypeVar("ModelType", bound="Model")
+JSON = MutableMapping[str, Any]
+
 
 class RawDeserializer:
 
@@ -277,8 +294,8 @@ class Model(object):
     _attribute_map: Dict[str, Dict[str, Any]] = {}
     _validation: Dict[str, Dict[str, Any]] = {}
 
-    def __init__(self, **kwargs):
-        self.additional_properties = {}
+    def __init__(self, **kwargs: Any) -> None:
+        self.additional_properties: Dict[str, Any] = {}
         for k in kwargs:
             if k not in self._attribute_map:
                 _LOGGER.warning("%s is not a known attribute of class %s and will be ignored", k, self.__class__)
@@ -287,25 +304,25 @@ def __init__(self, **kwargs):
             else:
                 setattr(self, k, kwargs[k])
 
-    def __eq__(self, other):
+    def __eq__(self, other: Any) -> bool:
         """Compare objects by comparing all attributes."""
         if isinstance(other, self.__class__):
             return self.__dict__ == other.__dict__
         return False
 
-    def __ne__(self, other):
+    def __ne__(self, other: Any) -> bool:
         """Compare objects by comparing all attributes."""
         return not self.__eq__(other)
 
-    def __str__(self):
+    def __str__(self) -> str:
         return str(self.__dict__)
 
     @classmethod
-    def enable_additional_properties_sending(cls):
+    def enable_additional_properties_sending(cls) -> None:
         cls._attribute_map["additional_properties"] = {"key": "", "type": "{object}"}
 
     @classmethod
-    def is_xml_model(cls):
+    def is_xml_model(cls) -> bool:
         try:
             cls._xml_map  # type: ignore
         except AttributeError:
@@ -322,7 +339,7 @@ def _create_xml_node(cls):
 
         return _create_xml_node(xml_map.get("name", cls.__name__), xml_map.get("prefix", None), xml_map.get("ns", None))
 
-    def serialize(self, keep_readonly=False, **kwargs):
+    def serialize(self, keep_readonly: bool = False, **kwargs: Any) -> JSON:
         """Return the JSON that would be sent to azure from this model.
 
         This is an alias to `as_dict(full_restapi_key_transformer, keep_readonly=False)`.
@@ -336,8 +353,13 @@ def serialize(self, keep_readonly=False, **kwargs):
         serializer = Serializer(self._infer_class_models())
         return serializer._serialize(self, keep_readonly=keep_readonly, **kwargs)
 
-    def as_dict(self, keep_readonly=True, key_transformer=attribute_transformer, **kwargs):
-        """Return a dict that can be JSONify using json.dump.
+    def as_dict(
+        self,
+        keep_readonly: bool = True,
+        key_transformer: Callable[[str, Dict[str, Any], Any], Any] = attribute_transformer,
+        **kwargs: Any
+    ) -> JSON:
+        """Return a dict that can be serialized using json.dump.
 
         Advanced usage might optionally use a callback as parameter:
 
@@ -384,7 +406,7 @@ def _infer_class_models(cls):
         return client_models
 
     @classmethod
-    def deserialize(cls, data, content_type=None):
+    def deserialize(cls: Type[ModelType], data: Any, content_type: Optional[str] = None) -> ModelType:
         """Parse a str using the RestAPI syntax and return a model.
 
         :param str data: A str using RestAPI structure. JSON by default.
@@ -396,7 +418,12 @@ def deserialize(cls, data, content_type=None):
         return deserializer(cls.__name__, data, content_type=content_type)
 
     @classmethod
-    def from_dict(cls, data, key_extractors=None, content_type=None):
+    def from_dict(
+        cls: Type[ModelType],
+        data: Any,
+        key_extractors: Optional[Callable[[str, Dict[str, Any], Any], Any]] = None,
+        content_type: Optional[str] = None,
+    ) -> ModelType:
         """Parse a dict using given key extractor return a model.
 
         By default consider key
@@ -409,8 +436,8 @@ def from_dict(cls, data, key_extractors=None, content_type=None):
         :raises: DeserializationError if something went wrong
         """
         deserializer = Deserializer(cls._infer_class_models())
-        deserializer.key_extractors = (
-            [
+        deserializer.key_extractors = (  # type: ignore
+            [  # type: ignore
                 attribute_key_case_insensitive_extractor,
                 rest_key_case_insensitive_extractor,
                 last_rest_key_case_insensitive_extractor,
@@ -518,7 +545,7 @@ class Serializer(object):
         "multiple": lambda x, y: x % y != 0,
     }
 
-    def __init__(self, classes=None):
+    def __init__(self, classes: Optional[Mapping[str, Type[ModelType]]] = None):
         self.serialize_type = {
             "iso-8601": Serializer.serialize_iso,
             "rfc-1123": Serializer.serialize_rfc,
@@ -534,7 +561,7 @@ def __init__(self, classes=None):
             "[]": self.serialize_iter,
             "{}": self.serialize_dict,
         }
-        self.dependencies = dict(classes) if classes else {}
+        self.dependencies: Dict[str, Type[ModelType]] = dict(classes) if classes else {}
         self.key_transformer = full_restapi_key_transformer
         self.client_side_validation = True
 
@@ -626,8 +653,7 @@ def _serialize(self, target_obj, data_type=None, **kwargs):
                             serialized.append(local_node)  # type: ignore
                     else:  # JSON
                         for k in reversed(keys):  # type: ignore
-                            unflattened = {k: new_attr}
-                            new_attr = unflattened
+                            new_attr = {k: new_attr}
 
                         _new_attr = new_attr
                         _serialized = serialized
@@ -656,8 +682,8 @@ def body(self, data, data_type, **kwargs):
         """
 
         # Just in case this is a dict
-        internal_data_type = data_type.strip("[]{}")
-        internal_data_type = self.dependencies.get(internal_data_type, None)
+        internal_data_type_str = data_type.strip("[]{}")
+        internal_data_type = self.dependencies.get(internal_data_type_str, None)
         try:
             is_xml_model_serialization = kwargs["is_xml"]
         except KeyError:
@@ -777,6 +803,8 @@ def serialize_data(self, data, data_type, **kwargs):
             raise ValueError("No value for given attribute")
 
         try:
+            if data is AzureCoreNull:
+                return None
             if data_type in self.basic_types.values():
                 return self.serialize_basic(data, data_type, **kwargs)
 
@@ -1161,7 +1189,8 @@ def rest_key_extractor(attr, attr_desc, data):
     working_data = data
 
     while "." in key:
-        dict_keys = _FLATTEN.split(key)
+        # Need the cast, as for some reasons "split" is typed as list[str | Any]
+        dict_keys = cast(List[str], _FLATTEN.split(key))
         if len(dict_keys) == 1:
             key = _decode_attribute_map_key(dict_keys[0])
             break
@@ -1332,7 +1361,7 @@ class Deserializer(object):
 
     valid_date = re.compile(r"\d{4}[-]\d{2}[-]\d{2}T\d{2}:\d{2}:\d{2}" r"\.?\d*Z?[-+]?[\d{2}]?:?[\d{2}]?")
 
-    def __init__(self, classes=None):
+    def __init__(self, classes: Optional[Mapping[str, Type[ModelType]]] = None):
         self.deserialize_type = {
             "iso-8601": Deserializer.deserialize_iso,
             "rfc-1123": Deserializer.deserialize_rfc,
@@ -1352,7 +1381,7 @@ def __init__(self, classes=None):
             "duration": (isodate.Duration, datetime.timedelta),
             "iso-8601": (datetime.datetime),
         }
-        self.dependencies = dict(classes) if classes else {}
+        self.dependencies: Dict[str, Type[ModelType]] = dict(classes) if classes else {}
         self.key_extractors = [rest_key_extractor, xml_key_extractor]
         # Additional properties only works if the "rest_key_extractor" is used to
         # extract the keys. Making it to work whatever the key extractor is too much
@@ -1471,7 +1500,7 @@ def _classify_target(self, target, data):
         Once classification has been determined, initialize object.
 
         :param str target: The target object type to deserialize to.
-        :param str/dict data: The response data to deseralize.
+        :param str/dict data: The response data to deserialize.
         """
         if target is None:
             return None, None
@@ -1486,7 +1515,7 @@ def _classify_target(self, target, data):
             target = target._classify(data, self.dependencies)
         except AttributeError:
             pass  # Target is not a Model, no classify
-        return target, target.__class__.__name__
+        return target, target.__class__.__name__  # type: ignore
 
     def failsafe_deserialize(self, target_obj, data, content_type=None):
         """Ignores any errors encountered in deserialization,
@@ -1496,7 +1525,7 @@ def failsafe_deserialize(self, target_obj, data, content_type=None):
         a deserialization error.
 
         :param str target_obj: The target object type to deserialize to.
-        :param str/dict data: The response data to deseralize.
+        :param str/dict data: The response data to deserialize.
         :param str content_type: Swagger "produces" if available.
         """
         try:
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_vendor.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_vendor.py
index 9aad73fc743e7..bd0df84f5319f 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_vendor.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_vendor.py
@@ -5,6 +5,8 @@
 # Changes may cause incorrect behavior and will be lost if the code is regenerated.
 # --------------------------------------------------------------------------
 
+from typing import List, cast
+
 from azure.core.pipeline.transport import HttpRequest
 
 
@@ -22,6 +24,7 @@ def _format_url_section(template, **kwargs):
         try:
             return template.format(**kwargs)
         except KeyError as key:
-            formatted_components = template.split("/")
+            # Need the cast, as for some reasons "split" is typed as list[str | Any]
+            formatted_components = cast(List[str], template.split("/"))
             components = [c for c in formatted_components if "{}".format(key.args[0]) not in c]
             template = "/".join(components)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_version.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_version.py
index e32dc6ec42180..e5754a47ce68f 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_version.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/_version.py
@@ -6,4 +6,4 @@
 # Changes may cause incorrect behavior and will be lost if the code is regenerated.
 # --------------------------------------------------------------------------
 
-VERSION = "2.0.0b1"
+VERSION = "1.0.0b1"
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/_configuration.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/_configuration.py
index 88eada53ff687..a17a6124d97c0 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/_configuration.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/_configuration.py
@@ -35,14 +35,14 @@ class MachineLearningServicesMgmtClientConfiguration(Configuration):  # pylint:
     :type credential: ~azure.core.credentials_async.AsyncTokenCredential
     :param subscription_id: The ID of the target subscription. Required.
     :type subscription_id: str
-    :keyword api_version: Api Version. Default value is "2022-10-01". Note that overriding this
-     default value may result in unsupported behavior.
+    :keyword api_version: Api Version. Default value is "2022-12-01-preview". Note that overriding
+     this default value may result in unsupported behavior.
     :paramtype api_version: str
     """
 
     def __init__(self, credential: "AsyncTokenCredential", subscription_id: str, **kwargs: Any) -> None:
         super(MachineLearningServicesMgmtClientConfiguration, self).__init__(**kwargs)
-        api_version: Literal["2022-10-01"] = kwargs.pop("api_version", "2022-10-01")
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop("api_version", "2022-12-01-preview")
 
         if credential is None:
             raise ValueError("Parameter 'credential' must not be None.")
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/_machine_learning_services_mgmt_client.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/_machine_learning_services_mgmt_client.py
index 4c1c840cfff50..699b6758acf31 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/_machine_learning_services_mgmt_client.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/_machine_learning_services_mgmt_client.py
@@ -29,6 +29,7 @@
     EnvironmentContainersOperations,
     EnvironmentVersionsOperations,
     JobsOperations,
+    LabelingJobsOperations,
     ModelContainersOperations,
     ModelVersionsOperations,
     OnlineDeploymentsOperations,
@@ -37,6 +38,15 @@
     PrivateEndpointConnectionsOperations,
     PrivateLinkResourcesOperations,
     QuotasOperations,
+    RegistriesOperations,
+    RegistryCodeContainersOperations,
+    RegistryCodeVersionsOperations,
+    RegistryComponentContainersOperations,
+    RegistryComponentVersionsOperations,
+    RegistryEnvironmentContainersOperations,
+    RegistryEnvironmentVersionsOperations,
+    RegistryModelContainersOperations,
+    RegistryModelVersionsOperations,
     SchedulesOperations,
     UsagesOperations,
     VirtualMachineSizesOperations,
@@ -75,6 +85,30 @@ class MachineLearningServicesMgmtClient:  # pylint: disable=client-accepts-api-v
     :ivar workspace_connections: WorkspaceConnectionsOperations operations
     :vartype workspace_connections:
      azure.mgmt.machinelearningservices.aio.operations.WorkspaceConnectionsOperations
+    :ivar registry_code_containers: RegistryCodeContainersOperations operations
+    :vartype registry_code_containers:
+     azure.mgmt.machinelearningservices.aio.operations.RegistryCodeContainersOperations
+    :ivar registry_code_versions: RegistryCodeVersionsOperations operations
+    :vartype registry_code_versions:
+     azure.mgmt.machinelearningservices.aio.operations.RegistryCodeVersionsOperations
+    :ivar registry_component_containers: RegistryComponentContainersOperations operations
+    :vartype registry_component_containers:
+     azure.mgmt.machinelearningservices.aio.operations.RegistryComponentContainersOperations
+    :ivar registry_component_versions: RegistryComponentVersionsOperations operations
+    :vartype registry_component_versions:
+     azure.mgmt.machinelearningservices.aio.operations.RegistryComponentVersionsOperations
+    :ivar registry_environment_containers: RegistryEnvironmentContainersOperations operations
+    :vartype registry_environment_containers:
+     azure.mgmt.machinelearningservices.aio.operations.RegistryEnvironmentContainersOperations
+    :ivar registry_environment_versions: RegistryEnvironmentVersionsOperations operations
+    :vartype registry_environment_versions:
+     azure.mgmt.machinelearningservices.aio.operations.RegistryEnvironmentVersionsOperations
+    :ivar registry_model_containers: RegistryModelContainersOperations operations
+    :vartype registry_model_containers:
+     azure.mgmt.machinelearningservices.aio.operations.RegistryModelContainersOperations
+    :ivar registry_model_versions: RegistryModelVersionsOperations operations
+    :vartype registry_model_versions:
+     azure.mgmt.machinelearningservices.aio.operations.RegistryModelVersionsOperations
     :ivar batch_endpoints: BatchEndpointsOperations operations
     :vartype batch_endpoints:
      azure.mgmt.machinelearningservices.aio.operations.BatchEndpointsOperations
@@ -109,6 +143,9 @@ class MachineLearningServicesMgmtClient:  # pylint: disable=client-accepts-api-v
      azure.mgmt.machinelearningservices.aio.operations.EnvironmentVersionsOperations
     :ivar jobs: JobsOperations operations
     :vartype jobs: azure.mgmt.machinelearningservices.aio.operations.JobsOperations
+    :ivar labeling_jobs: LabelingJobsOperations operations
+    :vartype labeling_jobs:
+     azure.mgmt.machinelearningservices.aio.operations.LabelingJobsOperations
     :ivar model_containers: ModelContainersOperations operations
     :vartype model_containers:
      azure.mgmt.machinelearningservices.aio.operations.ModelContainersOperations
@@ -123,6 +160,8 @@ class MachineLearningServicesMgmtClient:  # pylint: disable=client-accepts-api-v
      azure.mgmt.machinelearningservices.aio.operations.OnlineDeploymentsOperations
     :ivar schedules: SchedulesOperations operations
     :vartype schedules: azure.mgmt.machinelearningservices.aio.operations.SchedulesOperations
+    :ivar registries: RegistriesOperations operations
+    :vartype registries: azure.mgmt.machinelearningservices.aio.operations.RegistriesOperations
     :ivar workspace_features: WorkspaceFeaturesOperations operations
     :vartype workspace_features:
      azure.mgmt.machinelearningservices.aio.operations.WorkspaceFeaturesOperations
@@ -132,8 +171,8 @@ class MachineLearningServicesMgmtClient:  # pylint: disable=client-accepts-api-v
     :type subscription_id: str
     :param base_url: Service URL. Default value is "https://management.azure.com".
     :type base_url: str
-    :keyword api_version: Api Version. Default value is "2022-10-01". Note that overriding this
-     default value may result in unsupported behavior.
+    :keyword api_version: Api Version. Default value is "2022-12-01-preview". Note that overriding
+     this default value may result in unsupported behavior.
     :paramtype api_version: str
     :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
      Retry-After header is present.
@@ -172,6 +211,30 @@ def __init__(
         self.workspace_connections = WorkspaceConnectionsOperations(
             self._client, self._config, self._serialize, self._deserialize
         )
+        self.registry_code_containers = RegistryCodeContainersOperations(
+            self._client, self._config, self._serialize, self._deserialize
+        )
+        self.registry_code_versions = RegistryCodeVersionsOperations(
+            self._client, self._config, self._serialize, self._deserialize
+        )
+        self.registry_component_containers = RegistryComponentContainersOperations(
+            self._client, self._config, self._serialize, self._deserialize
+        )
+        self.registry_component_versions = RegistryComponentVersionsOperations(
+            self._client, self._config, self._serialize, self._deserialize
+        )
+        self.registry_environment_containers = RegistryEnvironmentContainersOperations(
+            self._client, self._config, self._serialize, self._deserialize
+        )
+        self.registry_environment_versions = RegistryEnvironmentVersionsOperations(
+            self._client, self._config, self._serialize, self._deserialize
+        )
+        self.registry_model_containers = RegistryModelContainersOperations(
+            self._client, self._config, self._serialize, self._deserialize
+        )
+        self.registry_model_versions = RegistryModelVersionsOperations(
+            self._client, self._config, self._serialize, self._deserialize
+        )
         self.batch_endpoints = BatchEndpointsOperations(self._client, self._config, self._serialize, self._deserialize)
         self.batch_deployments = BatchDeploymentsOperations(
             self._client, self._config, self._serialize, self._deserialize
@@ -194,6 +257,7 @@ def __init__(
             self._client, self._config, self._serialize, self._deserialize
         )
         self.jobs = JobsOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.labeling_jobs = LabelingJobsOperations(self._client, self._config, self._serialize, self._deserialize)
         self.model_containers = ModelContainersOperations(
             self._client, self._config, self._serialize, self._deserialize
         )
@@ -205,6 +269,7 @@ def __init__(
             self._client, self._config, self._serialize, self._deserialize
         )
         self.schedules = SchedulesOperations(self._client, self._config, self._serialize, self._deserialize)
+        self.registries = RegistriesOperations(self._client, self._config, self._serialize, self._deserialize)
         self.workspace_features = WorkspaceFeaturesOperations(
             self._client, self._config, self._serialize, self._deserialize
         )
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/__init__.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/__init__.py
index 0065485916d9d..60c27f074b69d 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/__init__.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/__init__.py
@@ -15,6 +15,14 @@
 from ._private_endpoint_connections_operations import PrivateEndpointConnectionsOperations
 from ._private_link_resources_operations import PrivateLinkResourcesOperations
 from ._workspace_connections_operations import WorkspaceConnectionsOperations
+from ._registry_code_containers_operations import RegistryCodeContainersOperations
+from ._registry_code_versions_operations import RegistryCodeVersionsOperations
+from ._registry_component_containers_operations import RegistryComponentContainersOperations
+from ._registry_component_versions_operations import RegistryComponentVersionsOperations
+from ._registry_environment_containers_operations import RegistryEnvironmentContainersOperations
+from ._registry_environment_versions_operations import RegistryEnvironmentVersionsOperations
+from ._registry_model_containers_operations import RegistryModelContainersOperations
+from ._registry_model_versions_operations import RegistryModelVersionsOperations
 from ._batch_endpoints_operations import BatchEndpointsOperations
 from ._batch_deployments_operations import BatchDeploymentsOperations
 from ._code_containers_operations import CodeContainersOperations
@@ -27,11 +35,13 @@
 from ._environment_containers_operations import EnvironmentContainersOperations
 from ._environment_versions_operations import EnvironmentVersionsOperations
 from ._jobs_operations import JobsOperations
+from ._labeling_jobs_operations import LabelingJobsOperations
 from ._model_containers_operations import ModelContainersOperations
 from ._model_versions_operations import ModelVersionsOperations
 from ._online_endpoints_operations import OnlineEndpointsOperations
 from ._online_deployments_operations import OnlineDeploymentsOperations
 from ._schedules_operations import SchedulesOperations
+from ._registries_operations import RegistriesOperations
 from ._workspace_features_operations import WorkspaceFeaturesOperations
 
 from ._patch import __all__ as _patch_all
@@ -48,6 +58,14 @@
     "PrivateEndpointConnectionsOperations",
     "PrivateLinkResourcesOperations",
     "WorkspaceConnectionsOperations",
+    "RegistryCodeContainersOperations",
+    "RegistryCodeVersionsOperations",
+    "RegistryComponentContainersOperations",
+    "RegistryComponentVersionsOperations",
+    "RegistryEnvironmentContainersOperations",
+    "RegistryEnvironmentVersionsOperations",
+    "RegistryModelContainersOperations",
+    "RegistryModelVersionsOperations",
     "BatchEndpointsOperations",
     "BatchDeploymentsOperations",
     "CodeContainersOperations",
@@ -60,11 +78,13 @@
     "EnvironmentContainersOperations",
     "EnvironmentVersionsOperations",
     "JobsOperations",
+    "LabelingJobsOperations",
     "ModelContainersOperations",
     "ModelVersionsOperations",
     "OnlineEndpointsOperations",
     "OnlineDeploymentsOperations",
     "SchedulesOperations",
+    "RegistriesOperations",
     "WorkspaceFeaturesOperations",
 ]
 __all__.extend([p for p in _patch_all if p not in __all__])
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_batch_deployments_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_batch_deployments_operations.py
index 37950b084904a..77824b3a87b35 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_batch_deployments_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_batch_deployments_operations.py
@@ -103,7 +103,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.BatchDeploymentTrackedResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -195,7 +195,7 @@ async def _delete_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -272,7 +272,7 @@ async def begin_delete(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -349,7 +349,7 @@ async def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.BatchDeployment] = kwargs.pop("cls", None)
@@ -410,7 +410,7 @@ async def _update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -612,7 +612,7 @@ async def begin_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -681,7 +681,7 @@ async def _create_or_update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -882,7 +882,7 @@ async def begin_create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_batch_endpoints_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_batch_endpoints_operations.py
index 848fe6dc7e278..db57e3efcdecf 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_batch_endpoints_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_batch_endpoints_operations.py
@@ -98,7 +98,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.BatchEndpointTrackedResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -188,7 +188,7 @@ async def _delete_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -262,7 +262,7 @@ async def begin_delete(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -336,7 +336,7 @@ async def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.BatchEndpoint] = kwargs.pop("cls", None)
@@ -395,7 +395,7 @@ async def _update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -586,7 +586,7 @@ async def begin_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -653,7 +653,7 @@ async def _create_or_update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -844,7 +844,7 @@ async def begin_create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -923,7 +923,7 @@ async def list_keys(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.EndpointAuthKeys] = kwargs.pop("cls", None)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_code_containers_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_code_containers_operations.py
index c69213fa842cb..a0605b1612743 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_code_containers_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_code_containers_operations.py
@@ -87,7 +87,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.CodeContainerResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -193,7 +193,7 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -260,7 +260,7 @@ async def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.CodeContainer] = kwargs.pop("cls", None)
@@ -408,7 +408,7 @@ async def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_code_versions_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_code_versions_operations.py
index 473dec5348b94..b515dcd890830 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_code_versions_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_code_versions_operations.py
@@ -100,7 +100,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.CodeVersionResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -211,7 +211,7 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -281,7 +281,7 @@ async def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.CodeVersion] = kwargs.pop("cls", None)
@@ -438,7 +438,7 @@ async def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_component_containers_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_component_containers_operations.py
index 59f121afb4b14..d6a8f6d396137 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_component_containers_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_component_containers_operations.py
@@ -95,7 +95,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ComponentContainerResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -202,7 +202,7 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -269,7 +269,7 @@ async def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ComponentContainer] = kwargs.pop("cls", None)
@@ -417,7 +417,7 @@ async def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_component_versions_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_component_versions_operations.py
index f217f6650def1..1c5dbedb83bc8 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_component_versions_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_component_versions_operations.py
@@ -104,7 +104,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ComponentVersionResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -216,7 +216,7 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -286,7 +286,7 @@ async def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ComponentVersion] = kwargs.pop("cls", None)
@@ -443,7 +443,7 @@ async def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_compute_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_compute_operations.py
index 3828288474813..bbc88f56a4bfa 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_compute_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_compute_operations.py
@@ -7,7 +7,7 @@
 # Changes may cause incorrect behavior and will be lost if the code is regenerated.
 # --------------------------------------------------------------------------
 import sys
-from typing import Any, AsyncIterable, Callable, Dict, IO, Optional, TypeVar, Union, cast, overload
+from typing import Any, AsyncIterable, Callable, Dict, IO, List, Optional, TypeVar, Union, cast, overload
 import urllib.parse
 
 from azure.core.async_paging import AsyncItemPaged, AsyncList
@@ -41,6 +41,8 @@
     build_restart_request,
     build_start_request,
     build_stop_request,
+    build_update_custom_services_request,
+    build_update_idle_shutdown_setting_request,
     build_update_request,
 )
 
@@ -93,7 +95,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.PaginatedComputeResourcesList] = kwargs.pop("cls", None)
@@ -198,7 +200,7 @@ async def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ComputeResource] = kwargs.pop("cls", None)
@@ -257,7 +259,7 @@ async def _create_or_update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -445,7 +447,7 @@ async def begin_create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -512,7 +514,7 @@ async def _update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -688,7 +690,7 @@ async def begin_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -755,7 +757,7 @@ async def _delete_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -837,7 +839,7 @@ async def begin_delete(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -881,6 +883,156 @@ def get_long_running_output(pipeline_response):  # pylint: disable=inconsistent-
         "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}"
     }
 
+    @overload
+    async def update_custom_services(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        compute_name: str,
+        custom_services: List[_models.CustomService],
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> None:
+        """Updates the custom services list. The list of custom services provided shall be overwritten.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace. Required.
+        :type workspace_name: str
+        :param compute_name: Name of the Azure Machine Learning compute. Required.
+        :type compute_name: str
+        :param custom_services: New list of Custom Services. Required.
+        :type custom_services: list[~azure.mgmt.machinelearningservices.models.CustomService]
+        :keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None or the result of cls(response)
+        :rtype: None
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @overload
+    async def update_custom_services(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        compute_name: str,
+        custom_services: IO,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> None:
+        """Updates the custom services list. The list of custom services provided shall be overwritten.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace. Required.
+        :type workspace_name: str
+        :param compute_name: Name of the Azure Machine Learning compute. Required.
+        :type compute_name: str
+        :param custom_services: New list of Custom Services. Required.
+        :type custom_services: IO
+        :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None or the result of cls(response)
+        :rtype: None
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @distributed_trace_async
+    async def update_custom_services(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        compute_name: str,
+        custom_services: Union[List[_models.CustomService], IO],
+        **kwargs: Any
+    ) -> None:
+        """Updates the custom services list. The list of custom services provided shall be overwritten.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace. Required.
+        :type workspace_name: str
+        :param compute_name: Name of the Azure Machine Learning compute. Required.
+        :type compute_name: str
+        :param custom_services: New list of Custom Services. Is either a list type or a IO type.
+         Required.
+        :type custom_services: list[~azure.mgmt.machinelearningservices.models.CustomService] or IO
+        :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
+         Default value is None.
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None or the result of cls(response)
+        :rtype: None
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[None] = kwargs.pop("cls", None)
+
+        content_type = content_type or "application/json"
+        _json = None
+        _content = None
+        if isinstance(custom_services, (IO, bytes)):
+            _content = custom_services
+        else:
+            _json = self._serialize.body(custom_services, "[CustomService]")
+
+        request = build_update_custom_services_request(
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            compute_name=compute_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            content=_content,
+            template_url=self.update_custom_services.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    update_custom_services.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/customServices"
+    }
+
     @distributed_trace
     def list_nodes(
         self, resource_group_name: str, workspace_name: str, compute_name: str, **kwargs: Any
@@ -904,7 +1056,7 @@ def list_nodes(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.AmlComputeNodesInformation] = kwargs.pop("cls", None)
@@ -1008,7 +1160,7 @@ async def list_keys(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ComputeSecrets] = kwargs.pop("cls", None)
@@ -1062,7 +1214,7 @@ async def _start_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -1126,7 +1278,7 @@ async def begin_start(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -1183,7 +1335,7 @@ async def _stop_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -1247,7 +1399,7 @@ async def begin_stop(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -1304,7 +1456,7 @@ async def _restart_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -1368,7 +1520,7 @@ async def begin_restart(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -1410,3 +1562,155 @@ def get_long_running_output(pipeline_response):  # pylint: disable=inconsistent-
     begin_restart.metadata = {
         "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/restart"
     }
+
+    @overload
+    async def update_idle_shutdown_setting(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        compute_name: str,
+        parameters: _models.IdleShutdownSetting,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> None:
+        """Updates the idle shutdown setting of a compute instance.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace. Required.
+        :type workspace_name: str
+        :param compute_name: Name of the Azure Machine Learning compute. Required.
+        :type compute_name: str
+        :param parameters: The object for updating idle shutdown setting of specified ComputeInstance.
+         Required.
+        :type parameters: ~azure.mgmt.machinelearningservices.models.IdleShutdownSetting
+        :keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None or the result of cls(response)
+        :rtype: None
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @overload
+    async def update_idle_shutdown_setting(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        compute_name: str,
+        parameters: IO,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> None:
+        """Updates the idle shutdown setting of a compute instance.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace. Required.
+        :type workspace_name: str
+        :param compute_name: Name of the Azure Machine Learning compute. Required.
+        :type compute_name: str
+        :param parameters: The object for updating idle shutdown setting of specified ComputeInstance.
+         Required.
+        :type parameters: IO
+        :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None or the result of cls(response)
+        :rtype: None
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @distributed_trace_async
+    async def update_idle_shutdown_setting(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        compute_name: str,
+        parameters: Union[_models.IdleShutdownSetting, IO],
+        **kwargs: Any
+    ) -> None:
+        """Updates the idle shutdown setting of a compute instance.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace. Required.
+        :type workspace_name: str
+        :param compute_name: Name of the Azure Machine Learning compute. Required.
+        :type compute_name: str
+        :param parameters: The object for updating idle shutdown setting of specified ComputeInstance.
+         Is either a model type or a IO type. Required.
+        :type parameters: ~azure.mgmt.machinelearningservices.models.IdleShutdownSetting or IO
+        :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
+         Default value is None.
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None or the result of cls(response)
+        :rtype: None
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[None] = kwargs.pop("cls", None)
+
+        content_type = content_type or "application/json"
+        _json = None
+        _content = None
+        if isinstance(parameters, (IO, bytes)):
+            _content = parameters
+        else:
+            _json = self._serialize.body(parameters, "IdleShutdownSetting")
+
+        request = build_update_idle_shutdown_setting_request(
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            compute_name=compute_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            content=_content,
+            template_url=self.update_idle_shutdown_setting.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    update_idle_shutdown_setting.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/updateIdleShutdownSetting"
+    }
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_data_containers_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_data_containers_operations.py
index 4c5aca387b8f9..ed1552dacf4e7 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_data_containers_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_data_containers_operations.py
@@ -95,7 +95,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.DataContainerResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -202,7 +202,7 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -269,7 +269,7 @@ async def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.DataContainer] = kwargs.pop("cls", None)
@@ -417,7 +417,7 @@ async def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_data_versions_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_data_versions_operations.py
index a9f90dd943ed3..5d61eaf1c052b 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_data_versions_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_data_versions_operations.py
@@ -112,7 +112,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.DataVersionBaseResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -225,7 +225,7 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -295,7 +295,7 @@ async def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.DataVersionBase] = kwargs.pop("cls", None)
@@ -452,7 +452,7 @@ async def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_datastores_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_datastores_operations.py
index 293c073184e6a..f054f830d8006 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_datastores_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_datastores_operations.py
@@ -110,7 +110,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.DatastoreResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -222,7 +222,7 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -287,7 +287,7 @@ async def get(self, resource_group_name: str, workspace_name: str, name: str, **
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.Datastore] = kwargs.pop("cls", None)
@@ -444,7 +444,7 @@ async def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -532,7 +532,7 @@ async def list_secrets(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.DatastoreSecrets] = kwargs.pop("cls", None)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_environment_containers_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_environment_containers_operations.py
index 0115c7250a438..ac6492f9f073e 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_environment_containers_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_environment_containers_operations.py
@@ -96,7 +96,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.EnvironmentContainerResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -203,7 +203,7 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -270,7 +270,7 @@ async def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.EnvironmentContainer] = kwargs.pop("cls", None)
@@ -418,7 +418,7 @@ async def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_environment_versions_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_environment_versions_operations.py
index 8b4432552f559..c455fd3e04e8a 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_environment_versions_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_environment_versions_operations.py
@@ -104,7 +104,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.EnvironmentVersionResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -216,7 +216,7 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -286,7 +286,7 @@ async def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.EnvironmentVersion] = kwargs.pop("cls", None)
@@ -443,7 +443,7 @@ async def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_jobs_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_jobs_operations.py
index e8e3110d7004e..7a39518a8d327 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_jobs_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_jobs_operations.py
@@ -75,6 +75,8 @@ def list(
         job_type: Optional[str] = None,
         tag: Optional[str] = None,
         list_view_type: Optional[Union[str, _models.ListViewType]] = None,
+        scheduled: Optional[bool] = None,
+        schedule_id: Optional[str] = None,
         **kwargs: Any
     ) -> AsyncIterable["_models.JobBase"]:
         """Lists Jobs in the workspace.
@@ -95,6 +97,10 @@ def list(
         :param list_view_type: View type for including/excluding (for example) archived entities. Known
          values are: "ActiveOnly", "ArchivedOnly", and "All". Default value is None.
         :type list_view_type: str or ~azure.mgmt.machinelearningservices.models.ListViewType
+        :param scheduled: Indicator whether the job is scheduled job. Default value is None.
+        :type scheduled: bool
+        :param schedule_id: The scheduled id for listing the job triggered from. Default value is None.
+        :type schedule_id: str
         :keyword callable cls: A custom type or function that will be passed the direct response
         :return: An iterator like instance of either JobBase or the result of cls(response)
         :rtype:
@@ -104,7 +110,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.JobBaseResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -128,6 +134,8 @@ def prepare_request(next_link=None):
                     job_type=job_type,
                     tag=tag,
                     list_view_type=list_view_type,
+                    scheduled=scheduled,
+                    schedule_id=schedule_id,
                     api_version=api_version,
                     template_url=self.list.metadata["url"],
                     headers=_headers,
@@ -196,7 +204,7 @@ async def _delete_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -270,7 +278,7 @@ async def begin_delete(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -342,7 +350,7 @@ async def get(self, resource_group_name: str, workspace_name: str, id: str, **kw
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.JobBase] = kwargs.pop("cls", None)
@@ -484,7 +492,7 @@ async def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -554,7 +562,7 @@ async def _cancel_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -625,7 +633,7 @@ async def begin_cancel(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_labeling_jobs_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_labeling_jobs_operations.py
new file mode 100644
index 0000000000000..693c18a7547d5
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_labeling_jobs_operations.py
@@ -0,0 +1,1036 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+import sys
+from typing import Any, AsyncIterable, Callable, Dict, IO, Optional, TypeVar, Union, cast, overload
+import urllib.parse
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import (
+    ClientAuthenticationError,
+    HttpResponseError,
+    ResourceExistsError,
+    ResourceNotFoundError,
+    ResourceNotModifiedError,
+    map_error,
+)
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.core.utils import case_insensitive_dict
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.async_arm_polling import AsyncARMPolling
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._labeling_jobs_operations import (
+    build_create_or_update_request,
+    build_delete_request,
+    build_export_labels_request,
+    build_get_request,
+    build_list_request,
+    build_pause_request,
+    build_resume_request,
+)
+
+if sys.version_info >= (3, 8):
+    from typing import Literal  # pylint: disable=no-name-in-module, ungrouped-imports
+else:
+    from typing_extensions import Literal  # type: ignore  # pylint: disable=ungrouped-imports
+T = TypeVar("T")
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+
+class LabelingJobsOperations:
+    """
+    .. warning::
+        **DO NOT** instantiate this class directly.
+
+        Instead, you should access the following operations through
+        :class:`~azure.mgmt.machinelearningservices.aio.MachineLearningServicesMgmtClient`'s
+        :attr:`labeling_jobs` attribute.
+    """
+
+    models = _models
+
+    def __init__(self, *args, **kwargs) -> None:
+        input_args = list(args)
+        self._client = input_args.pop(0) if input_args else kwargs.pop("client")
+        self._config = input_args.pop(0) if input_args else kwargs.pop("config")
+        self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer")
+        self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer")
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        skip: Optional[str] = None,
+        top: Optional[int] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.LabelingJob"]:
+        """Lists labeling jobs in the workspace.
+
+        Lists labeling jobs in the workspace.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace. Required.
+        :type workspace_name: str
+        :param skip: Continuation token for pagination. Default value is None.
+        :type skip: str
+        :param top: Number of labeling jobs to return. Default value is None.
+        :type top: int
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either LabelingJob or the result of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.LabelingJob]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.LabelingJobResourceArmPaginatedResult] = kwargs.pop("cls", None)
+
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        def prepare_request(next_link=None):
+            if not next_link:
+
+                request = build_list_request(
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    subscription_id=self._config.subscription_id,
+                    skip=skip,
+                    top=top,
+                    api_version=api_version,
+                    template_url=self.list.metadata["url"],
+                    headers=_headers,
+                    params=_params,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                # make call to next link with the client's api-version
+                _parsed_next_link = urllib.parse.urlparse(next_link)
+                _next_request_params = case_insensitive_dict(
+                    {
+                        key: [urllib.parse.quote(v) for v in value]
+                        for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items()
+                    }
+                )
+                _next_request_params["api-version"] = self._config.api_version
+                request = HttpRequest(
+                    "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("LabelingJobResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)  # type: ignore
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request, stream=False, **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+        return AsyncItemPaged(get_next, extract_data)
+
+    list.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/labelingJobs"
+    }
+
+    @distributed_trace_async
+    async def delete(  # pylint: disable=inconsistent-return-statements
+        self, resource_group_name: str, workspace_name: str, id: str, **kwargs: Any
+    ) -> None:
+        """Delete a labeling job.
+
+        Delete a labeling job.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace. Required.
+        :type workspace_name: str
+        :param id: The name and identifier for the LabelingJob. Required.
+        :type id: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None or the result of cls(response)
+        :rtype: None
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+
+        request = build_delete_request(
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            id=id,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self.delete.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    delete.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/labelingJobs/{id}"
+    }
+
+    @distributed_trace_async
+    async def get(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        id: str,
+        include_job_instructions: bool = False,
+        include_label_categories: bool = False,
+        **kwargs: Any
+    ) -> _models.LabelingJob:
+        """Gets a labeling job by name/id.
+
+        Gets a labeling job by name/id.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace. Required.
+        :type workspace_name: str
+        :param id: The name and identifier for the LabelingJob. Required.
+        :type id: str
+        :param include_job_instructions: Boolean value to indicate whether to include JobInstructions
+         in response. Default value is False.
+        :type include_job_instructions: bool
+        :param include_label_categories: Boolean value to indicate Whether to include LabelCategories
+         in response. Default value is False.
+        :type include_label_categories: bool
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: LabelingJob or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.LabelingJob
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.LabelingJob] = kwargs.pop("cls", None)
+
+        request = build_get_request(
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            id=id,
+            subscription_id=self._config.subscription_id,
+            include_job_instructions=include_job_instructions,
+            include_label_categories=include_label_categories,
+            api_version=api_version,
+            template_url=self.get.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize("LabelingJob", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/labelingJobs/{id}"
+    }
+
+    async def _create_or_update_initial(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        id: str,
+        body: Union[_models.LabelingJob, IO],
+        **kwargs: Any
+    ) -> _models.LabelingJob:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.LabelingJob] = kwargs.pop("cls", None)
+
+        content_type = content_type or "application/json"
+        _json = None
+        _content = None
+        if isinstance(body, (IO, bytes)):
+            _content = body
+        else:
+            _json = self._serialize.body(body, "LabelingJob")
+
+        request = build_create_or_update_request(
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            id=id,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            content=_content,
+            template_url=self._create_or_update_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize("LabelingJob", pipeline_response)
+
+        if response.status_code == 201:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Azure-AsyncOperation"] = self._deserialize(
+                "str", response.headers.get("Azure-AsyncOperation")
+            )
+
+            deserialized = self._deserialize("LabelingJob", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)  # type: ignore
+
+        return deserialized  # type: ignore
+
+    _create_or_update_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/labelingJobs/{id}"
+    }
+
+    @overload
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        id: str,
+        body: _models.LabelingJob,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.LabelingJob]:
+        """Creates or updates a labeling job (asynchronous).
+
+        Creates or updates a labeling job (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace. Required.
+        :type workspace_name: str
+        :param id: The name and identifier for the LabelingJob. Required.
+        :type id: str
+        :param body: LabelingJob definition object. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.LabelingJob
+        :keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either LabelingJob or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.LabelingJob]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @overload
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        id: str,
+        body: IO,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.LabelingJob]:
+        """Creates or updates a labeling job (asynchronous).
+
+        Creates or updates a labeling job (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace. Required.
+        :type workspace_name: str
+        :param id: The name and identifier for the LabelingJob. Required.
+        :type id: str
+        :param body: LabelingJob definition object. Required.
+        :type body: IO
+        :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either LabelingJob or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.LabelingJob]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @distributed_trace_async
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        id: str,
+        body: Union[_models.LabelingJob, IO],
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.LabelingJob]:
+        """Creates or updates a labeling job (asynchronous).
+
+        Creates or updates a labeling job (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace. Required.
+        :type workspace_name: str
+        :param id: The name and identifier for the LabelingJob. Required.
+        :type id: str
+        :param body: LabelingJob definition object. Is either a model type or a IO type. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.LabelingJob or IO
+        :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
+         Default value is None.
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either LabelingJob or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.LabelingJob]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.LabelingJob] = kwargs.pop("cls", None)
+        polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = await self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                id=id,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):
+            deserialized = self._deserialize("LabelingJob", pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+        if polling is True:
+            polling_method: AsyncPollingMethod = cast(AsyncPollingMethod, AsyncARMPolling(lro_delay, **kwargs))
+        elif polling is False:
+            polling_method = cast(AsyncPollingMethod, AsyncNoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_create_or_update.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/labelingJobs/{id}"
+    }
+
+    async def _export_labels_initial(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        id: str,
+        body: Union[_models.ExportSummary, IO],
+        **kwargs: Any
+    ) -> Optional[_models.ExportSummary]:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[Optional[_models.ExportSummary]] = kwargs.pop("cls", None)
+
+        content_type = content_type or "application/json"
+        _json = None
+        _content = None
+        if isinstance(body, (IO, bytes)):
+            _content = body
+        else:
+            _json = self._serialize.body(body, "ExportSummary")
+
+        request = build_export_labels_request(
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            id=id,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            content=_content,
+            template_url=self._export_labels_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = None
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize("ExportSummary", pipeline_response)
+
+        if response.status_code == 202:
+            response_headers["Location"] = self._deserialize("str", response.headers.get("Location"))
+            response_headers["Retry-After"] = self._deserialize("int", response.headers.get("Retry-After"))
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)
+
+        return deserialized
+
+    _export_labels_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/labelingJobs/{id}/exportLabels"
+    }
+
+    @overload
+    async def begin_export_labels(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        id: str,
+        body: _models.ExportSummary,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.ExportSummary]:
+        """Export labels from a labeling job (asynchronous).
+
+        Export labels from a labeling job (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace. Required.
+        :type workspace_name: str
+        :param id: The name and identifier for the LabelingJob. Required.
+        :type id: str
+        :param body: The export summary. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.ExportSummary
+        :keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either ExportSummary or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.ExportSummary]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @overload
+    async def begin_export_labels(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        id: str,
+        body: IO,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.ExportSummary]:
+        """Export labels from a labeling job (asynchronous).
+
+        Export labels from a labeling job (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace. Required.
+        :type workspace_name: str
+        :param id: The name and identifier for the LabelingJob. Required.
+        :type id: str
+        :param body: The export summary. Required.
+        :type body: IO
+        :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either ExportSummary or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.ExportSummary]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @distributed_trace_async
+    async def begin_export_labels(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        id: str,
+        body: Union[_models.ExportSummary, IO],
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.ExportSummary]:
+        """Export labels from a labeling job (asynchronous).
+
+        Export labels from a labeling job (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace. Required.
+        :type workspace_name: str
+        :param id: The name and identifier for the LabelingJob. Required.
+        :type id: str
+        :param body: The export summary. Is either a model type or a IO type. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.ExportSummary or IO
+        :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
+         Default value is None.
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either ExportSummary or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.ExportSummary]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.ExportSummary] = kwargs.pop("cls", None)
+        polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = await self._export_labels_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                id=id,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):
+            deserialized = self._deserialize("ExportSummary", pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+        if polling is True:
+            polling_method: AsyncPollingMethod = cast(
+                AsyncPollingMethod, AsyncARMPolling(lro_delay, lro_options={"final-state-via": "location"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(AsyncPollingMethod, AsyncNoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_export_labels.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/labelingJobs/{id}/exportLabels"
+    }
+
+    @distributed_trace_async
+    async def pause(  # pylint: disable=inconsistent-return-statements
+        self, resource_group_name: str, workspace_name: str, id: str, **kwargs: Any
+    ) -> None:
+        """Pause a labeling job.
+
+        Pause a labeling job.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace. Required.
+        :type workspace_name: str
+        :param id: The name and identifier for the LabelingJob. Required.
+        :type id: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None or the result of cls(response)
+        :rtype: None
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+
+        request = build_pause_request(
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            id=id,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self.pause.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    pause.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/labelingJobs/{id}/pause"
+    }
+
+    async def _resume_initial(  # pylint: disable=inconsistent-return-statements
+        self, resource_group_name: str, workspace_name: str, id: str, **kwargs: Any
+    ) -> None:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+
+        request = build_resume_request(
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            id=id,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self._resume_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers["Location"] = self._deserialize("str", response.headers.get("Location"))
+            response_headers["Retry-After"] = self._deserialize("int", response.headers.get("Retry-After"))
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _resume_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/labelingJobs/{id}/resume"
+    }
+
+    @distributed_trace_async
+    async def begin_resume(
+        self, resource_group_name: str, workspace_name: str, id: str, **kwargs: Any
+    ) -> AsyncLROPoller[None]:
+        """Resume a labeling job (asynchronous).
+
+        Resume a labeling job (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace. Required.
+        :type workspace_name: str
+        :param id: The name and identifier for the LabelingJob. Required.
+        :type id: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[None]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+        polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = await self._resume_initial(  # type: ignore
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                id=id,
+                api_version=api_version,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):  # pylint: disable=inconsistent-return-statements
+            if cls:
+                return cls(pipeline_response, None, {})
+
+        if polling is True:
+            polling_method: AsyncPollingMethod = cast(
+                AsyncPollingMethod, AsyncARMPolling(lro_delay, lro_options={"final-state-via": "location"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(AsyncPollingMethod, AsyncNoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_resume.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/labelingJobs/{id}/resume"
+    }
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_model_containers_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_model_containers_operations.py
index 0f18875a197a5..8f188ce138c6a 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_model_containers_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_model_containers_operations.py
@@ -98,7 +98,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ModelContainerResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -206,7 +206,7 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -273,7 +273,7 @@ async def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ModelContainer] = kwargs.pop("cls", None)
@@ -421,7 +421,7 @@ async def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_model_versions_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_model_versions_operations.py
index 68a4fd3be570d..f46465aba2b06 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_model_versions_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_model_versions_operations.py
@@ -124,7 +124,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ModelVersionResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -242,7 +242,7 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -312,7 +312,7 @@ async def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ModelVersion] = kwargs.pop("cls", None)
@@ -469,7 +469,7 @@ async def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_online_deployments_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_online_deployments_operations.py
index f78323104bda3..9c2280c305385 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_online_deployments_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_online_deployments_operations.py
@@ -105,7 +105,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.OnlineDeploymentTrackedResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -197,7 +197,7 @@ async def _delete_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -274,7 +274,7 @@ async def begin_delete(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -351,7 +351,7 @@ async def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.OnlineDeployment] = kwargs.pop("cls", None)
@@ -412,7 +412,7 @@ async def _update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -612,7 +612,7 @@ async def begin_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -681,7 +681,7 @@ async def _create_or_update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -882,7 +882,7 @@ async def begin_create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1048,7 +1048,7 @@ async def get_logs(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1138,7 +1138,7 @@ def list_skus(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.SkuResourceArmPaginatedResult] = kwargs.pop("cls", None)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_online_endpoints_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_online_endpoints_operations.py
index bb9f50e9e7b85..e2459a8b2bafd 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_online_endpoints_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_online_endpoints_operations.py
@@ -121,7 +121,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.OnlineEndpointTrackedResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -216,7 +216,7 @@ async def _delete_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -290,7 +290,7 @@ async def begin_delete(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -364,7 +364,7 @@ async def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.OnlineEndpoint] = kwargs.pop("cls", None)
@@ -423,7 +423,7 @@ async def _update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -614,7 +614,7 @@ async def begin_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -681,7 +681,7 @@ async def _create_or_update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -872,7 +872,7 @@ async def begin_create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -951,7 +951,7 @@ async def list_keys(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.EndpointAuthKeys] = kwargs.pop("cls", None)
@@ -1010,7 +1010,7 @@ async def _regenerate_keys_initial(  # pylint: disable=inconsistent-return-state
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1183,7 +1183,7 @@ async def begin_regenerate_keys(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1262,7 +1262,7 @@ async def get_token(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.EndpointAuthToken] = kwargs.pop("cls", None)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_operations.py
index ab8aec812cd07..d689fbd6f8465 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_operations.py
@@ -59,7 +59,7 @@ def __init__(self, *args, **kwargs) -> None:
 
     @distributed_trace
     def list(self, **kwargs: Any) -> AsyncIterable["_models.AmlOperation"]:
-        """Lists all of the available Azure Machine Learning Workspaces REST API operations.
+        """Lists all of the available Azure Machine Learning Services REST API operations.
 
         :keyword callable cls: A custom type or function that will be passed the direct response
         :return: An iterator like instance of either AmlOperation or the result of cls(response)
@@ -70,7 +70,7 @@ def list(self, **kwargs: Any) -> AsyncIterable["_models.AmlOperation"]:
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.AmlOperationListResult] = kwargs.pop("cls", None)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_private_endpoint_connections_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_private_endpoint_connections_operations.py
index ca1d84b13c3e7..11cede4285375 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_private_endpoint_connections_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_private_endpoint_connections_operations.py
@@ -84,7 +84,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.PrivateEndpointConnectionListResult] = kwargs.pop("cls", None)
@@ -188,7 +188,7 @@ async def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.PrivateEndpointConnection] = kwargs.pop("cls", None)
@@ -333,7 +333,7 @@ async def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -415,7 +415,7 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_private_link_resources_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_private_link_resources_operations.py
index 8c30d300602a6..08586b6c399d1 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_private_link_resources_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_private_link_resources_operations.py
@@ -82,7 +82,7 @@ async def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.PrivateLinkResourceListResult] = kwargs.pop("cls", None)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_quotas_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_quotas_operations.py
index b101e920473fb..3b6e62435a403 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_quotas_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_quotas_operations.py
@@ -130,7 +130,7 @@ async def update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -195,7 +195,7 @@ def list(self, location: str, **kwargs: Any) -> AsyncIterable["_models.ResourceQ
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ListWorkspaceQuotas] = kwargs.pop("cls", None)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_registries_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_registries_operations.py
new file mode 100644
index 0000000000000..564070ab6c689
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_registries_operations.py
@@ -0,0 +1,920 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+import sys
+from typing import Any, AsyncIterable, Callable, Dict, IO, Optional, TypeVar, Union, cast, overload
+import urllib.parse
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import (
+    ClientAuthenticationError,
+    HttpResponseError,
+    ResourceExistsError,
+    ResourceNotFoundError,
+    ResourceNotModifiedError,
+    map_error,
+)
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.core.utils import case_insensitive_dict
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.async_arm_polling import AsyncARMPolling
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._registries_operations import (
+    build_create_or_update_request,
+    build_delete_request,
+    build_get_request,
+    build_list_by_subscription_request,
+    build_list_request,
+    build_update_request,
+)
+
+if sys.version_info >= (3, 8):
+    from typing import Literal  # pylint: disable=no-name-in-module, ungrouped-imports
+else:
+    from typing_extensions import Literal  # type: ignore  # pylint: disable=ungrouped-imports
+T = TypeVar("T")
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+
+class RegistriesOperations:
+    """
+    .. warning::
+        **DO NOT** instantiate this class directly.
+
+        Instead, you should access the following operations through
+        :class:`~azure.mgmt.machinelearningservices.aio.MachineLearningServicesMgmtClient`'s
+        :attr:`registries` attribute.
+    """
+
+    models = _models
+
+    def __init__(self, *args, **kwargs) -> None:
+        input_args = list(args)
+        self._client = input_args.pop(0) if input_args else kwargs.pop("client")
+        self._config = input_args.pop(0) if input_args else kwargs.pop("config")
+        self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer")
+        self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer")
+
+    @distributed_trace
+    def list_by_subscription(self, skip: Optional[str] = None, **kwargs: Any) -> AsyncIterable["_models.Registry"]:
+        """List registries by subscription.
+
+        List registries by subscription.
+
+        :param skip: Continuation token for pagination. Default value is None.
+        :type skip: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either Registry or the result of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.Registry]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.RegistryTrackedResourceArmPaginatedResult] = kwargs.pop("cls", None)
+
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        def prepare_request(next_link=None):
+            if not next_link:
+
+                request = build_list_by_subscription_request(
+                    subscription_id=self._config.subscription_id,
+                    skip=skip,
+                    api_version=api_version,
+                    template_url=self.list_by_subscription.metadata["url"],
+                    headers=_headers,
+                    params=_params,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                # make call to next link with the client's api-version
+                _parsed_next_link = urllib.parse.urlparse(next_link)
+                _next_request_params = case_insensitive_dict(
+                    {
+                        key: [urllib.parse.quote(v) for v in value]
+                        for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items()
+                    }
+                )
+                _next_request_params["api-version"] = self._config.api_version
+                request = HttpRequest(
+                    "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("RegistryTrackedResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)  # type: ignore
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request, stream=False, **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+        return AsyncItemPaged(get_next, extract_data)
+
+    list_by_subscription.metadata = {
+        "url": "/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearningServices/registries"
+    }
+
+    @distributed_trace
+    def list(
+        self, resource_group_name: str, skip: Optional[str] = None, **kwargs: Any
+    ) -> AsyncIterable["_models.Registry"]:
+        """List registries.
+
+        List registries.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param skip: Continuation token for pagination. Default value is None.
+        :type skip: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either Registry or the result of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.Registry]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.RegistryTrackedResourceArmPaginatedResult] = kwargs.pop("cls", None)
+
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        def prepare_request(next_link=None):
+            if not next_link:
+
+                request = build_list_request(
+                    resource_group_name=resource_group_name,
+                    subscription_id=self._config.subscription_id,
+                    skip=skip,
+                    api_version=api_version,
+                    template_url=self.list.metadata["url"],
+                    headers=_headers,
+                    params=_params,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                # make call to next link with the client's api-version
+                _parsed_next_link = urllib.parse.urlparse(next_link)
+                _next_request_params = case_insensitive_dict(
+                    {
+                        key: [urllib.parse.quote(v) for v in value]
+                        for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items()
+                    }
+                )
+                _next_request_params["api-version"] = self._config.api_version
+                request = HttpRequest(
+                    "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("RegistryTrackedResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)  # type: ignore
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request, stream=False, **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+        return AsyncItemPaged(get_next, extract_data)
+
+    list.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries"
+    }
+
+    async def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self, resource_group_name: str, registry_name: str, **kwargs: Any
+    ) -> None:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+
+        request = build_delete_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self._delete_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Location"] = self._deserialize("str", response.headers.get("Location"))
+            response_headers["Retry-After"] = self._deserialize("int", response.headers.get("Retry-After"))
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _delete_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}"
+    }
+
+    @distributed_trace_async
+    async def begin_delete(self, resource_group_name: str, registry_name: str, **kwargs: Any) -> AsyncLROPoller[None]:
+        """Delete registry.
+
+        Delete registry.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of registry. This is case-insensitive. Required.
+        :type registry_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[None]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+        polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = await self._delete_initial(  # type: ignore
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                api_version=api_version,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):  # pylint: disable=inconsistent-return-statements
+            if cls:
+                return cls(pipeline_response, None, {})
+
+        if polling is True:
+            polling_method: AsyncPollingMethod = cast(
+                AsyncPollingMethod, AsyncARMPolling(lro_delay, lro_options={"final-state-via": "location"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(AsyncPollingMethod, AsyncNoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_delete.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}"
+    }
+
+    @distributed_trace_async
+    async def get(self, resource_group_name: str, registry_name: str, **kwargs: Any) -> _models.Registry:
+        """Get registry.
+
+        Get registry.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of registry. This is case-insensitive. Required.
+        :type registry_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: Registry or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.Registry
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.Registry] = kwargs.pop("cls", None)
+
+        request = build_get_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self.get.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize("Registry", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}"
+    }
+
+    async def _update_initial(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        body: Union[_models.PartialRegistryPartialTrackedResource, IO],
+        **kwargs: Any
+    ) -> _models.Registry:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.Registry] = kwargs.pop("cls", None)
+
+        content_type = content_type or "application/json"
+        _json = None
+        _content = None
+        if isinstance(body, (IO, bytes)):
+            _content = body
+        else:
+            _json = self._serialize.body(body, "PartialRegistryPartialTrackedResource")
+
+        request = build_update_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            content=_content,
+            template_url=self._update_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize("Registry", pipeline_response)
+
+        if response.status_code == 202:
+            response_headers["Location"] = self._deserialize("str", response.headers.get("Location"))
+            response_headers["Retry-After"] = self._deserialize("int", response.headers.get("Retry-After"))
+
+            deserialized = self._deserialize("Registry", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)  # type: ignore
+
+        return deserialized  # type: ignore
+
+    _update_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}"
+    }
+
+    @overload
+    async def begin_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        body: _models.PartialRegistryPartialTrackedResource,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.Registry]:
+        """Update tags.
+
+        Update tags.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of registry. This is case-insensitive. Required.
+        :type registry_name: str
+        :param body: Details required to create the registry. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.PartialRegistryPartialTrackedResource
+        :keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either Registry or the result of
+         cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.Registry]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @overload
+    async def begin_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        body: IO,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.Registry]:
+        """Update tags.
+
+        Update tags.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of registry. This is case-insensitive. Required.
+        :type registry_name: str
+        :param body: Details required to create the registry. Required.
+        :type body: IO
+        :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either Registry or the result of
+         cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.Registry]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @distributed_trace_async
+    async def begin_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        body: Union[_models.PartialRegistryPartialTrackedResource, IO],
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.Registry]:
+        """Update tags.
+
+        Update tags.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of registry. This is case-insensitive. Required.
+        :type registry_name: str
+        :param body: Details required to create the registry. Is either a model type or a IO type.
+         Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.PartialRegistryPartialTrackedResource or
+         IO
+        :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
+         Default value is None.
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either Registry or the result of
+         cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.Registry]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.Registry] = kwargs.pop("cls", None)
+        polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = await self._update_initial(
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):
+            deserialized = self._deserialize("Registry", pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+        if polling is True:
+            polling_method: AsyncPollingMethod = cast(
+                AsyncPollingMethod, AsyncARMPolling(lro_delay, lro_options={"final-state-via": "location"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(AsyncPollingMethod, AsyncNoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_update.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}"
+    }
+
+    async def _create_or_update_initial(
+        self, resource_group_name: str, registry_name: str, body: Union[_models.Registry, IO], **kwargs: Any
+    ) -> Optional[_models.Registry]:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[Optional[_models.Registry]] = kwargs.pop("cls", None)
+
+        content_type = content_type or "application/json"
+        _json = None
+        _content = None
+        if isinstance(body, (IO, bytes)):
+            _content = body
+        else:
+            _json = self._serialize.body(body, "Registry")
+
+        request = build_create_or_update_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            content=_content,
+            template_url=self._create_or_update_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201, 202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = None
+        if response.status_code == 200:
+            deserialized = self._deserialize("Registry", pipeline_response)
+
+        if response.status_code == 201:
+            deserialized = self._deserialize("Registry", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    _create_or_update_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}"
+    }
+
+    @overload
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        body: _models.Registry,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.Registry]:
+        """Create or update registry.
+
+        Create or update registry.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of registry. This is case-insensitive. Required.
+        :type registry_name: str
+        :param body: Details required to create the registry. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.Registry
+        :keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either Registry or the result of
+         cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.Registry]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @overload
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        body: IO,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.Registry]:
+        """Create or update registry.
+
+        Create or update registry.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of registry. This is case-insensitive. Required.
+        :type registry_name: str
+        :param body: Details required to create the registry. Required.
+        :type body: IO
+        :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either Registry or the result of
+         cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.Registry]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @distributed_trace_async
+    async def begin_create_or_update(
+        self, resource_group_name: str, registry_name: str, body: Union[_models.Registry, IO], **kwargs: Any
+    ) -> AsyncLROPoller[_models.Registry]:
+        """Create or update registry.
+
+        Create or update registry.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of registry. This is case-insensitive. Required.
+        :type registry_name: str
+        :param body: Details required to create the registry. Is either a model type or a IO type.
+         Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.Registry or IO
+        :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
+         Default value is None.
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either Registry or the result of
+         cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.Registry]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.Registry] = kwargs.pop("cls", None)
+        polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = await self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):
+            deserialized = self._deserialize("Registry", pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+        if polling is True:
+            polling_method: AsyncPollingMethod = cast(
+                AsyncPollingMethod,
+                AsyncARMPolling(lro_delay, lro_options={"final-state-via": "azure-async-operation"}, **kwargs),
+            )
+        elif polling is False:
+            polling_method = cast(AsyncPollingMethod, AsyncNoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_create_or_update.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}"
+    }
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_registry_code_containers_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_registry_code_containers_operations.py
new file mode 100644
index 0000000000000..b4bdff094f052
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_registry_code_containers_operations.py
@@ -0,0 +1,630 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+import sys
+from typing import Any, AsyncIterable, Callable, Dict, IO, Optional, TypeVar, Union, cast, overload
+import urllib.parse
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import (
+    ClientAuthenticationError,
+    HttpResponseError,
+    ResourceExistsError,
+    ResourceNotFoundError,
+    ResourceNotModifiedError,
+    map_error,
+)
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.core.utils import case_insensitive_dict
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.async_arm_polling import AsyncARMPolling
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._registry_code_containers_operations import (
+    build_create_or_update_request,
+    build_delete_request,
+    build_get_request,
+    build_list_request,
+)
+
+if sys.version_info >= (3, 8):
+    from typing import Literal  # pylint: disable=no-name-in-module, ungrouped-imports
+else:
+    from typing_extensions import Literal  # type: ignore  # pylint: disable=ungrouped-imports
+T = TypeVar("T")
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+
+class RegistryCodeContainersOperations:
+    """
+    .. warning::
+        **DO NOT** instantiate this class directly.
+
+        Instead, you should access the following operations through
+        :class:`~azure.mgmt.machinelearningservices.aio.MachineLearningServicesMgmtClient`'s
+        :attr:`registry_code_containers` attribute.
+    """
+
+    models = _models
+
+    def __init__(self, *args, **kwargs) -> None:
+        input_args = list(args)
+        self._client = input_args.pop(0) if input_args else kwargs.pop("client")
+        self._config = input_args.pop(0) if input_args else kwargs.pop("config")
+        self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer")
+        self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer")
+
+    @distributed_trace
+    def list(
+        self, resource_group_name: str, registry_name: str, skip: Optional[str] = None, **kwargs: Any
+    ) -> AsyncIterable["_models.CodeContainer"]:
+        """List containers.
+
+        List containers.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param skip: Continuation token for pagination. Default value is None.
+        :type skip: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either CodeContainer or the result of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.CodeContainer]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.CodeContainerResourceArmPaginatedResult] = kwargs.pop("cls", None)
+
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        def prepare_request(next_link=None):
+            if not next_link:
+
+                request = build_list_request(
+                    resource_group_name=resource_group_name,
+                    registry_name=registry_name,
+                    subscription_id=self._config.subscription_id,
+                    skip=skip,
+                    api_version=api_version,
+                    template_url=self.list.metadata["url"],
+                    headers=_headers,
+                    params=_params,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                # make call to next link with the client's api-version
+                _parsed_next_link = urllib.parse.urlparse(next_link)
+                _next_request_params = case_insensitive_dict(
+                    {
+                        key: [urllib.parse.quote(v) for v in value]
+                        for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items()
+                    }
+                )
+                _next_request_params["api-version"] = self._config.api_version
+                request = HttpRequest(
+                    "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("CodeContainerResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)  # type: ignore
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request, stream=False, **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+        return AsyncItemPaged(get_next, extract_data)
+
+    list.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/codes"
+    }
+
+    async def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self, resource_group_name: str, registry_name: str, code_name: str, **kwargs: Any
+    ) -> None:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+
+        request = build_delete_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            code_name=code_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self._delete_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Location"] = self._deserialize("str", response.headers.get("Location"))
+            response_headers["Retry-After"] = self._deserialize("int", response.headers.get("Retry-After"))
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _delete_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/codes/{codeName}"
+    }
+
+    @distributed_trace_async
+    async def begin_delete(
+        self, resource_group_name: str, registry_name: str, code_name: str, **kwargs: Any
+    ) -> AsyncLROPoller[None]:
+        """Delete container.
+
+        Delete container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param code_name: Container name. Required.
+        :type code_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[None]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+        polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = await self._delete_initial(  # type: ignore
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                code_name=code_name,
+                api_version=api_version,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):  # pylint: disable=inconsistent-return-statements
+            if cls:
+                return cls(pipeline_response, None, {})
+
+        if polling is True:
+            polling_method: AsyncPollingMethod = cast(
+                AsyncPollingMethod, AsyncARMPolling(lro_delay, lro_options={"final-state-via": "location"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(AsyncPollingMethod, AsyncNoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_delete.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/codes/{codeName}"
+    }
+
+    @distributed_trace_async
+    async def get(
+        self, resource_group_name: str, registry_name: str, code_name: str, **kwargs: Any
+    ) -> _models.CodeContainer:
+        """Get container.
+
+        Get container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param code_name: Container name. Required.
+        :type code_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: CodeContainer or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.CodeContainer
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.CodeContainer] = kwargs.pop("cls", None)
+
+        request = build_get_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            code_name=code_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self.get.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize("CodeContainer", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/codes/{codeName}"
+    }
+
+    async def _create_or_update_initial(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        code_name: str,
+        body: Union[_models.CodeContainer, IO],
+        **kwargs: Any
+    ) -> _models.CodeContainer:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.CodeContainer] = kwargs.pop("cls", None)
+
+        content_type = content_type or "application/json"
+        _json = None
+        _content = None
+        if isinstance(body, (IO, bytes)):
+            _content = body
+        else:
+            _json = self._serialize.body(body, "CodeContainer")
+
+        request = build_create_or_update_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            code_name=code_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            content=_content,
+            template_url=self._create_or_update_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize("CodeContainer", pipeline_response)
+
+        if response.status_code == 201:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Azure-AsyncOperation"] = self._deserialize(
+                "str", response.headers.get("Azure-AsyncOperation")
+            )
+
+            deserialized = self._deserialize("CodeContainer", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)  # type: ignore
+
+        return deserialized  # type: ignore
+
+    _create_or_update_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/codes/{codeName}"
+    }
+
+    @overload
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        code_name: str,
+        body: _models.CodeContainer,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.CodeContainer]:
+        """Create or update container.
+
+        Create or update container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param code_name: Container name. Required.
+        :type code_name: str
+        :param body: Container entity to create or update. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.CodeContainer
+        :keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either CodeContainer or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.CodeContainer]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @overload
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        code_name: str,
+        body: IO,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.CodeContainer]:
+        """Create or update container.
+
+        Create or update container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param code_name: Container name. Required.
+        :type code_name: str
+        :param body: Container entity to create or update. Required.
+        :type body: IO
+        :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either CodeContainer or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.CodeContainer]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @distributed_trace_async
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        code_name: str,
+        body: Union[_models.CodeContainer, IO],
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.CodeContainer]:
+        """Create or update container.
+
+        Create or update container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param code_name: Container name. Required.
+        :type code_name: str
+        :param body: Container entity to create or update. Is either a model type or a IO type.
+         Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.CodeContainer or IO
+        :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
+         Default value is None.
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either CodeContainer or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.CodeContainer]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.CodeContainer] = kwargs.pop("cls", None)
+        polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = await self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                code_name=code_name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):
+            deserialized = self._deserialize("CodeContainer", pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+        if polling is True:
+            polling_method: AsyncPollingMethod = cast(
+                AsyncPollingMethod,
+                AsyncARMPolling(lro_delay, lro_options={"final-state-via": "original-uri"}, **kwargs),
+            )
+        elif polling is False:
+            polling_method = cast(AsyncPollingMethod, AsyncNoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_create_or_update.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/codes/{codeName}"
+    }
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_registry_code_versions_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_registry_code_versions_operations.py
new file mode 100644
index 0000000000000..e71127ae38195
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_registry_code_versions_operations.py
@@ -0,0 +1,664 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+import sys
+from typing import Any, AsyncIterable, Callable, Dict, IO, Optional, TypeVar, Union, cast, overload
+import urllib.parse
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import (
+    ClientAuthenticationError,
+    HttpResponseError,
+    ResourceExistsError,
+    ResourceNotFoundError,
+    ResourceNotModifiedError,
+    map_error,
+)
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.core.utils import case_insensitive_dict
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.async_arm_polling import AsyncARMPolling
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._registry_code_versions_operations import (
+    build_create_or_update_request,
+    build_delete_request,
+    build_get_request,
+    build_list_request,
+)
+
+if sys.version_info >= (3, 8):
+    from typing import Literal  # pylint: disable=no-name-in-module, ungrouped-imports
+else:
+    from typing_extensions import Literal  # type: ignore  # pylint: disable=ungrouped-imports
+T = TypeVar("T")
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+
+class RegistryCodeVersionsOperations:
+    """
+    .. warning::
+        **DO NOT** instantiate this class directly.
+
+        Instead, you should access the following operations through
+        :class:`~azure.mgmt.machinelearningservices.aio.MachineLearningServicesMgmtClient`'s
+        :attr:`registry_code_versions` attribute.
+    """
+
+    models = _models
+
+    def __init__(self, *args, **kwargs) -> None:
+        input_args = list(args)
+        self._client = input_args.pop(0) if input_args else kwargs.pop("client")
+        self._config = input_args.pop(0) if input_args else kwargs.pop("config")
+        self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer")
+        self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer")
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        code_name: str,
+        order_by: Optional[str] = None,
+        top: Optional[int] = None,
+        skip: Optional[str] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.CodeVersion"]:
+        """List versions.
+
+        List versions.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param code_name: Container name. Required.
+        :type code_name: str
+        :param order_by: Ordering of list. Default value is None.
+        :type order_by: str
+        :param top: Maximum number of records to return. Default value is None.
+        :type top: int
+        :param skip: Continuation token for pagination. Default value is None.
+        :type skip: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either CodeVersion or the result of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.CodeVersion]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.CodeVersionResourceArmPaginatedResult] = kwargs.pop("cls", None)
+
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        def prepare_request(next_link=None):
+            if not next_link:
+
+                request = build_list_request(
+                    resource_group_name=resource_group_name,
+                    registry_name=registry_name,
+                    code_name=code_name,
+                    subscription_id=self._config.subscription_id,
+                    order_by=order_by,
+                    top=top,
+                    skip=skip,
+                    api_version=api_version,
+                    template_url=self.list.metadata["url"],
+                    headers=_headers,
+                    params=_params,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                # make call to next link with the client's api-version
+                _parsed_next_link = urllib.parse.urlparse(next_link)
+                _next_request_params = case_insensitive_dict(
+                    {
+                        key: [urllib.parse.quote(v) for v in value]
+                        for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items()
+                    }
+                )
+                _next_request_params["api-version"] = self._config.api_version
+                request = HttpRequest(
+                    "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("CodeVersionResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)  # type: ignore
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request, stream=False, **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+        return AsyncItemPaged(get_next, extract_data)
+
+    list.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/codes/{codeName}/versions"
+    }
+
+    async def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self, resource_group_name: str, registry_name: str, code_name: str, version: str, **kwargs: Any
+    ) -> None:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+
+        request = build_delete_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            code_name=code_name,
+            version=version,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self._delete_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Location"] = self._deserialize("str", response.headers.get("Location"))
+            response_headers["Retry-After"] = self._deserialize("int", response.headers.get("Retry-After"))
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _delete_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/codes/{codeName}/versions/{version}"
+    }
+
+    @distributed_trace_async
+    async def begin_delete(
+        self, resource_group_name: str, registry_name: str, code_name: str, version: str, **kwargs: Any
+    ) -> AsyncLROPoller[None]:
+        """Delete version.
+
+        Delete version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param code_name: Container name. Required.
+        :type code_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[None]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+        polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = await self._delete_initial(  # type: ignore
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                code_name=code_name,
+                version=version,
+                api_version=api_version,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):  # pylint: disable=inconsistent-return-statements
+            if cls:
+                return cls(pipeline_response, None, {})
+
+        if polling is True:
+            polling_method: AsyncPollingMethod = cast(
+                AsyncPollingMethod, AsyncARMPolling(lro_delay, lro_options={"final-state-via": "location"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(AsyncPollingMethod, AsyncNoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_delete.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/codes/{codeName}/versions/{version}"
+    }
+
+    @distributed_trace_async
+    async def get(
+        self, resource_group_name: str, registry_name: str, code_name: str, version: str, **kwargs: Any
+    ) -> _models.CodeVersion:
+        """Get version.
+
+        Get version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param code_name: Container name. Required.
+        :type code_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: CodeVersion or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.CodeVersion
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.CodeVersion] = kwargs.pop("cls", None)
+
+        request = build_get_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            code_name=code_name,
+            version=version,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self.get.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize("CodeVersion", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/codes/{codeName}/versions/{version}"
+    }
+
+    async def _create_or_update_initial(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        code_name: str,
+        version: str,
+        body: Union[_models.CodeVersion, IO],
+        **kwargs: Any
+    ) -> _models.CodeVersion:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.CodeVersion] = kwargs.pop("cls", None)
+
+        content_type = content_type or "application/json"
+        _json = None
+        _content = None
+        if isinstance(body, (IO, bytes)):
+            _content = body
+        else:
+            _json = self._serialize.body(body, "CodeVersion")
+
+        request = build_create_or_update_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            code_name=code_name,
+            version=version,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            content=_content,
+            template_url=self._create_or_update_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize("CodeVersion", pipeline_response)
+
+        if response.status_code == 201:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Azure-AsyncOperation"] = self._deserialize(
+                "str", response.headers.get("Azure-AsyncOperation")
+            )
+
+            deserialized = self._deserialize("CodeVersion", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)  # type: ignore
+
+        return deserialized  # type: ignore
+
+    _create_or_update_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/codes/{codeName}/versions/{version}"
+    }
+
+    @overload
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        code_name: str,
+        version: str,
+        body: _models.CodeVersion,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.CodeVersion]:
+        """Create or update version.
+
+        Create or update version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param code_name: Container name. Required.
+        :type code_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :param body: Version entity to create or update. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.CodeVersion
+        :keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either CodeVersion or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.CodeVersion]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @overload
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        code_name: str,
+        version: str,
+        body: IO,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.CodeVersion]:
+        """Create or update version.
+
+        Create or update version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param code_name: Container name. Required.
+        :type code_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :param body: Version entity to create or update. Required.
+        :type body: IO
+        :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either CodeVersion or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.CodeVersion]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @distributed_trace_async
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        code_name: str,
+        version: str,
+        body: Union[_models.CodeVersion, IO],
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.CodeVersion]:
+        """Create or update version.
+
+        Create or update version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param code_name: Container name. Required.
+        :type code_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :param body: Version entity to create or update. Is either a model type or a IO type. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.CodeVersion or IO
+        :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
+         Default value is None.
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either CodeVersion or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.CodeVersion]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.CodeVersion] = kwargs.pop("cls", None)
+        polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = await self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                code_name=code_name,
+                version=version,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):
+            deserialized = self._deserialize("CodeVersion", pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+        if polling is True:
+            polling_method: AsyncPollingMethod = cast(
+                AsyncPollingMethod,
+                AsyncARMPolling(lro_delay, lro_options={"final-state-via": "original-uri"}, **kwargs),
+            )
+        elif polling is False:
+            polling_method = cast(AsyncPollingMethod, AsyncNoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_create_or_update.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/codes/{codeName}/versions/{version}"
+    }
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_registry_component_containers_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_registry_component_containers_operations.py
new file mode 100644
index 0000000000000..a59227b2d1421
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_registry_component_containers_operations.py
@@ -0,0 +1,630 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+import sys
+from typing import Any, AsyncIterable, Callable, Dict, IO, Optional, TypeVar, Union, cast, overload
+import urllib.parse
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import (
+    ClientAuthenticationError,
+    HttpResponseError,
+    ResourceExistsError,
+    ResourceNotFoundError,
+    ResourceNotModifiedError,
+    map_error,
+)
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.core.utils import case_insensitive_dict
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.async_arm_polling import AsyncARMPolling
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._registry_component_containers_operations import (
+    build_create_or_update_request,
+    build_delete_request,
+    build_get_request,
+    build_list_request,
+)
+
+if sys.version_info >= (3, 8):
+    from typing import Literal  # pylint: disable=no-name-in-module, ungrouped-imports
+else:
+    from typing_extensions import Literal  # type: ignore  # pylint: disable=ungrouped-imports
+T = TypeVar("T")
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+
+class RegistryComponentContainersOperations:
+    """
+    .. warning::
+        **DO NOT** instantiate this class directly.
+
+        Instead, you should access the following operations through
+        :class:`~azure.mgmt.machinelearningservices.aio.MachineLearningServicesMgmtClient`'s
+        :attr:`registry_component_containers` attribute.
+    """
+
+    models = _models
+
+    def __init__(self, *args, **kwargs) -> None:
+        input_args = list(args)
+        self._client = input_args.pop(0) if input_args else kwargs.pop("client")
+        self._config = input_args.pop(0) if input_args else kwargs.pop("config")
+        self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer")
+        self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer")
+
+    @distributed_trace
+    def list(
+        self, resource_group_name: str, registry_name: str, skip: Optional[str] = None, **kwargs: Any
+    ) -> AsyncIterable["_models.ComponentContainer"]:
+        """List containers.
+
+        List containers.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param skip: Continuation token for pagination. Default value is None.
+        :type skip: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either ComponentContainer or the result of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.ComponentContainer]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.ComponentContainerResourceArmPaginatedResult] = kwargs.pop("cls", None)
+
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        def prepare_request(next_link=None):
+            if not next_link:
+
+                request = build_list_request(
+                    resource_group_name=resource_group_name,
+                    registry_name=registry_name,
+                    subscription_id=self._config.subscription_id,
+                    skip=skip,
+                    api_version=api_version,
+                    template_url=self.list.metadata["url"],
+                    headers=_headers,
+                    params=_params,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                # make call to next link with the client's api-version
+                _parsed_next_link = urllib.parse.urlparse(next_link)
+                _next_request_params = case_insensitive_dict(
+                    {
+                        key: [urllib.parse.quote(v) for v in value]
+                        for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items()
+                    }
+                )
+                _next_request_params["api-version"] = self._config.api_version
+                request = HttpRequest(
+                    "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("ComponentContainerResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)  # type: ignore
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request, stream=False, **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+        return AsyncItemPaged(get_next, extract_data)
+
+    list.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/components"
+    }
+
+    async def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self, resource_group_name: str, registry_name: str, component_name: str, **kwargs: Any
+    ) -> None:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+
+        request = build_delete_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            component_name=component_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self._delete_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Location"] = self._deserialize("str", response.headers.get("Location"))
+            response_headers["Retry-After"] = self._deserialize("int", response.headers.get("Retry-After"))
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _delete_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/components/{componentName}"
+    }
+
+    @distributed_trace_async
+    async def begin_delete(
+        self, resource_group_name: str, registry_name: str, component_name: str, **kwargs: Any
+    ) -> AsyncLROPoller[None]:
+        """Delete container.
+
+        Delete container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param component_name: Container name. Required.
+        :type component_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[None]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+        polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = await self._delete_initial(  # type: ignore
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                component_name=component_name,
+                api_version=api_version,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):  # pylint: disable=inconsistent-return-statements
+            if cls:
+                return cls(pipeline_response, None, {})
+
+        if polling is True:
+            polling_method: AsyncPollingMethod = cast(
+                AsyncPollingMethod, AsyncARMPolling(lro_delay, lro_options={"final-state-via": "location"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(AsyncPollingMethod, AsyncNoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_delete.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/components/{componentName}"
+    }
+
+    @distributed_trace_async
+    async def get(
+        self, resource_group_name: str, registry_name: str, component_name: str, **kwargs: Any
+    ) -> _models.ComponentContainer:
+        """Get container.
+
+        Get container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param component_name: Container name. Required.
+        :type component_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ComponentContainer or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ComponentContainer
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.ComponentContainer] = kwargs.pop("cls", None)
+
+        request = build_get_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            component_name=component_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self.get.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize("ComponentContainer", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/components/{componentName}"
+    }
+
+    async def _create_or_update_initial(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        component_name: str,
+        body: Union[_models.ComponentContainer, IO],
+        **kwargs: Any
+    ) -> _models.ComponentContainer:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.ComponentContainer] = kwargs.pop("cls", None)
+
+        content_type = content_type or "application/json"
+        _json = None
+        _content = None
+        if isinstance(body, (IO, bytes)):
+            _content = body
+        else:
+            _json = self._serialize.body(body, "ComponentContainer")
+
+        request = build_create_or_update_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            component_name=component_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            content=_content,
+            template_url=self._create_or_update_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize("ComponentContainer", pipeline_response)
+
+        if response.status_code == 201:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Azure-AsyncOperation"] = self._deserialize(
+                "str", response.headers.get("Azure-AsyncOperation")
+            )
+
+            deserialized = self._deserialize("ComponentContainer", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)  # type: ignore
+
+        return deserialized  # type: ignore
+
+    _create_or_update_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/components/{componentName}"
+    }
+
+    @overload
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        component_name: str,
+        body: _models.ComponentContainer,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.ComponentContainer]:
+        """Create or update container.
+
+        Create or update container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param component_name: Container name. Required.
+        :type component_name: str
+        :param body: Container entity to create or update. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.ComponentContainer
+        :keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either ComponentContainer or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.ComponentContainer]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @overload
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        component_name: str,
+        body: IO,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.ComponentContainer]:
+        """Create or update container.
+
+        Create or update container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param component_name: Container name. Required.
+        :type component_name: str
+        :param body: Container entity to create or update. Required.
+        :type body: IO
+        :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either ComponentContainer or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.ComponentContainer]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @distributed_trace_async
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        component_name: str,
+        body: Union[_models.ComponentContainer, IO],
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.ComponentContainer]:
+        """Create or update container.
+
+        Create or update container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param component_name: Container name. Required.
+        :type component_name: str
+        :param body: Container entity to create or update. Is either a model type or a IO type.
+         Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.ComponentContainer or IO
+        :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
+         Default value is None.
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either ComponentContainer or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.ComponentContainer]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.ComponentContainer] = kwargs.pop("cls", None)
+        polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = await self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                component_name=component_name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):
+            deserialized = self._deserialize("ComponentContainer", pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+        if polling is True:
+            polling_method: AsyncPollingMethod = cast(
+                AsyncPollingMethod,
+                AsyncARMPolling(lro_delay, lro_options={"final-state-via": "original-uri"}, **kwargs),
+            )
+        elif polling is False:
+            polling_method = cast(AsyncPollingMethod, AsyncNoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_create_or_update.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/components/{componentName}"
+    }
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_registry_component_versions_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_registry_component_versions_operations.py
new file mode 100644
index 0000000000000..8f8040580d642
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_registry_component_versions_operations.py
@@ -0,0 +1,664 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+import sys
+from typing import Any, AsyncIterable, Callable, Dict, IO, Optional, TypeVar, Union, cast, overload
+import urllib.parse
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import (
+    ClientAuthenticationError,
+    HttpResponseError,
+    ResourceExistsError,
+    ResourceNotFoundError,
+    ResourceNotModifiedError,
+    map_error,
+)
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.core.utils import case_insensitive_dict
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.async_arm_polling import AsyncARMPolling
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._registry_component_versions_operations import (
+    build_create_or_update_request,
+    build_delete_request,
+    build_get_request,
+    build_list_request,
+)
+
+if sys.version_info >= (3, 8):
+    from typing import Literal  # pylint: disable=no-name-in-module, ungrouped-imports
+else:
+    from typing_extensions import Literal  # type: ignore  # pylint: disable=ungrouped-imports
+T = TypeVar("T")
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+
+class RegistryComponentVersionsOperations:
+    """
+    .. warning::
+        **DO NOT** instantiate this class directly.
+
+        Instead, you should access the following operations through
+        :class:`~azure.mgmt.machinelearningservices.aio.MachineLearningServicesMgmtClient`'s
+        :attr:`registry_component_versions` attribute.
+    """
+
+    models = _models
+
+    def __init__(self, *args, **kwargs) -> None:
+        input_args = list(args)
+        self._client = input_args.pop(0) if input_args else kwargs.pop("client")
+        self._config = input_args.pop(0) if input_args else kwargs.pop("config")
+        self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer")
+        self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer")
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        component_name: str,
+        order_by: Optional[str] = None,
+        top: Optional[int] = None,
+        skip: Optional[str] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.ComponentVersion"]:
+        """List versions.
+
+        List versions.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param component_name: Container name. Required.
+        :type component_name: str
+        :param order_by: Ordering of list. Default value is None.
+        :type order_by: str
+        :param top: Maximum number of records to return. Default value is None.
+        :type top: int
+        :param skip: Continuation token for pagination. Default value is None.
+        :type skip: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either ComponentVersion or the result of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.ComponentVersion]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.ComponentVersionResourceArmPaginatedResult] = kwargs.pop("cls", None)
+
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        def prepare_request(next_link=None):
+            if not next_link:
+
+                request = build_list_request(
+                    resource_group_name=resource_group_name,
+                    registry_name=registry_name,
+                    component_name=component_name,
+                    subscription_id=self._config.subscription_id,
+                    order_by=order_by,
+                    top=top,
+                    skip=skip,
+                    api_version=api_version,
+                    template_url=self.list.metadata["url"],
+                    headers=_headers,
+                    params=_params,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                # make call to next link with the client's api-version
+                _parsed_next_link = urllib.parse.urlparse(next_link)
+                _next_request_params = case_insensitive_dict(
+                    {
+                        key: [urllib.parse.quote(v) for v in value]
+                        for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items()
+                    }
+                )
+                _next_request_params["api-version"] = self._config.api_version
+                request = HttpRequest(
+                    "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("ComponentVersionResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)  # type: ignore
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request, stream=False, **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+        return AsyncItemPaged(get_next, extract_data)
+
+    list.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/components/{componentName}/versions"
+    }
+
+    async def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self, resource_group_name: str, registry_name: str, component_name: str, version: str, **kwargs: Any
+    ) -> None:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+
+        request = build_delete_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            component_name=component_name,
+            version=version,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self._delete_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Location"] = self._deserialize("str", response.headers.get("Location"))
+            response_headers["Retry-After"] = self._deserialize("int", response.headers.get("Retry-After"))
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _delete_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/components/{componentName}/versions/{version}"
+    }
+
+    @distributed_trace_async
+    async def begin_delete(
+        self, resource_group_name: str, registry_name: str, component_name: str, version: str, **kwargs: Any
+    ) -> AsyncLROPoller[None]:
+        """Delete version.
+
+        Delete version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param component_name: Container name. Required.
+        :type component_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[None]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+        polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = await self._delete_initial(  # type: ignore
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                component_name=component_name,
+                version=version,
+                api_version=api_version,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):  # pylint: disable=inconsistent-return-statements
+            if cls:
+                return cls(pipeline_response, None, {})
+
+        if polling is True:
+            polling_method: AsyncPollingMethod = cast(
+                AsyncPollingMethod, AsyncARMPolling(lro_delay, lro_options={"final-state-via": "location"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(AsyncPollingMethod, AsyncNoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_delete.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/components/{componentName}/versions/{version}"
+    }
+
+    @distributed_trace_async
+    async def get(
+        self, resource_group_name: str, registry_name: str, component_name: str, version: str, **kwargs: Any
+    ) -> _models.ComponentVersion:
+        """Get version.
+
+        Get version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param component_name: Container name. Required.
+        :type component_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ComponentVersion or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ComponentVersion
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.ComponentVersion] = kwargs.pop("cls", None)
+
+        request = build_get_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            component_name=component_name,
+            version=version,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self.get.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize("ComponentVersion", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/components/{componentName}/versions/{version}"
+    }
+
+    async def _create_or_update_initial(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        component_name: str,
+        version: str,
+        body: Union[_models.ComponentVersion, IO],
+        **kwargs: Any
+    ) -> _models.ComponentVersion:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.ComponentVersion] = kwargs.pop("cls", None)
+
+        content_type = content_type or "application/json"
+        _json = None
+        _content = None
+        if isinstance(body, (IO, bytes)):
+            _content = body
+        else:
+            _json = self._serialize.body(body, "ComponentVersion")
+
+        request = build_create_or_update_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            component_name=component_name,
+            version=version,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            content=_content,
+            template_url=self._create_or_update_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize("ComponentVersion", pipeline_response)
+
+        if response.status_code == 201:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Azure-AsyncOperation"] = self._deserialize(
+                "str", response.headers.get("Azure-AsyncOperation")
+            )
+
+            deserialized = self._deserialize("ComponentVersion", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)  # type: ignore
+
+        return deserialized  # type: ignore
+
+    _create_or_update_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/components/{componentName}/versions/{version}"
+    }
+
+    @overload
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        component_name: str,
+        version: str,
+        body: _models.ComponentVersion,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.ComponentVersion]:
+        """Create or update version.
+
+        Create or update version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param component_name: Container name. Required.
+        :type component_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :param body: Version entity to create or update. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.ComponentVersion
+        :keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either ComponentVersion or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.ComponentVersion]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @overload
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        component_name: str,
+        version: str,
+        body: IO,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.ComponentVersion]:
+        """Create or update version.
+
+        Create or update version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param component_name: Container name. Required.
+        :type component_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :param body: Version entity to create or update. Required.
+        :type body: IO
+        :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either ComponentVersion or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.ComponentVersion]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @distributed_trace_async
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        component_name: str,
+        version: str,
+        body: Union[_models.ComponentVersion, IO],
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.ComponentVersion]:
+        """Create or update version.
+
+        Create or update version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param component_name: Container name. Required.
+        :type component_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :param body: Version entity to create or update. Is either a model type or a IO type. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.ComponentVersion or IO
+        :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
+         Default value is None.
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either ComponentVersion or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.ComponentVersion]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.ComponentVersion] = kwargs.pop("cls", None)
+        polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = await self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                component_name=component_name,
+                version=version,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):
+            deserialized = self._deserialize("ComponentVersion", pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+        if polling is True:
+            polling_method: AsyncPollingMethod = cast(
+                AsyncPollingMethod,
+                AsyncARMPolling(lro_delay, lro_options={"final-state-via": "original-uri"}, **kwargs),
+            )
+        elif polling is False:
+            polling_method = cast(AsyncPollingMethod, AsyncNoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_create_or_update.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/components/{componentName}/versions/{version}"
+    }
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_registry_environment_containers_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_registry_environment_containers_operations.py
new file mode 100644
index 0000000000000..a47c8d9811f35
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_registry_environment_containers_operations.py
@@ -0,0 +1,640 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+import sys
+from typing import Any, AsyncIterable, Callable, Dict, IO, Optional, TypeVar, Union, cast, overload
+import urllib.parse
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import (
+    ClientAuthenticationError,
+    HttpResponseError,
+    ResourceExistsError,
+    ResourceNotFoundError,
+    ResourceNotModifiedError,
+    map_error,
+)
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.core.utils import case_insensitive_dict
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.async_arm_polling import AsyncARMPolling
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._registry_environment_containers_operations import (
+    build_create_or_update_request,
+    build_delete_request,
+    build_get_request,
+    build_list_request,
+)
+
+if sys.version_info >= (3, 8):
+    from typing import Literal  # pylint: disable=no-name-in-module, ungrouped-imports
+else:
+    from typing_extensions import Literal  # type: ignore  # pylint: disable=ungrouped-imports
+T = TypeVar("T")
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+
+class RegistryEnvironmentContainersOperations:
+    """
+    .. warning::
+        **DO NOT** instantiate this class directly.
+
+        Instead, you should access the following operations through
+        :class:`~azure.mgmt.machinelearningservices.aio.MachineLearningServicesMgmtClient`'s
+        :attr:`registry_environment_containers` attribute.
+    """
+
+    models = _models
+
+    def __init__(self, *args, **kwargs) -> None:
+        input_args = list(args)
+        self._client = input_args.pop(0) if input_args else kwargs.pop("client")
+        self._config = input_args.pop(0) if input_args else kwargs.pop("config")
+        self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer")
+        self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer")
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        skip: Optional[str] = None,
+        list_view_type: Optional[Union[str, _models.ListViewType]] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.EnvironmentContainer"]:
+        """List environment containers.
+
+        List environment containers.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param skip: Continuation token for pagination. Default value is None.
+        :type skip: str
+        :param list_view_type: View type for including/excluding (for example) archived entities. Known
+         values are: "ActiveOnly", "ArchivedOnly", and "All". Default value is None.
+        :type list_view_type: str or ~azure.mgmt.machinelearningservices.models.ListViewType
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either EnvironmentContainer or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.EnvironmentContainer]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.EnvironmentContainerResourceArmPaginatedResult] = kwargs.pop("cls", None)
+
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        def prepare_request(next_link=None):
+            if not next_link:
+
+                request = build_list_request(
+                    resource_group_name=resource_group_name,
+                    registry_name=registry_name,
+                    subscription_id=self._config.subscription_id,
+                    skip=skip,
+                    list_view_type=list_view_type,
+                    api_version=api_version,
+                    template_url=self.list.metadata["url"],
+                    headers=_headers,
+                    params=_params,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                # make call to next link with the client's api-version
+                _parsed_next_link = urllib.parse.urlparse(next_link)
+                _next_request_params = case_insensitive_dict(
+                    {
+                        key: [urllib.parse.quote(v) for v in value]
+                        for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items()
+                    }
+                )
+                _next_request_params["api-version"] = self._config.api_version
+                request = HttpRequest(
+                    "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("EnvironmentContainerResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)  # type: ignore
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request, stream=False, **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+        return AsyncItemPaged(get_next, extract_data)
+
+    list.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/environments"
+    }
+
+    async def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self, resource_group_name: str, registry_name: str, environment_name: str, **kwargs: Any
+    ) -> None:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+
+        request = build_delete_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            environment_name=environment_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self._delete_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Location"] = self._deserialize("str", response.headers.get("Location"))
+            response_headers["Retry-After"] = self._deserialize("int", response.headers.get("Retry-After"))
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _delete_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/environments/{environmentName}"
+    }
+
+    @distributed_trace_async
+    async def begin_delete(
+        self, resource_group_name: str, registry_name: str, environment_name: str, **kwargs: Any
+    ) -> AsyncLROPoller[None]:
+        """Delete container.
+
+        Delete container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param environment_name: Container name. Required.
+        :type environment_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[None]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+        polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = await self._delete_initial(  # type: ignore
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                environment_name=environment_name,
+                api_version=api_version,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):  # pylint: disable=inconsistent-return-statements
+            if cls:
+                return cls(pipeline_response, None, {})
+
+        if polling is True:
+            polling_method: AsyncPollingMethod = cast(
+                AsyncPollingMethod, AsyncARMPolling(lro_delay, lro_options={"final-state-via": "location"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(AsyncPollingMethod, AsyncNoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_delete.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/environments/{environmentName}"
+    }
+
+    @distributed_trace_async
+    async def get(
+        self, resource_group_name: str, registry_name: str, environment_name: str, **kwargs: Any
+    ) -> _models.EnvironmentContainer:
+        """Get container.
+
+        Get container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param environment_name: Container name. This is case-sensitive. Required.
+        :type environment_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: EnvironmentContainer or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.EnvironmentContainer
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.EnvironmentContainer] = kwargs.pop("cls", None)
+
+        request = build_get_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            environment_name=environment_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self.get.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize("EnvironmentContainer", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/environments/{environmentName}"
+    }
+
+    async def _create_or_update_initial(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        environment_name: str,
+        body: Union[_models.EnvironmentContainer, IO],
+        **kwargs: Any
+    ) -> _models.EnvironmentContainer:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.EnvironmentContainer] = kwargs.pop("cls", None)
+
+        content_type = content_type or "application/json"
+        _json = None
+        _content = None
+        if isinstance(body, (IO, bytes)):
+            _content = body
+        else:
+            _json = self._serialize.body(body, "EnvironmentContainer")
+
+        request = build_create_or_update_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            environment_name=environment_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            content=_content,
+            template_url=self._create_or_update_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize("EnvironmentContainer", pipeline_response)
+
+        if response.status_code == 201:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Azure-AsyncOperation"] = self._deserialize(
+                "str", response.headers.get("Azure-AsyncOperation")
+            )
+
+            deserialized = self._deserialize("EnvironmentContainer", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)  # type: ignore
+
+        return deserialized  # type: ignore
+
+    _create_or_update_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/environments/{environmentName}"
+    }
+
+    @overload
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        environment_name: str,
+        body: _models.EnvironmentContainer,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.EnvironmentContainer]:
+        """Create or update container.
+
+        Create or update container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param environment_name: Container name. Required.
+        :type environment_name: str
+        :param body: Container entity to create or update. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.EnvironmentContainer
+        :keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either EnvironmentContainer or the result
+         of cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.EnvironmentContainer]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @overload
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        environment_name: str,
+        body: IO,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.EnvironmentContainer]:
+        """Create or update container.
+
+        Create or update container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param environment_name: Container name. Required.
+        :type environment_name: str
+        :param body: Container entity to create or update. Required.
+        :type body: IO
+        :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either EnvironmentContainer or the result
+         of cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.EnvironmentContainer]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @distributed_trace_async
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        environment_name: str,
+        body: Union[_models.EnvironmentContainer, IO],
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.EnvironmentContainer]:
+        """Create or update container.
+
+        Create or update container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param environment_name: Container name. Required.
+        :type environment_name: str
+        :param body: Container entity to create or update. Is either a model type or a IO type.
+         Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.EnvironmentContainer or IO
+        :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
+         Default value is None.
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either EnvironmentContainer or the result
+         of cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.EnvironmentContainer]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.EnvironmentContainer] = kwargs.pop("cls", None)
+        polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = await self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                environment_name=environment_name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):
+            deserialized = self._deserialize("EnvironmentContainer", pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+        if polling is True:
+            polling_method: AsyncPollingMethod = cast(
+                AsyncPollingMethod,
+                AsyncARMPolling(lro_delay, lro_options={"final-state-via": "original-uri"}, **kwargs),
+            )
+        elif polling is False:
+            polling_method = cast(AsyncPollingMethod, AsyncNoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_create_or_update.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/environments/{environmentName}"
+    }
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_registry_environment_versions_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_registry_environment_versions_operations.py
new file mode 100644
index 0000000000000..30c7aeea9e56b
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_registry_environment_versions_operations.py
@@ -0,0 +1,669 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+import sys
+from typing import Any, AsyncIterable, Callable, Dict, IO, Optional, TypeVar, Union, cast, overload
+import urllib.parse
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import (
+    ClientAuthenticationError,
+    HttpResponseError,
+    ResourceExistsError,
+    ResourceNotFoundError,
+    ResourceNotModifiedError,
+    map_error,
+)
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.core.utils import case_insensitive_dict
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.async_arm_polling import AsyncARMPolling
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._registry_environment_versions_operations import (
+    build_create_or_update_request,
+    build_delete_request,
+    build_get_request,
+    build_list_request,
+)
+
+if sys.version_info >= (3, 8):
+    from typing import Literal  # pylint: disable=no-name-in-module, ungrouped-imports
+else:
+    from typing_extensions import Literal  # type: ignore  # pylint: disable=ungrouped-imports
+T = TypeVar("T")
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+
+class RegistryEnvironmentVersionsOperations:
+    """
+    .. warning::
+        **DO NOT** instantiate this class directly.
+
+        Instead, you should access the following operations through
+        :class:`~azure.mgmt.machinelearningservices.aio.MachineLearningServicesMgmtClient`'s
+        :attr:`registry_environment_versions` attribute.
+    """
+
+    models = _models
+
+    def __init__(self, *args, **kwargs) -> None:
+        input_args = list(args)
+        self._client = input_args.pop(0) if input_args else kwargs.pop("client")
+        self._config = input_args.pop(0) if input_args else kwargs.pop("config")
+        self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer")
+        self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer")
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        environment_name: str,
+        order_by: Optional[str] = None,
+        top: Optional[int] = None,
+        skip: Optional[str] = None,
+        list_view_type: Optional[Union[str, _models.ListViewType]] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.EnvironmentVersion"]:
+        """List versions.
+
+        List versions.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param environment_name: Container name. This is case-sensitive. Required.
+        :type environment_name: str
+        :param order_by: Ordering of list. Default value is None.
+        :type order_by: str
+        :param top: Maximum number of records to return. Default value is None.
+        :type top: int
+        :param skip: Continuation token for pagination. Default value is None.
+        :type skip: str
+        :param list_view_type: View type for including/excluding (for example) archived entities. Known
+         values are: "ActiveOnly", "ArchivedOnly", and "All". Default value is None.
+        :type list_view_type: str or ~azure.mgmt.machinelearningservices.models.ListViewType
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either EnvironmentVersion or the result of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.EnvironmentVersion]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.EnvironmentVersionResourceArmPaginatedResult] = kwargs.pop("cls", None)
+
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        def prepare_request(next_link=None):
+            if not next_link:
+
+                request = build_list_request(
+                    resource_group_name=resource_group_name,
+                    registry_name=registry_name,
+                    environment_name=environment_name,
+                    subscription_id=self._config.subscription_id,
+                    order_by=order_by,
+                    top=top,
+                    skip=skip,
+                    list_view_type=list_view_type,
+                    api_version=api_version,
+                    template_url=self.list.metadata["url"],
+                    headers=_headers,
+                    params=_params,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                # make call to next link with the client's api-version
+                _parsed_next_link = urllib.parse.urlparse(next_link)
+                _next_request_params = case_insensitive_dict(
+                    {
+                        key: [urllib.parse.quote(v) for v in value]
+                        for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items()
+                    }
+                )
+                _next_request_params["api-version"] = self._config.api_version
+                request = HttpRequest(
+                    "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("EnvironmentVersionResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)  # type: ignore
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request, stream=False, **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+        return AsyncItemPaged(get_next, extract_data)
+
+    list.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/environments/{environmentName}/versions"
+    }
+
+    async def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self, resource_group_name: str, registry_name: str, environment_name: str, version: str, **kwargs: Any
+    ) -> None:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+
+        request = build_delete_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            environment_name=environment_name,
+            version=version,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self._delete_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Location"] = self._deserialize("str", response.headers.get("Location"))
+            response_headers["Retry-After"] = self._deserialize("int", response.headers.get("Retry-After"))
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _delete_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/environments/{environmentName}/versions/{version}"
+    }
+
+    @distributed_trace_async
+    async def begin_delete(
+        self, resource_group_name: str, registry_name: str, environment_name: str, version: str, **kwargs: Any
+    ) -> AsyncLROPoller[None]:
+        """Delete version.
+
+        Delete version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param environment_name: Container name. Required.
+        :type environment_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[None]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+        polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = await self._delete_initial(  # type: ignore
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                environment_name=environment_name,
+                version=version,
+                api_version=api_version,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):  # pylint: disable=inconsistent-return-statements
+            if cls:
+                return cls(pipeline_response, None, {})
+
+        if polling is True:
+            polling_method: AsyncPollingMethod = cast(
+                AsyncPollingMethod, AsyncARMPolling(lro_delay, lro_options={"final-state-via": "location"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(AsyncPollingMethod, AsyncNoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_delete.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/environments/{environmentName}/versions/{version}"
+    }
+
+    @distributed_trace_async
+    async def get(
+        self, resource_group_name: str, registry_name: str, environment_name: str, version: str, **kwargs: Any
+    ) -> _models.EnvironmentVersion:
+        """Get version.
+
+        Get version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param environment_name: Container name. This is case-sensitive. Required.
+        :type environment_name: str
+        :param version: Version identifier. This is case-sensitive. Required.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: EnvironmentVersion or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.EnvironmentVersion
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.EnvironmentVersion] = kwargs.pop("cls", None)
+
+        request = build_get_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            environment_name=environment_name,
+            version=version,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self.get.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize("EnvironmentVersion", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/environments/{environmentName}/versions/{version}"
+    }
+
+    async def _create_or_update_initial(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        environment_name: str,
+        version: str,
+        body: Union[_models.EnvironmentVersion, IO],
+        **kwargs: Any
+    ) -> _models.EnvironmentVersion:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.EnvironmentVersion] = kwargs.pop("cls", None)
+
+        content_type = content_type or "application/json"
+        _json = None
+        _content = None
+        if isinstance(body, (IO, bytes)):
+            _content = body
+        else:
+            _json = self._serialize.body(body, "EnvironmentVersion")
+
+        request = build_create_or_update_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            environment_name=environment_name,
+            version=version,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            content=_content,
+            template_url=self._create_or_update_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize("EnvironmentVersion", pipeline_response)
+
+        if response.status_code == 201:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Azure-AsyncOperation"] = self._deserialize(
+                "str", response.headers.get("Azure-AsyncOperation")
+            )
+
+            deserialized = self._deserialize("EnvironmentVersion", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)  # type: ignore
+
+        return deserialized  # type: ignore
+
+    _create_or_update_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/environments/{environmentName}/versions/{version}"
+    }
+
+    @overload
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        environment_name: str,
+        version: str,
+        body: _models.EnvironmentVersion,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.EnvironmentVersion]:
+        """Create or update version.
+
+        Create or update version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param environment_name: Container name. Required.
+        :type environment_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :param body: Version entity to create or update. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.EnvironmentVersion
+        :keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either EnvironmentVersion or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.EnvironmentVersion]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @overload
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        environment_name: str,
+        version: str,
+        body: IO,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.EnvironmentVersion]:
+        """Create or update version.
+
+        Create or update version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param environment_name: Container name. Required.
+        :type environment_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :param body: Version entity to create or update. Required.
+        :type body: IO
+        :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either EnvironmentVersion or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.EnvironmentVersion]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @distributed_trace_async
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        environment_name: str,
+        version: str,
+        body: Union[_models.EnvironmentVersion, IO],
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.EnvironmentVersion]:
+        """Create or update version.
+
+        Create or update version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param environment_name: Container name. Required.
+        :type environment_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :param body: Version entity to create or update. Is either a model type or a IO type. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.EnvironmentVersion or IO
+        :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
+         Default value is None.
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either EnvironmentVersion or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.EnvironmentVersion]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.EnvironmentVersion] = kwargs.pop("cls", None)
+        polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = await self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                environment_name=environment_name,
+                version=version,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):
+            deserialized = self._deserialize("EnvironmentVersion", pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+        if polling is True:
+            polling_method: AsyncPollingMethod = cast(
+                AsyncPollingMethod,
+                AsyncARMPolling(lro_delay, lro_options={"final-state-via": "original-uri"}, **kwargs),
+            )
+        elif polling is False:
+            polling_method = cast(AsyncPollingMethod, AsyncNoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_create_or_update.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/environments/{environmentName}/versions/{version}"
+    }
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_registry_model_containers_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_registry_model_containers_operations.py
new file mode 100644
index 0000000000000..447d7b78ee51f
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_registry_model_containers_operations.py
@@ -0,0 +1,639 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+import sys
+from typing import Any, AsyncIterable, Callable, Dict, IO, Optional, TypeVar, Union, cast, overload
+import urllib.parse
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import (
+    ClientAuthenticationError,
+    HttpResponseError,
+    ResourceExistsError,
+    ResourceNotFoundError,
+    ResourceNotModifiedError,
+    map_error,
+)
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.core.utils import case_insensitive_dict
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.async_arm_polling import AsyncARMPolling
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._registry_model_containers_operations import (
+    build_create_or_update_request,
+    build_delete_request,
+    build_get_request,
+    build_list_request,
+)
+
+if sys.version_info >= (3, 8):
+    from typing import Literal  # pylint: disable=no-name-in-module, ungrouped-imports
+else:
+    from typing_extensions import Literal  # type: ignore  # pylint: disable=ungrouped-imports
+T = TypeVar("T")
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+
+class RegistryModelContainersOperations:
+    """
+    .. warning::
+        **DO NOT** instantiate this class directly.
+
+        Instead, you should access the following operations through
+        :class:`~azure.mgmt.machinelearningservices.aio.MachineLearningServicesMgmtClient`'s
+        :attr:`registry_model_containers` attribute.
+    """
+
+    models = _models
+
+    def __init__(self, *args, **kwargs) -> None:
+        input_args = list(args)
+        self._client = input_args.pop(0) if input_args else kwargs.pop("client")
+        self._config = input_args.pop(0) if input_args else kwargs.pop("config")
+        self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer")
+        self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer")
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        skip: Optional[str] = None,
+        list_view_type: Optional[Union[str, _models.ListViewType]] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.ModelContainer"]:
+        """List model containers.
+
+        List model containers.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param skip: Continuation token for pagination. Default value is None.
+        :type skip: str
+        :param list_view_type: View type for including/excluding (for example) archived entities. Known
+         values are: "ActiveOnly", "ArchivedOnly", and "All". Default value is None.
+        :type list_view_type: str or ~azure.mgmt.machinelearningservices.models.ListViewType
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either ModelContainer or the result of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.ModelContainer]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.ModelContainerResourceArmPaginatedResult] = kwargs.pop("cls", None)
+
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        def prepare_request(next_link=None):
+            if not next_link:
+
+                request = build_list_request(
+                    resource_group_name=resource_group_name,
+                    registry_name=registry_name,
+                    subscription_id=self._config.subscription_id,
+                    skip=skip,
+                    list_view_type=list_view_type,
+                    api_version=api_version,
+                    template_url=self.list.metadata["url"],
+                    headers=_headers,
+                    params=_params,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                # make call to next link with the client's api-version
+                _parsed_next_link = urllib.parse.urlparse(next_link)
+                _next_request_params = case_insensitive_dict(
+                    {
+                        key: [urllib.parse.quote(v) for v in value]
+                        for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items()
+                    }
+                )
+                _next_request_params["api-version"] = self._config.api_version
+                request = HttpRequest(
+                    "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("ModelContainerResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)  # type: ignore
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request, stream=False, **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+        return AsyncItemPaged(get_next, extract_data)
+
+    list.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/models"
+    }
+
+    async def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self, resource_group_name: str, registry_name: str, model_name: str, **kwargs: Any
+    ) -> None:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+
+        request = build_delete_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            model_name=model_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self._delete_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Location"] = self._deserialize("str", response.headers.get("Location"))
+            response_headers["Retry-After"] = self._deserialize("int", response.headers.get("Retry-After"))
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _delete_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/models/{modelName}"
+    }
+
+    @distributed_trace_async
+    async def begin_delete(
+        self, resource_group_name: str, registry_name: str, model_name: str, **kwargs: Any
+    ) -> AsyncLROPoller[None]:
+        """Delete container.
+
+        Delete container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param model_name: Container name. Required.
+        :type model_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[None]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+        polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = await self._delete_initial(  # type: ignore
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                model_name=model_name,
+                api_version=api_version,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):  # pylint: disable=inconsistent-return-statements
+            if cls:
+                return cls(pipeline_response, None, {})
+
+        if polling is True:
+            polling_method: AsyncPollingMethod = cast(
+                AsyncPollingMethod, AsyncARMPolling(lro_delay, lro_options={"final-state-via": "location"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(AsyncPollingMethod, AsyncNoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_delete.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/models/{modelName}"
+    }
+
+    @distributed_trace_async
+    async def get(
+        self, resource_group_name: str, registry_name: str, model_name: str, **kwargs: Any
+    ) -> _models.ModelContainer:
+        """Get container.
+
+        Get container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param model_name: Container name. This is case-sensitive. Required.
+        :type model_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ModelContainer or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ModelContainer
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.ModelContainer] = kwargs.pop("cls", None)
+
+        request = build_get_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            model_name=model_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self.get.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize("ModelContainer", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/models/{modelName}"
+    }
+
+    async def _create_or_update_initial(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        model_name: str,
+        body: Union[_models.ModelContainer, IO],
+        **kwargs: Any
+    ) -> _models.ModelContainer:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.ModelContainer] = kwargs.pop("cls", None)
+
+        content_type = content_type or "application/json"
+        _json = None
+        _content = None
+        if isinstance(body, (IO, bytes)):
+            _content = body
+        else:
+            _json = self._serialize.body(body, "ModelContainer")
+
+        request = build_create_or_update_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            model_name=model_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            content=_content,
+            template_url=self._create_or_update_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize("ModelContainer", pipeline_response)
+
+        if response.status_code == 201:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Azure-AsyncOperation"] = self._deserialize(
+                "str", response.headers.get("Azure-AsyncOperation")
+            )
+
+            deserialized = self._deserialize("ModelContainer", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)  # type: ignore
+
+        return deserialized  # type: ignore
+
+    _create_or_update_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/models/{modelName}"
+    }
+
+    @overload
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        model_name: str,
+        body: _models.ModelContainer,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.ModelContainer]:
+        """Create or update model container.
+
+        Create or update model container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param model_name: Container name. Required.
+        :type model_name: str
+        :param body: Container entity to create or update. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.ModelContainer
+        :keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either ModelContainer or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.ModelContainer]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @overload
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        model_name: str,
+        body: IO,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.ModelContainer]:
+        """Create or update model container.
+
+        Create or update model container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param model_name: Container name. Required.
+        :type model_name: str
+        :param body: Container entity to create or update. Required.
+        :type body: IO
+        :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either ModelContainer or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.ModelContainer]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @distributed_trace_async
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        model_name: str,
+        body: Union[_models.ModelContainer, IO],
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.ModelContainer]:
+        """Create or update model container.
+
+        Create or update model container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param model_name: Container name. Required.
+        :type model_name: str
+        :param body: Container entity to create or update. Is either a model type or a IO type.
+         Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.ModelContainer or IO
+        :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
+         Default value is None.
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either ModelContainer or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.ModelContainer]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.ModelContainer] = kwargs.pop("cls", None)
+        polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = await self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                model_name=model_name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):
+            deserialized = self._deserialize("ModelContainer", pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+        if polling is True:
+            polling_method: AsyncPollingMethod = cast(
+                AsyncPollingMethod,
+                AsyncARMPolling(lro_delay, lro_options={"final-state-via": "original-uri"}, **kwargs),
+            )
+        elif polling is False:
+            polling_method = cast(AsyncPollingMethod, AsyncNoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_create_or_update.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/models/{modelName}"
+    }
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_registry_model_versions_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_registry_model_versions_operations.py
new file mode 100644
index 0000000000000..82f3e418ce524
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_registry_model_versions_operations.py
@@ -0,0 +1,687 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+import sys
+from typing import Any, AsyncIterable, Callable, Dict, IO, Optional, TypeVar, Union, cast, overload
+import urllib.parse
+
+from azure.core.async_paging import AsyncItemPaged, AsyncList
+from azure.core.exceptions import (
+    ClientAuthenticationError,
+    HttpResponseError,
+    ResourceExistsError,
+    ResourceNotFoundError,
+    ResourceNotModifiedError,
+    map_error,
+)
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import AsyncHttpResponse
+from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.tracing.decorator_async import distributed_trace_async
+from azure.core.utils import case_insensitive_dict
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.async_arm_polling import AsyncARMPolling
+
+from ... import models as _models
+from ..._vendor import _convert_request
+from ...operations._registry_model_versions_operations import (
+    build_create_or_update_request,
+    build_delete_request,
+    build_get_request,
+    build_list_request,
+)
+
+if sys.version_info >= (3, 8):
+    from typing import Literal  # pylint: disable=no-name-in-module, ungrouped-imports
+else:
+    from typing_extensions import Literal  # type: ignore  # pylint: disable=ungrouped-imports
+T = TypeVar("T")
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
+
+
+class RegistryModelVersionsOperations:
+    """
+    .. warning::
+        **DO NOT** instantiate this class directly.
+
+        Instead, you should access the following operations through
+        :class:`~azure.mgmt.machinelearningservices.aio.MachineLearningServicesMgmtClient`'s
+        :attr:`registry_model_versions` attribute.
+    """
+
+    models = _models
+
+    def __init__(self, *args, **kwargs) -> None:
+        input_args = list(args)
+        self._client = input_args.pop(0) if input_args else kwargs.pop("client")
+        self._config = input_args.pop(0) if input_args else kwargs.pop("config")
+        self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer")
+        self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer")
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        model_name: str,
+        skip: Optional[str] = None,
+        order_by: Optional[str] = None,
+        top: Optional[int] = None,
+        version: Optional[str] = None,
+        description: Optional[str] = None,
+        tags: Optional[str] = None,
+        properties: Optional[str] = None,
+        list_view_type: Optional[Union[str, _models.ListViewType]] = None,
+        **kwargs: Any
+    ) -> AsyncIterable["_models.ModelVersion"]:
+        """List versions.
+
+        List versions.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param model_name: Container name. This is case-sensitive. Required.
+        :type model_name: str
+        :param skip: Continuation token for pagination. Default value is None.
+        :type skip: str
+        :param order_by: Ordering of list. Default value is None.
+        :type order_by: str
+        :param top: Maximum number of records to return. Default value is None.
+        :type top: int
+        :param version: Version identifier. Default value is None.
+        :type version: str
+        :param description: Model description. Default value is None.
+        :type description: str
+        :param tags: Comma-separated list of tag names (and optionally values). Example:
+         tag1,tag2=value2. Default value is None.
+        :type tags: str
+        :param properties: Comma-separated list of property names (and optionally values). Example:
+         prop1,prop2=value2. Default value is None.
+        :type properties: str
+        :param list_view_type: View type for including/excluding (for example) archived entities. Known
+         values are: "ActiveOnly", "ArchivedOnly", and "All". Default value is None.
+        :type list_view_type: str or ~azure.mgmt.machinelearningservices.models.ListViewType
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either ModelVersion or the result of cls(response)
+        :rtype:
+         ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.machinelearningservices.models.ModelVersion]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.ModelVersionResourceArmPaginatedResult] = kwargs.pop("cls", None)
+
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        def prepare_request(next_link=None):
+            if not next_link:
+
+                request = build_list_request(
+                    resource_group_name=resource_group_name,
+                    registry_name=registry_name,
+                    model_name=model_name,
+                    subscription_id=self._config.subscription_id,
+                    skip=skip,
+                    order_by=order_by,
+                    top=top,
+                    version=version,
+                    description=description,
+                    tags=tags,
+                    properties=properties,
+                    list_view_type=list_view_type,
+                    api_version=api_version,
+                    template_url=self.list.metadata["url"],
+                    headers=_headers,
+                    params=_params,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                # make call to next link with the client's api-version
+                _parsed_next_link = urllib.parse.urlparse(next_link)
+                _next_request_params = case_insensitive_dict(
+                    {
+                        key: [urllib.parse.quote(v) for v in value]
+                        for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items()
+                    }
+                )
+                _next_request_params["api-version"] = self._config.api_version
+                request = HttpRequest(
+                    "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        async def extract_data(pipeline_response):
+            deserialized = self._deserialize("ModelVersionResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)  # type: ignore
+            return deserialized.next_link or None, AsyncList(list_of_elem)
+
+        async def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+                request, stream=False, **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+        return AsyncItemPaged(get_next, extract_data)
+
+    list.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/models/{modelName}/versions"
+    }
+
+    async def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self, resource_group_name: str, registry_name: str, model_name: str, version: str, **kwargs: Any
+    ) -> None:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+
+        request = build_delete_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            model_name=model_name,
+            version=version,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self._delete_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Location"] = self._deserialize("str", response.headers.get("Location"))
+            response_headers["Retry-After"] = self._deserialize("int", response.headers.get("Retry-After"))
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _delete_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/models/{modelName}/versions/{version}"
+    }
+
+    @distributed_trace_async
+    async def begin_delete(
+        self, resource_group_name: str, registry_name: str, model_name: str, version: str, **kwargs: Any
+    ) -> AsyncLROPoller[None]:
+        """Delete version.
+
+        Delete version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param model_name: Container name. Required.
+        :type model_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.AsyncLROPoller[None]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+        polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = await self._delete_initial(  # type: ignore
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                model_name=model_name,
+                version=version,
+                api_version=api_version,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):  # pylint: disable=inconsistent-return-statements
+            if cls:
+                return cls(pipeline_response, None, {})
+
+        if polling is True:
+            polling_method: AsyncPollingMethod = cast(
+                AsyncPollingMethod, AsyncARMPolling(lro_delay, lro_options={"final-state-via": "location"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(AsyncPollingMethod, AsyncNoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_delete.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/models/{modelName}/versions/{version}"
+    }
+
+    @distributed_trace_async
+    async def get(
+        self, resource_group_name: str, registry_name: str, model_name: str, version: str, **kwargs: Any
+    ) -> _models.ModelVersion:
+        """Get version.
+
+        Get version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param model_name: Container name. This is case-sensitive. Required.
+        :type model_name: str
+        :param version: Version identifier. This is case-sensitive. Required.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ModelVersion or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ModelVersion
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.ModelVersion] = kwargs.pop("cls", None)
+
+        request = build_get_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            model_name=model_name,
+            version=version,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self.get.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize("ModelVersion", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/models/{modelName}/versions/{version}"
+    }
+
+    async def _create_or_update_initial(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        model_name: str,
+        version: str,
+        body: Union[_models.ModelVersion, IO],
+        **kwargs: Any
+    ) -> _models.ModelVersion:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.ModelVersion] = kwargs.pop("cls", None)
+
+        content_type = content_type or "application/json"
+        _json = None
+        _content = None
+        if isinstance(body, (IO, bytes)):
+            _content = body
+        else:
+            _json = self._serialize.body(body, "ModelVersion")
+
+        request = build_create_or_update_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            model_name=model_name,
+            version=version,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            content=_content,
+            template_url=self._create_or_update_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize("ModelVersion", pipeline_response)
+
+        if response.status_code == 201:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Azure-AsyncOperation"] = self._deserialize(
+                "str", response.headers.get("Azure-AsyncOperation")
+            )
+
+            deserialized = self._deserialize("ModelVersion", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)  # type: ignore
+
+        return deserialized  # type: ignore
+
+    _create_or_update_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/models/{modelName}/versions/{version}"
+    }
+
+    @overload
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        model_name: str,
+        version: str,
+        body: _models.ModelVersion,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.ModelVersion]:
+        """Create or update version.
+
+        Create or update version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param model_name: Container name. Required.
+        :type model_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :param body: Version entity to create or update. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.ModelVersion
+        :keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either ModelVersion or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.ModelVersion]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @overload
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        model_name: str,
+        version: str,
+        body: IO,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.ModelVersion]:
+        """Create or update version.
+
+        Create or update version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param model_name: Container name. Required.
+        :type model_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :param body: Version entity to create or update. Required.
+        :type body: IO
+        :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either ModelVersion or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.ModelVersion]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @distributed_trace_async
+    async def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        model_name: str,
+        version: str,
+        body: Union[_models.ModelVersion, IO],
+        **kwargs: Any
+    ) -> AsyncLROPoller[_models.ModelVersion]:
+        """Create or update version.
+
+        Create or update version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param model_name: Container name. Required.
+        :type model_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :param body: Version entity to create or update. Is either a model type or a IO type. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.ModelVersion or IO
+        :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
+         Default value is None.
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for
+         this operation to not poll, or pass in your own initialized polling object for a personal
+         polling strategy.
+        :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of AsyncLROPoller that returns either ModelVersion or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.AsyncLROPoller[~azure.mgmt.machinelearningservices.models.ModelVersion]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.ModelVersion] = kwargs.pop("cls", None)
+        polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = await self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                model_name=model_name,
+                version=version,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):
+            deserialized = self._deserialize("ModelVersion", pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+        if polling is True:
+            polling_method: AsyncPollingMethod = cast(
+                AsyncPollingMethod,
+                AsyncARMPolling(lro_delay, lro_options={"final-state-via": "original-uri"}, **kwargs),
+            )
+        elif polling is False:
+            polling_method = cast(AsyncPollingMethod, AsyncNoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return AsyncLROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_create_or_update.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/models/{modelName}/versions/{version}"
+    }
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_schedules_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_schedules_operations.py
index 7e2d8fa6ae159..21f7e63629b6a 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_schedules_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_schedules_operations.py
@@ -97,7 +97,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ScheduleResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -187,7 +187,7 @@ async def _delete_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -261,7 +261,7 @@ async def begin_delete(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -333,7 +333,7 @@ async def get(self, resource_group_name: str, workspace_name: str, name: str, **
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.Schedule] = kwargs.pop("cls", None)
@@ -387,7 +387,7 @@ async def _create_or_update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -569,7 +569,7 @@ async def begin_create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_usages_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_usages_operations.py
index f7db5ff755cff..1288b3d60947c 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_usages_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_usages_operations.py
@@ -73,7 +73,7 @@ def list(self, location: str, **kwargs: Any) -> AsyncIterable["_models.Usage"]:
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ListUsagesResult] = kwargs.pop("cls", None)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_virtual_machine_sizes_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_virtual_machine_sizes_operations.py
index e119495994266..d429c9356746f 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_virtual_machine_sizes_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_virtual_machine_sizes_operations.py
@@ -77,7 +77,7 @@ async def list(self, location: str, **kwargs: Any) -> _models.VirtualMachineSize
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.VirtualMachineSizeListResult] = kwargs.pop("cls", None)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_workspace_connections_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_workspace_connections_operations.py
index d849184954e4c..b86e2669d77f3 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_workspace_connections_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_workspace_connections_operations.py
@@ -167,7 +167,7 @@ async def create(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -248,7 +248,7 @@ async def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.WorkspaceConnectionPropertiesV2BasicResource] = kwargs.pop("cls", None)
@@ -317,7 +317,7 @@ async def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -383,7 +383,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.WorkspaceConnectionPropertiesV2BasicResourceArmPaginatedResult] = kwargs.pop("cls", None)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_workspace_features_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_workspace_features_operations.py
index a968302c5446f..d009b29a14a4c 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_workspace_features_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_workspace_features_operations.py
@@ -77,7 +77,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ListAmlUserFeatureResult] = kwargs.pop("cls", None)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_workspaces_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_workspaces_operations.py
index 5c6cf54fea4d7..0e124bd814820 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_workspaces_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/aio/operations/_workspaces_operations.py
@@ -100,7 +100,7 @@ async def get(self, resource_group_name: str, workspace_name: str, **kwargs: Any
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.Workspace] = kwargs.pop("cls", None)
@@ -153,7 +153,7 @@ async def _create_or_update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -316,7 +316,7 @@ async def begin_create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -377,7 +377,7 @@ async def _delete_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -436,7 +436,7 @@ async def begin_delete(self, resource_group_name: str, workspace_name: str, **kw
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -496,7 +496,7 @@ async def _update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -661,7 +661,7 @@ async def begin_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -728,7 +728,7 @@ def list_by_resource_group(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.WorkspaceListResult] = kwargs.pop("cls", None)
@@ -820,7 +820,7 @@ async def _diagnose_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -999,7 +999,7 @@ async def begin_diagnose(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1076,7 +1076,7 @@ async def list_keys(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ListWorkspaceKeysResult] = kwargs.pop("cls", None)
@@ -1129,7 +1129,7 @@ async def _resync_keys_initial(  # pylint: disable=inconsistent-return-statement
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -1191,7 +1191,7 @@ async def begin_resync_keys(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -1248,7 +1248,7 @@ def list_by_subscription(self, skip: Optional[str] = None, **kwargs: Any) -> Asy
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.WorkspaceListResult] = kwargs.pop("cls", None)
@@ -1348,7 +1348,7 @@ async def list_notebook_access_token(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.NotebookAccessTokenResult] = kwargs.pop("cls", None)
@@ -1401,7 +1401,7 @@ async def _prepare_notebook_initial(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[Optional[_models.NotebookResourceInfo]] = kwargs.pop("cls", None)
@@ -1470,7 +1470,7 @@ async def begin_prepare_notebook(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.NotebookResourceInfo] = kwargs.pop("cls", None)
@@ -1543,7 +1543,7 @@ async def list_storage_account_keys(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ListStorageAccountKeysResult] = kwargs.pop("cls", None)
@@ -1609,7 +1609,7 @@ async def list_notebook_keys(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ListNotebookKeysResult] = kwargs.pop("cls", None)
@@ -1679,7 +1679,7 @@ async def list_outbound_network_dependencies_endpoints(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ExternalFQDNResponse] = kwargs.pop("cls", None)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/models/__init__.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/models/__init__.py
index 72bfcc9bc3c22..6719480a648cd 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/models/__init__.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/models/__init__.py
@@ -9,11 +9,14 @@
 from ._models_py3 import AKS
 from ._models_py3 import AKSSchema
 from ._models_py3 import AKSSchemaProperties
+from ._models_py3 import AccessKeyAuthTypeWorkspaceConnectionProperties
 from ._models_py3 import AccountKeyDatastoreCredentials
 from ._models_py3 import AccountKeyDatastoreSecrets
+from ._models_py3 import AcrDetails
 from ._models_py3 import AksComputeSecrets
 from ._models_py3 import AksComputeSecretsProperties
 from ._models_py3 import AksNetworkingConfiguration
+from ._models_py3 import AllNodes
 from ._models_py3 import AmlCompute
 from ._models_py3 import AmlComputeNodeInformation
 from ._models_py3 import AmlComputeNodesInformation
@@ -24,6 +27,7 @@
 from ._models_py3 import AmlOperationListResult
 from ._models_py3 import AmlToken
 from ._models_py3 import AmlUserFeature
+from ._models_py3 import ArmResourceId
 from ._models_py3 import AssetBase
 from ._models_py3 import AssetContainer
 from ._models_py3 import AssetJobInput
@@ -39,9 +43,11 @@
 from ._models_py3 import AutoSeasonality
 from ._models_py3 import AutoTargetLags
 from ._models_py3 import AutoTargetRollingWindowSize
+from ._models_py3 import AutologgerSettings
 from ._models_py3 import AzureBlobDatastore
 from ._models_py3 import AzureDataLakeGen1Datastore
 from ._models_py3 import AzureDataLakeGen2Datastore
+from ._models_py3 import AzureDatastore
 from ._models_py3 import AzureFileDatastore
 from ._models_py3 import BanditPolicy
 from ._models_py3 import BatchDeployment
@@ -53,12 +59,14 @@
 from ._models_py3 import BatchEndpointTrackedResourceArmPaginatedResult
 from ._models_py3 import BatchRetrySettings
 from ._models_py3 import BayesianSamplingAlgorithm
+from ._models_py3 import BindOptions
 from ._models_py3 import BuildContext
 from ._models_py3 import CertificateDatastoreCredentials
 from ._models_py3 import CertificateDatastoreSecrets
 from ._models_py3 import Classification
 from ._models_py3 import ClassificationTrainingSettings
 from ._models_py3 import ClusterUpdateParameters
+from ._models_py3 import CocoExportSummary
 from ._models_py3 import CodeConfiguration
 from ._models_py3 import CodeContainer
 from ._models_py3 import CodeContainerProperties
@@ -78,6 +86,7 @@
 from ._models_py3 import Compute
 from ._models_py3 import ComputeInstance
 from ._models_py3 import ComputeInstanceApplication
+from ._models_py3 import ComputeInstanceAutologgerSettings
 from ._models_py3 import ComputeInstanceConnectivityEndpoints
 from ._models_py3 import ComputeInstanceContainer
 from ._models_py3 import ComputeInstanceCreatedBy
@@ -98,11 +107,13 @@
 from ._models_py3 import ContainerResourceSettings
 from ._models_py3 import CosmosDbSettings
 from ._models_py3 import CronTrigger
+from ._models_py3 import CsvExportSummary
 from ._models_py3 import CustomForecastHorizon
 from ._models_py3 import CustomModelJobInput
 from ._models_py3 import CustomModelJobOutput
 from ._models_py3 import CustomNCrossValidations
 from ._models_py3 import CustomSeasonality
+from ._models_py3 import CustomService
 from ._models_py3 import CustomTargetLags
 from ._models_py3 import CustomTargetRollingWindowSize
 from ._models_py3 import DataContainer
@@ -121,6 +132,7 @@
 from ._models_py3 import DatabricksComputeSecretsProperties
 from ._models_py3 import DatabricksProperties
 from ._models_py3 import DatabricksSchema
+from ._models_py3 import DatasetExportSummary
 from ._models_py3 import Datastore
 from ._models_py3 import DatastoreCredentials
 from ._models_py3 import DatastoreProperties
@@ -136,9 +148,13 @@
 from ._models_py3 import DiagnoseResult
 from ._models_py3 import DiagnoseWorkspaceParameters
 from ._models_py3 import DistributionConfiguration
+from ._models_py3 import Docker
 from ._models_py3 import EarlyTerminationPolicy
 from ._models_py3 import EncryptionKeyVaultProperties
+from ._models_py3 import EncryptionKeyVaultUpdateProperties
 from ._models_py3 import EncryptionProperty
+from ._models_py3 import EncryptionUpdateProperties
+from ._models_py3 import Endpoint
 from ._models_py3 import EndpointAuthKeys
 from ._models_py3 import EndpointAuthToken
 from ._models_py3 import EndpointDeploymentPropertiesBase
@@ -147,6 +163,7 @@
 from ._models_py3 import EnvironmentContainer
 from ._models_py3 import EnvironmentContainerProperties
 from ._models_py3 import EnvironmentContainerResourceArmPaginatedResult
+from ._models_py3 import EnvironmentVariable
 from ._models_py3 import EnvironmentVersion
 from ._models_py3 import EnvironmentVersionProperties
 from ._models_py3 import EnvironmentVersionResourceArmPaginatedResult
@@ -155,6 +172,7 @@
 from ._models_py3 import ErrorResponse
 from ._models_py3 import EstimatedVMPrice
 from ._models_py3 import EstimatedVMPrices
+from ._models_py3 import ExportSummary
 from ._models_py3 import ExternalFQDNResponse
 from ._models_py3 import FQDNEndpoint
 from ._models_py3 import FQDNEndpointDetail
@@ -170,14 +188,18 @@
 from ._models_py3 import HDInsight
 from ._models_py3 import HDInsightProperties
 from ._models_py3 import HDInsightSchema
+from ._models_py3 import HdfsDatastore
 from ._models_py3 import IdAssetReference
 from ._models_py3 import IdentityConfiguration
 from ._models_py3 import IdentityForCmk
+from ._models_py3 import IdleShutdownSetting
+from ._models_py3 import Image
 from ._models_py3 import ImageClassification
 from ._models_py3 import ImageClassificationBase
 from ._models_py3 import ImageClassificationMultilabel
 from ._models_py3 import ImageInstanceSegmentation
 from ._models_py3 import ImageLimitSettings
+from ._models_py3 import ImageMetadata
 from ._models_py3 import ImageModelDistributionSettings
 from ._models_py3 import ImageModelDistributionSettingsClassification
 from ._models_py3 import ImageModelDistributionSettingsObjectDetection
@@ -200,10 +222,25 @@
 from ._models_py3 import JobResourceConfiguration
 from ._models_py3 import JobScheduleAction
 from ._models_py3 import JobService
+from ._models_py3 import KerberosCredentials
+from ._models_py3 import KerberosKeytabCredentials
+from ._models_py3 import KerberosKeytabSecrets
+from ._models_py3 import KerberosPasswordCredentials
+from ._models_py3 import KerberosPasswordSecrets
 from ._models_py3 import Kubernetes
 from ._models_py3 import KubernetesOnlineDeployment
 from ._models_py3 import KubernetesProperties
 from ._models_py3 import KubernetesSchema
+from ._models_py3 import LabelCategory
+from ._models_py3 import LabelClass
+from ._models_py3 import LabelingDataConfiguration
+from ._models_py3 import LabelingJob
+from ._models_py3 import LabelingJobImageProperties
+from ._models_py3 import LabelingJobInstructions
+from ._models_py3 import LabelingJobMediaProperties
+from ._models_py3 import LabelingJobProperties
+from ._models_py3 import LabelingJobResourceArmPaginatedResult
+from ._models_py3 import LabelingJobTextProperties
 from ._models_py3 import ListAmlUserFeatureResult
 from ._models_py3 import ListNotebookKeysResult
 from ._models_py3 import ListStorageAccountKeysResult
@@ -211,6 +248,9 @@
 from ._models_py3 import ListWorkspaceKeysResult
 from ._models_py3 import ListWorkspaceQuotas
 from ._models_py3 import LiteralJobInput
+from ._models_py3 import MLAssistConfiguration
+from ._models_py3 import MLAssistConfigurationDisabled
+from ._models_py3 import MLAssistConfigurationEnabled
 from ._models_py3 import MLFlowModelJobInput
 from ._models_py3 import MLFlowModelJobOutput
 from ._models_py3 import MLTableData
@@ -229,10 +269,14 @@
 from ._models_py3 import ModelVersionResourceArmPaginatedResult
 from ._models_py3 import Mpi
 from ._models_py3 import NCrossValidations
+from ._models_py3 import NlpFixedParameters
+from ._models_py3 import NlpParameterSubspace
+from ._models_py3 import NlpSweepSettings
 from ._models_py3 import NlpVertical
 from ._models_py3 import NlpVerticalFeaturizationSettings
 from ._models_py3 import NlpVerticalLimitSettings
 from ._models_py3 import NodeStateCounts
+from ._models_py3 import Nodes
 from ._models_py3 import NoneAuthTypeWorkspaceConnectionProperties
 from ._models_py3 import NoneDatastoreCredentials
 from ._models_py3 import NotebookAccessTokenResult
@@ -256,6 +300,7 @@
 from ._models_py3 import PartialMinimalTrackedResource
 from ._models_py3 import PartialMinimalTrackedResourceWithIdentity
 from ._models_py3 import PartialMinimalTrackedResourceWithSku
+from ._models_py3 import PartialRegistryPartialTrackedResource
 from ._models_py3 import PartialSku
 from ._models_py3 import Password
 from ._models_py3 import PersonalComputeInstanceSettings
@@ -267,6 +312,7 @@
 from ._models_py3 import PrivateLinkResourceListResult
 from ._models_py3 import PrivateLinkServiceConnectionState
 from ._models_py3 import ProbeSettings
+from ._models_py3 import ProgressMetrics
 from ._models_py3 import PyTorch
 from ._models_py3 import QuotaBaseProperties
 from ._models_py3 import QuotaUpdateParameters
@@ -274,7 +320,11 @@
 from ._models_py3 import RecurrenceSchedule
 from ._models_py3 import RecurrenceTrigger
 from ._models_py3 import RegenerateEndpointKeysRequest
+from ._models_py3 import Registry
 from ._models_py3 import RegistryListCredentialsResult
+from ._models_py3 import RegistryProperties
+from ._models_py3 import RegistryRegionArmDetails
+from ._models_py3 import RegistryTrackedResourceArmPaginatedResult
 from ._models_py3 import Regression
 from ._models_py3 import RegressionTrainingSettings
 from ._models_py3 import Resource
@@ -299,6 +349,7 @@
 from ._models_py3 import ScriptsToExecute
 from ._models_py3 import Seasonality
 from ._models_py3 import ServiceManagedResourcesSettings
+from ._models_py3 import ServicePrincipalAuthTypeWorkspaceConnectionProperties
 from ._models_py3 import ServicePrincipalDatastoreCredentials
 from ._models_py3 import ServicePrincipalDatastoreSecrets
 from ._models_py3 import SetupScripts
@@ -308,14 +359,26 @@
 from ._models_py3 import SkuResource
 from ._models_py3 import SkuResourceArmPaginatedResult
 from ._models_py3 import SkuSetting
+from ._models_py3 import SparkJob
+from ._models_py3 import SparkJobEntry
+from ._models_py3 import SparkJobPythonEntry
+from ._models_py3 import SparkJobScalaEntry
+from ._models_py3 import SparkResourceConfiguration
 from ._models_py3 import SslConfiguration
 from ._models_py3 import StackEnsembleSettings
+from ._models_py3 import StatusMessage
+from ._models_py3 import StorageAccountDetails
 from ._models_py3 import SweepJob
 from ._models_py3 import SweepJobLimits
 from ._models_py3 import SynapseSpark
 from ._models_py3 import SynapseSparkProperties
+from ._models_py3 import SystemCreatedAcrAccount
+from ._models_py3 import SystemCreatedStorageAccount
 from ._models_py3 import SystemData
 from ._models_py3 import SystemService
+from ._models_py3 import TableFixedParameters
+from ._models_py3 import TableParameterSubspace
+from ._models_py3 import TableSweepSettings
 from ._models_py3 import TableVertical
 from ._models_py3 import TableVerticalFeaturizationSettings
 from ._models_py3 import TableVerticalLimitSettings
@@ -326,6 +389,7 @@
 from ._models_py3 import TextClassification
 from ._models_py3 import TextClassificationMultilabel
 from ._models_py3 import TextNer
+from ._models_py3 import TmpfsOptions
 from ._models_py3 import TrackedResource
 from ._models_py3 import TrainingSettings
 from ._models_py3 import TrialComponent
@@ -345,6 +409,8 @@
 from ._models_py3 import UsageName
 from ._models_py3 import UserAccountCredentials
 from ._models_py3 import UserAssignedIdentity
+from ._models_py3 import UserCreatedAcrAccount
+from ._models_py3 import UserCreatedStorageAccount
 from ._models_py3 import UserIdentity
 from ._models_py3 import UsernamePasswordAuthTypeWorkspaceConnectionProperties
 from ._models_py3 import VirtualMachine
@@ -356,12 +422,16 @@
 from ._models_py3 import VirtualMachineSize
 from ._models_py3 import VirtualMachineSizeListResult
 from ._models_py3 import VirtualMachineSshCredentials
+from ._models_py3 import VolumeDefinition
+from ._models_py3 import VolumeOptions
 from ._models_py3 import Workspace
+from ._models_py3 import WorkspaceConnectionAccessKey
 from ._models_py3 import WorkspaceConnectionManagedIdentity
 from ._models_py3 import WorkspaceConnectionPersonalAccessToken
 from ._models_py3 import WorkspaceConnectionPropertiesV2
 from ._models_py3 import WorkspaceConnectionPropertiesV2BasicResource
 from ._models_py3 import WorkspaceConnectionPropertiesV2BasicResourceArmPaginatedResult
+from ._models_py3 import WorkspaceConnectionServicePrincipal
 from ._models_py3 import WorkspaceConnectionSharedAccessSignature
 from ._models_py3 import WorkspaceConnectionUsernamePassword
 from ._models_py3 import WorkspaceListResult
@@ -369,6 +439,7 @@
 
 from ._machine_learning_services_mgmt_client_enums import AllocationState
 from ._machine_learning_services_mgmt_client_enums import ApplicationSharingPolicy
+from ._machine_learning_services_mgmt_client_enums import AssetProvisioningState
 from ._machine_learning_services_mgmt_client_enums import AutoRebuildSetting
 from ._machine_learning_services_mgmt_client_enums import Autosave
 from ._machine_learning_services_mgmt_client_enums import BatchLoggingLevel
@@ -401,6 +472,8 @@
 from ._machine_learning_services_mgmt_client_enums import EndpointComputeType
 from ._machine_learning_services_mgmt_client_enums import EndpointProvisioningState
 from ._machine_learning_services_mgmt_client_enums import EnvironmentType
+from ._machine_learning_services_mgmt_client_enums import EnvironmentVariableType
+from ._machine_learning_services_mgmt_client_enums import ExportFormatType
 from ._machine_learning_services_mgmt_client_enums import FeatureLags
 from ._machine_learning_services_mgmt_client_enums import FeaturizationMode
 from ._machine_learning_services_mgmt_client_enums import ForecastHorizonMode
@@ -408,11 +481,15 @@
 from ._machine_learning_services_mgmt_client_enums import ForecastingPrimaryMetrics
 from ._machine_learning_services_mgmt_client_enums import Goal
 from ._machine_learning_services_mgmt_client_enums import IdentityConfigurationType
+from ._machine_learning_services_mgmt_client_enums import ImageAnnotationType
+from ._machine_learning_services_mgmt_client_enums import ImageType
+from ._machine_learning_services_mgmt_client_enums import IncrementalDataRefresh
 from ._machine_learning_services_mgmt_client_enums import InputDeliveryMode
 from ._machine_learning_services_mgmt_client_enums import InstanceSegmentationPrimaryMetrics
 from ._machine_learning_services_mgmt_client_enums import JobInputType
 from ._machine_learning_services_mgmt_client_enums import JobLimitsType
 from ._machine_learning_services_mgmt_client_enums import JobOutputType
+from ._machine_learning_services_mgmt_client_enums import JobProvisioningState
 from ._machine_learning_services_mgmt_client_enums import JobStatus
 from ._machine_learning_services_mgmt_client_enums import JobType
 from ._machine_learning_services_mgmt_client_enums import KeyType
@@ -420,13 +497,20 @@
 from ._machine_learning_services_mgmt_client_enums import ListViewType
 from ._machine_learning_services_mgmt_client_enums import LoadBalancerType
 from ._machine_learning_services_mgmt_client_enums import LogVerbosity
+from ._machine_learning_services_mgmt_client_enums import MLAssistConfigurationType
+from ._machine_learning_services_mgmt_client_enums import MLFlowAutologgerState
 from ._machine_learning_services_mgmt_client_enums import ManagedServiceIdentityType
+from ._machine_learning_services_mgmt_client_enums import MediaType
+from ._machine_learning_services_mgmt_client_enums import MlflowAutologger
 from ._machine_learning_services_mgmt_client_enums import ModelSize
 from ._machine_learning_services_mgmt_client_enums import MountAction
 from ._machine_learning_services_mgmt_client_enums import MountState
+from ._machine_learning_services_mgmt_client_enums import MultiSelect
 from ._machine_learning_services_mgmt_client_enums import NCrossValidationsMode
 from ._machine_learning_services_mgmt_client_enums import Network
+from ._machine_learning_services_mgmt_client_enums import NlpLearningRateScheduler
 from ._machine_learning_services_mgmt_client_enums import NodeState
+from ._machine_learning_services_mgmt_client_enums import NodesValueType
 from ._machine_learning_services_mgmt_client_enums import ObjectDetectionPrimaryMetrics
 from ._machine_learning_services_mgmt_client_enums import OperatingSystemType
 from ._machine_learning_services_mgmt_client_enums import OperationName
@@ -437,6 +521,7 @@
 from ._machine_learning_services_mgmt_client_enums import OutputDeliveryMode
 from ._machine_learning_services_mgmt_client_enums import PrivateEndpointConnectionProvisioningState
 from ._machine_learning_services_mgmt_client_enums import PrivateEndpointServiceConnectionStatus
+from ._machine_learning_services_mgmt_client_enums import Protocol
 from ._machine_learning_services_mgmt_client_enums import ProvisioningState
 from ._machine_learning_services_mgmt_client_enums import ProvisioningStatus
 from ._machine_learning_services_mgmt_client_enums import PublicNetworkAccess
@@ -462,16 +547,20 @@
 from ._machine_learning_services_mgmt_client_enums import SkuScaleType
 from ._machine_learning_services_mgmt_client_enums import SkuTier
 from ._machine_learning_services_mgmt_client_enums import SourceType
+from ._machine_learning_services_mgmt_client_enums import SparkJobEntryType
 from ._machine_learning_services_mgmt_client_enums import SshPublicAccess
 from ._machine_learning_services_mgmt_client_enums import SslConfigStatus
 from ._machine_learning_services_mgmt_client_enums import StackMetaLearnerType
 from ._machine_learning_services_mgmt_client_enums import Status
+from ._machine_learning_services_mgmt_client_enums import StatusMessageLevel
 from ._machine_learning_services_mgmt_client_enums import StochasticOptimizer
 from ._machine_learning_services_mgmt_client_enums import StorageAccountType
 from ._machine_learning_services_mgmt_client_enums import TargetAggregationFunction
 from ._machine_learning_services_mgmt_client_enums import TargetLagsMode
 from ._machine_learning_services_mgmt_client_enums import TargetRollingWindowSizeMode
 from ._machine_learning_services_mgmt_client_enums import TaskType
+from ._machine_learning_services_mgmt_client_enums import TextAnnotationType
+from ._machine_learning_services_mgmt_client_enums import TrainingMode
 from ._machine_learning_services_mgmt_client_enums import TriggerType
 from ._machine_learning_services_mgmt_client_enums import UnderlyingResourceAction
 from ._machine_learning_services_mgmt_client_enums import UnitOfMeasure
@@ -482,6 +571,7 @@
 from ._machine_learning_services_mgmt_client_enums import ValidationMetricType
 from ._machine_learning_services_mgmt_client_enums import ValueFormat
 from ._machine_learning_services_mgmt_client_enums import VmPriority
+from ._machine_learning_services_mgmt_client_enums import VolumeDefinitionType
 from ._machine_learning_services_mgmt_client_enums import WeekDay
 from ._patch import __all__ as _patch_all
 from ._patch import *  # pylint: disable=unused-wildcard-import
@@ -491,11 +581,14 @@
     "AKS",
     "AKSSchema",
     "AKSSchemaProperties",
+    "AccessKeyAuthTypeWorkspaceConnectionProperties",
     "AccountKeyDatastoreCredentials",
     "AccountKeyDatastoreSecrets",
+    "AcrDetails",
     "AksComputeSecrets",
     "AksComputeSecretsProperties",
     "AksNetworkingConfiguration",
+    "AllNodes",
     "AmlCompute",
     "AmlComputeNodeInformation",
     "AmlComputeNodesInformation",
@@ -506,6 +599,7 @@
     "AmlOperationListResult",
     "AmlToken",
     "AmlUserFeature",
+    "ArmResourceId",
     "AssetBase",
     "AssetContainer",
     "AssetJobInput",
@@ -521,9 +615,11 @@
     "AutoSeasonality",
     "AutoTargetLags",
     "AutoTargetRollingWindowSize",
+    "AutologgerSettings",
     "AzureBlobDatastore",
     "AzureDataLakeGen1Datastore",
     "AzureDataLakeGen2Datastore",
+    "AzureDatastore",
     "AzureFileDatastore",
     "BanditPolicy",
     "BatchDeployment",
@@ -535,12 +631,14 @@
     "BatchEndpointTrackedResourceArmPaginatedResult",
     "BatchRetrySettings",
     "BayesianSamplingAlgorithm",
+    "BindOptions",
     "BuildContext",
     "CertificateDatastoreCredentials",
     "CertificateDatastoreSecrets",
     "Classification",
     "ClassificationTrainingSettings",
     "ClusterUpdateParameters",
+    "CocoExportSummary",
     "CodeConfiguration",
     "CodeContainer",
     "CodeContainerProperties",
@@ -560,6 +658,7 @@
     "Compute",
     "ComputeInstance",
     "ComputeInstanceApplication",
+    "ComputeInstanceAutologgerSettings",
     "ComputeInstanceConnectivityEndpoints",
     "ComputeInstanceContainer",
     "ComputeInstanceCreatedBy",
@@ -580,11 +679,13 @@
     "ContainerResourceSettings",
     "CosmosDbSettings",
     "CronTrigger",
+    "CsvExportSummary",
     "CustomForecastHorizon",
     "CustomModelJobInput",
     "CustomModelJobOutput",
     "CustomNCrossValidations",
     "CustomSeasonality",
+    "CustomService",
     "CustomTargetLags",
     "CustomTargetRollingWindowSize",
     "DataContainer",
@@ -603,6 +704,7 @@
     "DatabricksComputeSecretsProperties",
     "DatabricksProperties",
     "DatabricksSchema",
+    "DatasetExportSummary",
     "Datastore",
     "DatastoreCredentials",
     "DatastoreProperties",
@@ -618,9 +720,13 @@
     "DiagnoseResult",
     "DiagnoseWorkspaceParameters",
     "DistributionConfiguration",
+    "Docker",
     "EarlyTerminationPolicy",
     "EncryptionKeyVaultProperties",
+    "EncryptionKeyVaultUpdateProperties",
     "EncryptionProperty",
+    "EncryptionUpdateProperties",
+    "Endpoint",
     "EndpointAuthKeys",
     "EndpointAuthToken",
     "EndpointDeploymentPropertiesBase",
@@ -629,6 +735,7 @@
     "EnvironmentContainer",
     "EnvironmentContainerProperties",
     "EnvironmentContainerResourceArmPaginatedResult",
+    "EnvironmentVariable",
     "EnvironmentVersion",
     "EnvironmentVersionProperties",
     "EnvironmentVersionResourceArmPaginatedResult",
@@ -637,6 +744,7 @@
     "ErrorResponse",
     "EstimatedVMPrice",
     "EstimatedVMPrices",
+    "ExportSummary",
     "ExternalFQDNResponse",
     "FQDNEndpoint",
     "FQDNEndpointDetail",
@@ -652,14 +760,18 @@
     "HDInsight",
     "HDInsightProperties",
     "HDInsightSchema",
+    "HdfsDatastore",
     "IdAssetReference",
     "IdentityConfiguration",
     "IdentityForCmk",
+    "IdleShutdownSetting",
+    "Image",
     "ImageClassification",
     "ImageClassificationBase",
     "ImageClassificationMultilabel",
     "ImageInstanceSegmentation",
     "ImageLimitSettings",
+    "ImageMetadata",
     "ImageModelDistributionSettings",
     "ImageModelDistributionSettingsClassification",
     "ImageModelDistributionSettingsObjectDetection",
@@ -682,10 +794,25 @@
     "JobResourceConfiguration",
     "JobScheduleAction",
     "JobService",
+    "KerberosCredentials",
+    "KerberosKeytabCredentials",
+    "KerberosKeytabSecrets",
+    "KerberosPasswordCredentials",
+    "KerberosPasswordSecrets",
     "Kubernetes",
     "KubernetesOnlineDeployment",
     "KubernetesProperties",
     "KubernetesSchema",
+    "LabelCategory",
+    "LabelClass",
+    "LabelingDataConfiguration",
+    "LabelingJob",
+    "LabelingJobImageProperties",
+    "LabelingJobInstructions",
+    "LabelingJobMediaProperties",
+    "LabelingJobProperties",
+    "LabelingJobResourceArmPaginatedResult",
+    "LabelingJobTextProperties",
     "ListAmlUserFeatureResult",
     "ListNotebookKeysResult",
     "ListStorageAccountKeysResult",
@@ -693,6 +820,9 @@
     "ListWorkspaceKeysResult",
     "ListWorkspaceQuotas",
     "LiteralJobInput",
+    "MLAssistConfiguration",
+    "MLAssistConfigurationDisabled",
+    "MLAssistConfigurationEnabled",
     "MLFlowModelJobInput",
     "MLFlowModelJobOutput",
     "MLTableData",
@@ -711,10 +841,14 @@
     "ModelVersionResourceArmPaginatedResult",
     "Mpi",
     "NCrossValidations",
+    "NlpFixedParameters",
+    "NlpParameterSubspace",
+    "NlpSweepSettings",
     "NlpVertical",
     "NlpVerticalFeaturizationSettings",
     "NlpVerticalLimitSettings",
     "NodeStateCounts",
+    "Nodes",
     "NoneAuthTypeWorkspaceConnectionProperties",
     "NoneDatastoreCredentials",
     "NotebookAccessTokenResult",
@@ -738,6 +872,7 @@
     "PartialMinimalTrackedResource",
     "PartialMinimalTrackedResourceWithIdentity",
     "PartialMinimalTrackedResourceWithSku",
+    "PartialRegistryPartialTrackedResource",
     "PartialSku",
     "Password",
     "PersonalComputeInstanceSettings",
@@ -749,6 +884,7 @@
     "PrivateLinkResourceListResult",
     "PrivateLinkServiceConnectionState",
     "ProbeSettings",
+    "ProgressMetrics",
     "PyTorch",
     "QuotaBaseProperties",
     "QuotaUpdateParameters",
@@ -756,7 +892,11 @@
     "RecurrenceSchedule",
     "RecurrenceTrigger",
     "RegenerateEndpointKeysRequest",
+    "Registry",
     "RegistryListCredentialsResult",
+    "RegistryProperties",
+    "RegistryRegionArmDetails",
+    "RegistryTrackedResourceArmPaginatedResult",
     "Regression",
     "RegressionTrainingSettings",
     "Resource",
@@ -781,6 +921,7 @@
     "ScriptsToExecute",
     "Seasonality",
     "ServiceManagedResourcesSettings",
+    "ServicePrincipalAuthTypeWorkspaceConnectionProperties",
     "ServicePrincipalDatastoreCredentials",
     "ServicePrincipalDatastoreSecrets",
     "SetupScripts",
@@ -790,14 +931,26 @@
     "SkuResource",
     "SkuResourceArmPaginatedResult",
     "SkuSetting",
+    "SparkJob",
+    "SparkJobEntry",
+    "SparkJobPythonEntry",
+    "SparkJobScalaEntry",
+    "SparkResourceConfiguration",
     "SslConfiguration",
     "StackEnsembleSettings",
+    "StatusMessage",
+    "StorageAccountDetails",
     "SweepJob",
     "SweepJobLimits",
     "SynapseSpark",
     "SynapseSparkProperties",
+    "SystemCreatedAcrAccount",
+    "SystemCreatedStorageAccount",
     "SystemData",
     "SystemService",
+    "TableFixedParameters",
+    "TableParameterSubspace",
+    "TableSweepSettings",
     "TableVertical",
     "TableVerticalFeaturizationSettings",
     "TableVerticalLimitSettings",
@@ -808,6 +961,7 @@
     "TextClassification",
     "TextClassificationMultilabel",
     "TextNer",
+    "TmpfsOptions",
     "TrackedResource",
     "TrainingSettings",
     "TrialComponent",
@@ -827,6 +981,8 @@
     "UsageName",
     "UserAccountCredentials",
     "UserAssignedIdentity",
+    "UserCreatedAcrAccount",
+    "UserCreatedStorageAccount",
     "UserIdentity",
     "UsernamePasswordAuthTypeWorkspaceConnectionProperties",
     "VirtualMachine",
@@ -838,18 +994,23 @@
     "VirtualMachineSize",
     "VirtualMachineSizeListResult",
     "VirtualMachineSshCredentials",
+    "VolumeDefinition",
+    "VolumeOptions",
     "Workspace",
+    "WorkspaceConnectionAccessKey",
     "WorkspaceConnectionManagedIdentity",
     "WorkspaceConnectionPersonalAccessToken",
     "WorkspaceConnectionPropertiesV2",
     "WorkspaceConnectionPropertiesV2BasicResource",
     "WorkspaceConnectionPropertiesV2BasicResourceArmPaginatedResult",
+    "WorkspaceConnectionServicePrincipal",
     "WorkspaceConnectionSharedAccessSignature",
     "WorkspaceConnectionUsernamePassword",
     "WorkspaceListResult",
     "WorkspaceUpdateParameters",
     "AllocationState",
     "ApplicationSharingPolicy",
+    "AssetProvisioningState",
     "AutoRebuildSetting",
     "Autosave",
     "BatchLoggingLevel",
@@ -882,6 +1043,8 @@
     "EndpointComputeType",
     "EndpointProvisioningState",
     "EnvironmentType",
+    "EnvironmentVariableType",
+    "ExportFormatType",
     "FeatureLags",
     "FeaturizationMode",
     "ForecastHorizonMode",
@@ -889,11 +1052,15 @@
     "ForecastingPrimaryMetrics",
     "Goal",
     "IdentityConfigurationType",
+    "ImageAnnotationType",
+    "ImageType",
+    "IncrementalDataRefresh",
     "InputDeliveryMode",
     "InstanceSegmentationPrimaryMetrics",
     "JobInputType",
     "JobLimitsType",
     "JobOutputType",
+    "JobProvisioningState",
     "JobStatus",
     "JobType",
     "KeyType",
@@ -901,13 +1068,20 @@
     "ListViewType",
     "LoadBalancerType",
     "LogVerbosity",
+    "MLAssistConfigurationType",
+    "MLFlowAutologgerState",
     "ManagedServiceIdentityType",
+    "MediaType",
+    "MlflowAutologger",
     "ModelSize",
     "MountAction",
     "MountState",
+    "MultiSelect",
     "NCrossValidationsMode",
     "Network",
+    "NlpLearningRateScheduler",
     "NodeState",
+    "NodesValueType",
     "ObjectDetectionPrimaryMetrics",
     "OperatingSystemType",
     "OperationName",
@@ -918,6 +1092,7 @@
     "OutputDeliveryMode",
     "PrivateEndpointConnectionProvisioningState",
     "PrivateEndpointServiceConnectionStatus",
+    "Protocol",
     "ProvisioningState",
     "ProvisioningStatus",
     "PublicNetworkAccess",
@@ -943,16 +1118,20 @@
     "SkuScaleType",
     "SkuTier",
     "SourceType",
+    "SparkJobEntryType",
     "SshPublicAccess",
     "SslConfigStatus",
     "StackMetaLearnerType",
     "Status",
+    "StatusMessageLevel",
     "StochasticOptimizer",
     "StorageAccountType",
     "TargetAggregationFunction",
     "TargetLagsMode",
     "TargetRollingWindowSizeMode",
     "TaskType",
+    "TextAnnotationType",
+    "TrainingMode",
     "TriggerType",
     "UnderlyingResourceAction",
     "UnitOfMeasure",
@@ -963,6 +1142,7 @@
     "ValidationMetricType",
     "ValueFormat",
     "VmPriority",
+    "VolumeDefinitionType",
     "WeekDay",
 ]
 __all__.extend([p for p in _patch_all if p not in __all__])
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/models/_machine_learning_services_mgmt_client_enums.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/models/_machine_learning_services_mgmt_client_enums.py
index 640b9b12a6505..594883fdae6b3 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/models/_machine_learning_services_mgmt_client_enums.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/models/_machine_learning_services_mgmt_client_enums.py
@@ -32,6 +32,17 @@ class ApplicationSharingPolicy(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     SHARED = "Shared"
 
 
+class AssetProvisioningState(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Provisioning state of registry asset."""
+
+    SUCCEEDED = "Succeeded"
+    FAILED = "Failed"
+    CANCELED = "Canceled"
+    CREATING = "Creating"
+    UPDATING = "Updating"
+    DELETING = "Deleting"
+
+
 class AutoRebuildSetting(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """AutoRebuild setting for the derived image."""
 
@@ -74,30 +85,30 @@ class BillingCurrency(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 class BlockedTransformers(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Enum for all classification models supported by AutoML."""
 
-    #: Target encoding for text data.
     TEXT_TARGET_ENCODER = "TextTargetEncoder"
-    #: Ohe hot encoding creates a binary feature transformation.
+    """Target encoding for text data."""
     ONE_HOT_ENCODER = "OneHotEncoder"
-    #: Target encoding for categorical data.
+    """Ohe hot encoding creates a binary feature transformation."""
     CAT_TARGET_ENCODER = "CatTargetEncoder"
-    #: Tf-Idf stands for, term-frequency times inverse document-frequency. This is a common term
-    #: weighting scheme for identifying information from documents.
+    """Target encoding for categorical data."""
     TF_IDF = "TfIdf"
-    #: Weight of Evidence encoding is a technique used to encode categorical variables. It uses the
-    #: natural log of the P(1)/P(0) to create weights.
+    """Tf-Idf stands for, term-frequency times inverse document-frequency. This is a common term
+    #: weighting scheme for identifying information from documents."""
     WO_E_TARGET_ENCODER = "WoETargetEncoder"
-    #: Label encoder converts labels/categorical variables in a numerical form.
+    """Weight of Evidence encoding is a technique used to encode categorical variables. It uses the
+    #: natural log of the P(1)/P(0) to create weights."""
     LABEL_ENCODER = "LabelEncoder"
-    #: Word embedding helps represents words or phrases as a vector, or a series of numbers.
+    """Label encoder converts labels/categorical variables in a numerical form."""
     WORD_EMBEDDING = "WordEmbedding"
-    #: Naive Bayes is a classified that is used for classification of discrete features that are
-    #: categorically distributed.
+    """Word embedding helps represents words or phrases as a vector, or a series of numbers."""
     NAIVE_BAYES = "NaiveBayes"
-    #: Count Vectorizer converts a collection of text documents to a matrix of token counts.
+    """Naive Bayes is a classified that is used for classification of discrete features that are
+    #: categorically distributed."""
     COUNT_VECTORIZER = "CountVectorizer"
-    #: Hashing One Hot Encoder can turn categorical variables into a limited number of new features.
-    #: This is often used for high-cardinality categorical features.
+    """Count Vectorizer converts a collection of text documents to a matrix of token counts."""
     HASH_ONE_HOT_ENCODER = "HashOneHotEncoder"
+    """Hashing One Hot Encoder can turn categorical variables into a limited number of new features.
+    #: This is often used for high-cardinality categorical features."""
 
 
 class Caching(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -111,107 +122,107 @@ class Caching(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 class ClassificationModels(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Enum for all classification models supported by AutoML."""
 
-    #: Logistic regression is a fundamental classification technique.
+    LOGISTIC_REGRESSION = "LogisticRegression"
+    """Logistic regression is a fundamental classification technique.
     #: It belongs to the group of linear classifiers and is somewhat similar to polynomial and linear
     #: regression.
     #: Logistic regression is fast and relatively uncomplicated, and it's convenient for you to
     #: interpret the results.
     #: Although it's essentially a method for binary classification, it can also be applied to
-    #: multiclass problems.
-    LOGISTIC_REGRESSION = "LogisticRegression"
-    #: SGD: Stochastic gradient descent is an optimization algorithm often used in machine learning
+    #: multiclass problems."""
+    SGD = "SGD"
+    """SGD: Stochastic gradient descent is an optimization algorithm often used in machine learning
     #: applications
     #: to find the model parameters that correspond to the best fit between predicted and actual
-    #: outputs.
-    SGD = "SGD"
-    #: The multinomial Naive Bayes classifier is suitable for classification with discrete features
+    #: outputs."""
+    MULTINOMIAL_NAIVE_BAYES = "MultinomialNaiveBayes"
+    """The multinomial Naive Bayes classifier is suitable for classification with discrete features
     #: (e.g., word counts for text classification).
     #: The multinomial distribution normally requires integer feature counts. However, in practice,
-    #: fractional counts such as tf-idf may also work.
-    MULTINOMIAL_NAIVE_BAYES = "MultinomialNaiveBayes"
-    #: Naive Bayes classifier for multivariate Bernoulli models.
+    #: fractional counts such as tf-idf may also work."""
     BERNOULLI_NAIVE_BAYES = "BernoulliNaiveBayes"
-    #: A support vector machine (SVM) is a supervised machine learning model that uses classification
+    """Naive Bayes classifier for multivariate Bernoulli models."""
+    SVM = "SVM"
+    """A support vector machine (SVM) is a supervised machine learning model that uses classification
     #: algorithms for two-group classification problems.
     #: After giving an SVM model sets of labeled training data for each category, they're able to
-    #: categorize new text.
-    SVM = "SVM"
-    #: A support vector machine (SVM) is a supervised machine learning model that uses classification
+    #: categorize new text."""
+    LINEAR_SVM = "LinearSVM"
+    """A support vector machine (SVM) is a supervised machine learning model that uses classification
     #: algorithms for two-group classification problems.
     #: After giving an SVM model sets of labeled training data for each category, they're able to
     #: categorize new text.
     #: Linear SVM performs best when input data is linear, i.e., data can be easily classified by
-    #: drawing the straight line between classified values on a plotted graph.
-    LINEAR_SVM = "LinearSVM"
-    #: K-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new
+    #: drawing the straight line between classified values on a plotted graph."""
+    KNN = "KNN"
+    """K-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new
     #: datapoints
     #: which further means that the new data point will be assigned a value based on how closely it
-    #: matches the points in the training set.
-    KNN = "KNN"
-    #: Decision Trees are a non-parametric supervised learning method used for both classification and
+    #: matches the points in the training set."""
+    DECISION_TREE = "DecisionTree"
+    """Decision Trees are a non-parametric supervised learning method used for both classification and
     #: regression tasks.
     #: The goal is to create a model that predicts the value of a target variable by learning simple
-    #: decision rules inferred from the data features.
-    DECISION_TREE = "DecisionTree"
-    #: Random forest is a supervised learning algorithm.
+    #: decision rules inferred from the data features."""
+    RANDOM_FOREST = "RandomForest"
+    """Random forest is a supervised learning algorithm.
     #: The "forest"\  it builds, is an ensemble of decision trees, usually trained with the “bagging”\
     #: method.
     #: The general idea of the bagging method is that a combination of learning models increases the
-    #: overall result.
-    RANDOM_FOREST = "RandomForest"
-    #: Extreme Trees is an ensemble machine learning algorithm that combines the predictions from many
-    #: decision trees. It is related to the widely used random forest algorithm.
+    #: overall result."""
     EXTREME_RANDOM_TREES = "ExtremeRandomTrees"
-    #: LightGBM is a gradient boosting framework that uses tree based learning algorithms.
+    """Extreme Trees is an ensemble machine learning algorithm that combines the predictions from many
+    #: decision trees. It is related to the widely used random forest algorithm."""
     LIGHT_GBM = "LightGBM"
-    #: The technique of transiting week learners into a strong learner is called Boosting. The
-    #: gradient boosting algorithm process works on this theory of execution.
+    """LightGBM is a gradient boosting framework that uses tree based learning algorithms."""
     GRADIENT_BOOSTING = "GradientBoosting"
-    #: XGBoost: Extreme Gradient Boosting Algorithm. This algorithm is used for structured data where
-    #: target column values can be divided into distinct class values.
+    """The technique of transiting week learners into a strong learner is called Boosting. The
+    #: gradient boosting algorithm process works on this theory of execution."""
     XG_BOOST_CLASSIFIER = "XGBoostClassifier"
+    """XGBoost: Extreme Gradient Boosting Algorithm. This algorithm is used for structured data where
+    #: target column values can be divided into distinct class values."""
 
 
 class ClassificationMultilabelPrimaryMetrics(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Primary metrics for classification multilabel tasks."""
 
-    #: AUC is the Area under the curve.
-    #: This metric represents arithmetic mean of the score for each class,
-    #: weighted by the number of true instances in each class.
     AUC_WEIGHTED = "AUCWeighted"
-    #: Accuracy is the ratio of predictions that exactly match the true class labels.
+    """AUC is the Area under the curve.
+    #: This metric represents arithmetic mean of the score for each class,
+    #: weighted by the number of true instances in each class."""
     ACCURACY = "Accuracy"
-    #: Normalized macro recall is recall macro-averaged and normalized, so that random
-    #: performance has a score of 0, and perfect performance has a score of 1.
+    """Accuracy is the ratio of predictions that exactly match the true class labels."""
     NORM_MACRO_RECALL = "NormMacroRecall"
-    #: The arithmetic mean of the average precision score for each class, weighted by
-    #: the number of true instances in each class.
+    """Normalized macro recall is recall macro-averaged and normalized, so that random
+    #: performance has a score of 0, and perfect performance has a score of 1."""
     AVERAGE_PRECISION_SCORE_WEIGHTED = "AveragePrecisionScoreWeighted"
-    #: The arithmetic mean of precision for each class, weighted by number of true instances in each
-    #: class.
+    """The arithmetic mean of the average precision score for each class, weighted by
+    #: the number of true instances in each class."""
     PRECISION_SCORE_WEIGHTED = "PrecisionScoreWeighted"
-    #: Intersection Over Union. Intersection of predictions divided by union of predictions.
+    """The arithmetic mean of precision for each class, weighted by number of true instances in each
+    #: class."""
     IOU = "IOU"
+    """Intersection Over Union. Intersection of predictions divided by union of predictions."""
 
 
 class ClassificationPrimaryMetrics(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Primary metrics for classification tasks."""
 
-    #: AUC is the Area under the curve.
-    #: This metric represents arithmetic mean of the score for each class,
-    #: weighted by the number of true instances in each class.
     AUC_WEIGHTED = "AUCWeighted"
-    #: Accuracy is the ratio of predictions that exactly match the true class labels.
+    """AUC is the Area under the curve.
+    #: This metric represents arithmetic mean of the score for each class,
+    #: weighted by the number of true instances in each class."""
     ACCURACY = "Accuracy"
-    #: Normalized macro recall is recall macro-averaged and normalized, so that random
-    #: performance has a score of 0, and perfect performance has a score of 1.
+    """Accuracy is the ratio of predictions that exactly match the true class labels."""
     NORM_MACRO_RECALL = "NormMacroRecall"
-    #: The arithmetic mean of the average precision score for each class, weighted by
-    #: the number of true instances in each class.
+    """Normalized macro recall is recall macro-averaged and normalized, so that random
+    #: performance has a score of 0, and perfect performance has a score of 1."""
     AVERAGE_PRECISION_SCORE_WEIGHTED = "AveragePrecisionScoreWeighted"
-    #: The arithmetic mean of precision for each class, weighted by number of true instances in each
-    #: class.
+    """The arithmetic mean of the average precision score for each class, weighted by
+    #: the number of true instances in each class."""
     PRECISION_SCORE_WEIGHTED = "PrecisionScoreWeighted"
+    """The arithmetic mean of precision for each class, weighted by number of true instances in each
+    #: class."""
 
 
 class ClusterPurpose(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -249,7 +260,7 @@ class ComputeInstanceState(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 
 
 class ComputePowerAction(str, Enum, metaclass=CaseInsensitiveEnumMeta):
-    """The compute power action."""
+    """[Required] The compute power action."""
 
     START = "Start"
     STOP = "Stop"
@@ -278,6 +289,8 @@ class ConnectionAuthType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     USERNAME_PASSWORD = "UsernamePassword"
     NONE = "None"
     SAS = "SAS"
+    SERVICE_PRINCIPAL = "ServicePrincipal"
+    ACCESS_KEY = "AccessKey"
 
 
 class ConnectionCategory(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -286,13 +299,22 @@ class ConnectionCategory(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     PYTHON_FEED = "PythonFeed"
     CONTAINER_REGISTRY = "ContainerRegistry"
     GIT = "Git"
+    FEATURE_STORE = "FeatureStore"
+    S3 = "S3"
+    SNOWFLAKE = "Snowflake"
+    AZURE_SQL_DB = "AzureSqlDb"
+    AZURE_SYNAPSE_ANALYTICS = "AzureSynapseAnalytics"
 
 
 class ContainerType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
-    """ContainerType."""
+    """The type of container to retrieve logs from."""
 
     STORAGE_INITIALIZER = "StorageInitializer"
+    """The container used to download models and score script."""
     INFERENCE_SERVER = "InferenceServer"
+    """The container used to serve user's request."""
+    MODEL_DATA_COLLECTOR = "ModelDataCollector"
+    """The container used to collect payload and custom logging when mdc is enabled."""
 
 
 class CreatedByType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -312,6 +334,8 @@ class CredentialsType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     NONE = "None"
     SAS = "Sas"
     SERVICE_PRINCIPAL = "ServicePrincipal"
+    KERBEROS_KEYTAB = "KerberosKeytab"
+    KERBEROS_PASSWORD = "KerberosPassword"
 
 
 class DatastoreType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -321,6 +345,7 @@ class DatastoreType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     AZURE_DATA_LAKE_GEN1 = "AzureDataLakeGen1"
     AZURE_DATA_LAKE_GEN2 = "AzureDataLakeGen2"
     AZURE_FILE = "AzureFile"
+    HDFS = "Hdfs"
 
 
 class DataType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -417,128 +442,142 @@ class EnvironmentType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     USER_CREATED = "UserCreated"
 
 
+class EnvironmentVariableType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Type of the Environment Variable. Possible values are: local - For local variable."""
+
+    LOCAL = "local"
+
+
+class ExportFormatType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """The format of exported labels."""
+
+    DATASET = "Dataset"
+    COCO = "Coco"
+    CSV = "CSV"
+
+
 class FeatureLags(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Flag for generating lags for the numeric features."""
 
-    #: No feature lags generated.
     NONE = "None"
-    #: System auto-generates feature lags.
+    """No feature lags generated."""
     AUTO = "Auto"
+    """System auto-generates feature lags."""
 
 
 class FeaturizationMode(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Featurization mode - determines data featurization mode."""
 
-    #: Auto mode, system performs featurization without any custom featurization inputs.
     AUTO = "Auto"
-    #: Custom featurization.
+    """Auto mode, system performs featurization without any custom featurization inputs."""
     CUSTOM = "Custom"
-    #: Featurization off. 'Forecasting' task cannot use this value.
+    """Custom featurization."""
     OFF = "Off"
+    """Featurization off. 'Forecasting' task cannot use this value."""
 
 
 class ForecastHorizonMode(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Enum to determine forecast horizon selection mode."""
 
-    #: Forecast horizon to be determined automatically.
     AUTO = "Auto"
-    #: Use the custom forecast horizon.
+    """Forecast horizon to be determined automatically."""
     CUSTOM = "Custom"
+    """Use the custom forecast horizon."""
 
 
 class ForecastingModels(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Enum for all forecasting models supported by AutoML."""
 
-    #: Auto-Autoregressive Integrated Moving Average (ARIMA) model uses time-series data and
+    AUTO_ARIMA = "AutoArima"
+    """Auto-Autoregressive Integrated Moving Average (ARIMA) model uses time-series data and
     #: statistical analysis to interpret the data and make future predictions.
     #: This model aims to explain data by using time series data on its past values and uses linear
-    #: regression to make predictions.
-    AUTO_ARIMA = "AutoArima"
-    #: Prophet is a procedure for forecasting time series data based on an additive model where
+    #: regression to make predictions."""
+    PROPHET = "Prophet"
+    """Prophet is a procedure for forecasting time series data based on an additive model where
     #: non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects.
     #: It works best with time series that have strong seasonal effects and several seasons of
     #: historical data. Prophet is robust to missing data and shifts in the trend, and typically
-    #: handles outliers well.
-    PROPHET = "Prophet"
-    #: The Naive forecasting model makes predictions by carrying forward the latest target value for
-    #: each time-series in the training data.
+    #: handles outliers well."""
     NAIVE = "Naive"
-    #: The Seasonal Naive forecasting model makes predictions by carrying forward the latest season of
-    #: target values for each time-series in the training data.
+    """The Naive forecasting model makes predictions by carrying forward the latest target value for
+    #: each time-series in the training data."""
     SEASONAL_NAIVE = "SeasonalNaive"
-    #: The Average forecasting model makes predictions by carrying forward the average of the target
-    #: values for each time-series in the training data.
+    """The Seasonal Naive forecasting model makes predictions by carrying forward the latest season of
+    #: target values for each time-series in the training data."""
     AVERAGE = "Average"
-    #: The Seasonal Average forecasting model makes predictions by carrying forward the average value
-    #: of the latest season of data for each time-series in the training data.
+    """The Average forecasting model makes predictions by carrying forward the average of the target
+    #: values for each time-series in the training data."""
     SEASONAL_AVERAGE = "SeasonalAverage"
-    #: Exponential smoothing is a time series forecasting method for univariate data that can be
-    #: extended to support data with a systematic trend or seasonal component.
+    """The Seasonal Average forecasting model makes predictions by carrying forward the average value
+    #: of the latest season of data for each time-series in the training data."""
     EXPONENTIAL_SMOOTHING = "ExponentialSmoothing"
-    #: An Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) model can be
+    """Exponential smoothing is a time series forecasting method for univariate data that can be
+    #: extended to support data with a systematic trend or seasonal component."""
+    ARIMAX = "Arimax"
+    """An Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) model can be
     #: viewed as a multiple regression model with one or more autoregressive (AR) terms and/or one or
     #: more moving average (MA) terms.
     #: This method is suitable for forecasting when data is stationary/non stationary, and
-    #: multivariate with any type of data pattern, i.e., level/trend /seasonality/cyclicity.
-    ARIMAX = "Arimax"
-    #: TCNForecaster: Temporal Convolutional Networks Forecaster. //TODO: Ask forecasting team for
-    #: brief intro.
+    #: multivariate with any type of data pattern, i.e., level/trend /seasonality/cyclicity."""
     TCN_FORECASTER = "TCNForecaster"
-    #: Elastic net is a popular type of regularized linear regression that combines two popular
-    #: penalties, specifically the L1 and L2 penalty functions.
+    """TCNForecaster: Temporal Convolutional Networks Forecaster. //TODO: Ask forecasting team for
+    #: brief intro."""
     ELASTIC_NET = "ElasticNet"
-    #: The technique of transiting week learners into a strong learner is called Boosting. The
-    #: gradient boosting algorithm process works on this theory of execution.
+    """Elastic net is a popular type of regularized linear regression that combines two popular
+    #: penalties, specifically the L1 and L2 penalty functions."""
     GRADIENT_BOOSTING = "GradientBoosting"
-    #: Decision Trees are a non-parametric supervised learning method used for both classification and
+    """The technique of transiting week learners into a strong learner is called Boosting. The
+    #: gradient boosting algorithm process works on this theory of execution."""
+    DECISION_TREE = "DecisionTree"
+    """Decision Trees are a non-parametric supervised learning method used for both classification and
     #: regression tasks.
     #: The goal is to create a model that predicts the value of a target variable by learning simple
-    #: decision rules inferred from the data features.
-    DECISION_TREE = "DecisionTree"
-    #: K-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new
+    #: decision rules inferred from the data features."""
+    KNN = "KNN"
+    """K-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new
     #: datapoints
     #: which further means that the new data point will be assigned a value based on how closely it
-    #: matches the points in the training set.
-    KNN = "KNN"
-    #: Lasso model fit with Least Angle Regression a.k.a. Lars. It is a Linear Model trained with an
-    #: L1 prior as regularizer.
+    #: matches the points in the training set."""
     LASSO_LARS = "LassoLars"
-    #: SGD: Stochastic gradient descent is an optimization algorithm often used in machine learning
+    """Lasso model fit with Least Angle Regression a.k.a. Lars. It is a Linear Model trained with an
+    #: L1 prior as regularizer."""
+    SGD = "SGD"
+    """SGD: Stochastic gradient descent is an optimization algorithm often used in machine learning
     #: applications
     #: to find the model parameters that correspond to the best fit between predicted and actual
     #: outputs.
-    #: It's an inexact but powerful technique.
-    SGD = "SGD"
-    #: Random forest is a supervised learning algorithm.
+    #: It's an inexact but powerful technique."""
+    RANDOM_FOREST = "RandomForest"
+    """Random forest is a supervised learning algorithm.
     #: The "forest" it builds, is an ensemble of decision trees, usually trained with the “bagging”
     #: method.
     #: The general idea of the bagging method is that a combination of learning models increases the
-    #: overall result.
-    RANDOM_FOREST = "RandomForest"
-    #: Extreme Trees is an ensemble machine learning algorithm that combines the predictions from many
-    #: decision trees. It is related to the widely used random forest algorithm.
+    #: overall result."""
     EXTREME_RANDOM_TREES = "ExtremeRandomTrees"
-    #: LightGBM is a gradient boosting framework that uses tree based learning algorithms.
+    """Extreme Trees is an ensemble machine learning algorithm that combines the predictions from many
+    #: decision trees. It is related to the widely used random forest algorithm."""
     LIGHT_GBM = "LightGBM"
-    #: XGBoostRegressor: Extreme Gradient Boosting Regressor is a supervised machine learning model
-    #: using ensemble of base learners.
+    """LightGBM is a gradient boosting framework that uses tree based learning algorithms."""
     XG_BOOST_REGRESSOR = "XGBoostRegressor"
+    """XGBoostRegressor: Extreme Gradient Boosting Regressor is a supervised machine learning model
+    #: using ensemble of base learners."""
 
 
 class ForecastingPrimaryMetrics(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Primary metrics for Forecasting task."""
 
-    #: The Spearman's rank coefficient of correlation is a non-parametric measure of rank correlation.
     SPEARMAN_CORRELATION = "SpearmanCorrelation"
-    #: The Normalized Root Mean Squared Error (NRMSE) the RMSE facilitates the comparison between
-    #: models with different scales.
+    """The Spearman's rank coefficient of correlation is a non-parametric measure of rank correlation."""
     NORMALIZED_ROOT_MEAN_SQUARED_ERROR = "NormalizedRootMeanSquaredError"
-    #: The R2 score is one of the performance evaluation measures for forecasting-based machine
-    #: learning models.
+    """The Normalized Root Mean Squared Error (NRMSE) the RMSE facilitates the comparison between
+    #: models with different scales."""
     R2_SCORE = "R2Score"
-    #: The Normalized Mean Absolute Error (NMAE) is a validation metric to compare the Mean Absolute
-    #: Error (MAE) of (time) series with different scales.
+    """The R2 score is one of the performance evaluation measures for forecasting-based machine
+    #: learning models."""
     NORMALIZED_MEAN_ABSOLUTE_ERROR = "NormalizedMeanAbsoluteError"
+    """The Normalized Mean Absolute Error (NMAE) is a validation metric to compare the Mean Absolute
+    #: Error (MAE) of (time) series with different scales."""
 
 
 class Goal(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -556,6 +595,30 @@ class IdentityConfigurationType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     USER_IDENTITY = "UserIdentity"
 
 
+class ImageAnnotationType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Annotation type of image data."""
+
+    CLASSIFICATION = "Classification"
+    BOUNDING_BOX = "BoundingBox"
+    INSTANCE_SEGMENTATION = "InstanceSegmentation"
+
+
+class ImageType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Type of the image. Possible values are: docker - For docker images. azureml - For AzureML
+    images.
+    """
+
+    DOCKER = "docker"
+    AZUREML = "azureml"
+
+
+class IncrementalDataRefresh(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Whether IncrementalDataRefresh is enabled."""
+
+    ENABLED = "Enabled"
+    DISABLED = "Disabled"
+
+
 class InputDeliveryMode(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Enum to determine the input data delivery mode."""
 
@@ -570,9 +633,9 @@ class InputDeliveryMode(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 class InstanceSegmentationPrimaryMetrics(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Primary metrics for InstanceSegmentation tasks."""
 
-    #: Mean Average Precision (MAP) is the average of AP (Average Precision).
-    #: AP is calculated for each class and averaged to get the MAP.
     MEAN_AVERAGE_PRECISION = "MeanAveragePrecision"
+    """Mean Average Precision (MAP) is the average of AP (Average Precision).
+    #: AP is calculated for each class and averaged to get the MAP."""
 
 
 class JobInputType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -605,42 +668,53 @@ class JobOutputType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     TRITON_MODEL = "triton_model"
 
 
+class JobProvisioningState(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Enum to determine the job provisioning state."""
+
+    SUCCEEDED = "Succeeded"
+    FAILED = "Failed"
+    CANCELED = "Canceled"
+    IN_PROGRESS = "InProgress"
+
+
 class JobStatus(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """The status of a job."""
 
-    #: Run hasn't started yet.
     NOT_STARTED = "NotStarted"
-    #: Run has started. The user has a run ID.
+    """Run hasn't started yet."""
     STARTING = "Starting"
-    #: (Not used currently) It will be used if ES is creating the compute target.
+    """Run has started. The user has a run ID."""
     PROVISIONING = "Provisioning"
-    #: The run environment is being prepared.
+    """(Not used currently) It will be used if ES is creating the compute target."""
     PREPARING = "Preparing"
-    #: The job is queued in the compute target. For example, in BatchAI the job is in queued state,
-    #: while waiting for all required nodes to be ready.
+    """The run environment is being prepared."""
     QUEUED = "Queued"
-    #: The job started to run in the compute target.
+    """The job is queued in the compute target. For example, in BatchAI the job is in queued state,
+    #: while waiting for all required nodes to be ready."""
     RUNNING = "Running"
-    #: Job is completed in the target. It is in output collection state now.
+    """The job started to run in the compute target."""
     FINALIZING = "Finalizing"
-    #: Cancellation has been requested for the job.
+    """Job is completed in the target. It is in output collection state now."""
     CANCEL_REQUESTED = "CancelRequested"
-    #: Job completed successfully. This reflects that both the job itself and output collection states
-    #: completed successfully
+    """Cancellation has been requested for the job."""
     COMPLETED = "Completed"
-    #: Job failed.
+    """Job completed successfully. This reflects that both the job itself and output collection states
+    #: completed successfully"""
     FAILED = "Failed"
-    #: Following cancellation request, the job is now successfully canceled.
+    """Job failed."""
     CANCELED = "Canceled"
-    #: When heartbeat is enabled, if the run isn't updating any information to RunHistory then the run
+    """Following cancellation request, the job is now successfully canceled."""
+    NOT_RESPONDING = "NotResponding"
+    """When heartbeat is enabled, if the run isn't updating any information to RunHistory then the run
     #: goes to NotResponding state.
     #: NotResponding is the only state that is exempt from strict transition orders. A run can go from
-    #: NotResponding to any of the previous states.
-    NOT_RESPONDING = "NotResponding"
-    #: The job is paused by users. Some adjustment to labeling jobs can be made only in paused state.
+    #: NotResponding to any of the previous states."""
     PAUSED = "Paused"
-    #: Default job status if not mapped to all other statuses
+    """The job is paused by users. Some adjustment to labeling jobs can be made only in paused state."""
     UNKNOWN = "Unknown"
+    """Default job status if not mapped to all other statuses"""
+    SCHEDULED = "Scheduled"
+    """The job is in a scheduled state. Job is not in any active state."""
 
 
 class JobType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -648,8 +722,10 @@ class JobType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 
     AUTO_ML = "AutoML"
     COMMAND = "Command"
+    LABELING = "Labeling"
     SWEEP = "Sweep"
     PIPELINE = "Pipeline"
+    SPARK = "Spark"
 
 
 class KeyType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -662,12 +738,12 @@ class KeyType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 class LearningRateScheduler(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Learning rate scheduler enum."""
 
-    #: No learning rate scheduler selected.
     NONE = "None"
-    #: Cosine Annealing With Warmup.
+    """No learning rate scheduler selected."""
     WARMUP_COSINE = "WarmupCosine"
-    #: Step learning rate scheduler.
+    """Cosine Annealing With Warmup."""
     STEP = "Step"
+    """Step learning rate scheduler."""
 
 
 class ListViewType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -688,18 +764,18 @@ class LoadBalancerType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 class LogVerbosity(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Enum for setting log verbosity."""
 
-    #: No logs emitted.
     NOT_SET = "NotSet"
-    #: Debug and above log statements logged.
+    """No logs emitted."""
     DEBUG = "Debug"
-    #: Info and above log statements logged.
+    """Debug and above log statements logged."""
     INFO = "Info"
-    #: Warning and above log statements logged.
+    """Info and above log statements logged."""
     WARNING = "Warning"
-    #: Error and above log statements logged.
+    """Warning and above log statements logged."""
     ERROR = "Error"
-    #: Only critical statements logged.
+    """Error and above log statements logged."""
     CRITICAL = "Critical"
+    """Only critical statements logged."""
 
 
 class ManagedServiceIdentityType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -713,19 +789,47 @@ class ManagedServiceIdentityType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     SYSTEM_ASSIGNED_USER_ASSIGNED = "SystemAssigned,UserAssigned"
 
 
+class MediaType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Media type of data asset."""
+
+    IMAGE = "Image"
+    TEXT = "Text"
+
+
+class MLAssistConfigurationType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """MLAssistConfigurationType."""
+
+    ENABLED = "Enabled"
+    DISABLED = "Disabled"
+
+
+class MlflowAutologger(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Indicates whether mlflow autologger is enabled for notebooks."""
+
+    ENABLED = "Enabled"
+    DISABLED = "Disabled"
+
+
+class MLFlowAutologgerState(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Enum to determine the state of mlflow autologger."""
+
+    ENABLED = "Enabled"
+    DISABLED = "Disabled"
+
+
 class ModelSize(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Image model size."""
 
-    #: No value selected.
     NONE = "None"
-    #: Small size.
+    """No value selected."""
     SMALL = "Small"
-    #: Medium size.
+    """Small size."""
     MEDIUM = "Medium"
-    #: Large size.
+    """Medium size."""
     LARGE = "Large"
-    #: Extra large size.
+    """Large size."""
     EXTRA_LARGE = "ExtraLarge"
+    """Extra large size."""
 
 
 class MountAction(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -746,14 +850,21 @@ class MountState(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     UNMOUNTED = "Unmounted"
 
 
+class MultiSelect(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Whether multiSelect is enabled."""
+
+    ENABLED = "Enabled"
+    DISABLED = "Disabled"
+
+
 class NCrossValidationsMode(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Determines how N-Cross validations value is determined."""
 
-    #: Determine N-Cross validations value automatically. Supported only for 'Forecasting' AutoML
-    #: task.
     AUTO = "Auto"
-    #: Use custom N-Cross validations value.
+    """Determine N-Cross validations value automatically. Supported only for 'Forecasting' AutoML
+    #: task."""
     CUSTOM = "Custom"
+    """Use custom N-Cross validations value."""
 
 
 class Network(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -763,6 +874,25 @@ class Network(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     HOST = "Host"
 
 
+class NlpLearningRateScheduler(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Enum of learning rate schedulers that aligns with those supported by HF."""
+
+    NONE = "None"
+    """No learning rate schedule."""
+    LINEAR = "Linear"
+    """Linear warmup and decay."""
+    COSINE = "Cosine"
+    """Linear warmup then cosine decay."""
+    COSINE_WITH_RESTARTS = "CosineWithRestarts"
+    """Linear warmup, cosine decay, then restart to initial LR."""
+    POLYNOMIAL = "Polynomial"
+    """Increase linearly then polynomially decay."""
+    CONSTANT = "Constant"
+    """Constant learning rate."""
+    CONSTANT_WITH_WARMUP = "ConstantWithWarmup"
+    """Linear warmup followed by constant value."""
+
+
 class NodeState(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """State of the compute node. Values are idle, running, preparing, unusable, leaving and
     preempted.
@@ -776,12 +906,19 @@ class NodeState(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     PREEMPTED = "preempted"
 
 
+class NodesValueType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """The enumerated types for the nodes value."""
+
+    ALL = "All"
+    CUSTOM = "Custom"
+
+
 class ObjectDetectionPrimaryMetrics(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Primary metrics for Image ObjectDetection task."""
 
-    #: Mean Average Precision (MAP) is the average of AP (Average Precision).
-    #: AP is calculated for each class and averaged to get the MAP.
     MEAN_AVERAGE_PRECISION = "MeanAveragePrecision"
+    """Mean Average Precision (MAP) is the average of AP (Average Precision).
+    #: AP is calculated for each class and averaged to get the MAP."""
 
 
 class OperatingSystemType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -844,6 +981,7 @@ class OutputDeliveryMode(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 
     READ_WRITE_MOUNT = "ReadWriteMount"
     UPLOAD = "Upload"
+    DIRECT = "Direct"
 
 
 class PrivateEndpointConnectionProvisioningState(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -865,6 +1003,14 @@ class PrivateEndpointServiceConnectionStatus(str, Enum, metaclass=CaseInsensitiv
     TIMEOUT = "Timeout"
 
 
+class Protocol(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Protocol over which communication will happen over this endpoint."""
+
+    TCP = "tcp"
+    UDP = "udp"
+    HTTP = "http"
+
+
 class ProvisioningState(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """The current deployment state of workspace resource. The provisioningState is to indicate states
     for resource provisioning.
@@ -877,6 +1023,7 @@ class ProvisioningState(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     SUCCEEDED = "Succeeded"
     FAILED = "Failed"
     CANCELED = "Canceled"
+    SOFT_DELETED = "SoftDeleted"
 
 
 class ProvisioningStatus(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -917,16 +1064,16 @@ class RandomSamplingAlgorithmRule(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 class RecurrenceFrequency(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Enum to describe the frequency of a recurrence schedule."""
 
-    #: Minute frequency
     MINUTE = "Minute"
-    #: Hour frequency
+    """Minute frequency"""
     HOUR = "Hour"
-    #: Day frequency
+    """Hour frequency"""
     DAY = "Day"
-    #: Week frequency
+    """Day frequency"""
     WEEK = "Week"
-    #: Month frequency
+    """Week frequency"""
     MONTH = "Month"
+    """Month frequency"""
 
 
 class ReferenceType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -940,61 +1087,61 @@ class ReferenceType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 class RegressionModels(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Enum for all Regression models supported by AutoML."""
 
-    #: Elastic net is a popular type of regularized linear regression that combines two popular
-    #: penalties, specifically the L1 and L2 penalty functions.
     ELASTIC_NET = "ElasticNet"
-    #: The technique of transiting week learners into a strong learner is called Boosting. The
-    #: gradient boosting algorithm process works on this theory of execution.
+    """Elastic net is a popular type of regularized linear regression that combines two popular
+    #: penalties, specifically the L1 and L2 penalty functions."""
     GRADIENT_BOOSTING = "GradientBoosting"
-    #: Decision Trees are a non-parametric supervised learning method used for both classification and
+    """The technique of transiting week learners into a strong learner is called Boosting. The
+    #: gradient boosting algorithm process works on this theory of execution."""
+    DECISION_TREE = "DecisionTree"
+    """Decision Trees are a non-parametric supervised learning method used for both classification and
     #: regression tasks.
     #: The goal is to create a model that predicts the value of a target variable by learning simple
-    #: decision rules inferred from the data features.
-    DECISION_TREE = "DecisionTree"
-    #: K-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new
+    #: decision rules inferred from the data features."""
+    KNN = "KNN"
+    """K-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new
     #: datapoints
     #: which further means that the new data point will be assigned a value based on how closely it
-    #: matches the points in the training set.
-    KNN = "KNN"
-    #: Lasso model fit with Least Angle Regression a.k.a. Lars. It is a Linear Model trained with an
-    #: L1 prior as regularizer.
+    #: matches the points in the training set."""
     LASSO_LARS = "LassoLars"
-    #: SGD: Stochastic gradient descent is an optimization algorithm often used in machine learning
+    """Lasso model fit with Least Angle Regression a.k.a. Lars. It is a Linear Model trained with an
+    #: L1 prior as regularizer."""
+    SGD = "SGD"
+    """SGD: Stochastic gradient descent is an optimization algorithm often used in machine learning
     #: applications
     #: to find the model parameters that correspond to the best fit between predicted and actual
     #: outputs.
-    #: It's an inexact but powerful technique.
-    SGD = "SGD"
-    #: Random forest is a supervised learning algorithm.
+    #: It's an inexact but powerful technique."""
+    RANDOM_FOREST = "RandomForest"
+    """Random forest is a supervised learning algorithm.
     #: The "forest"\  it builds, is an ensemble of decision trees, usually trained with the “bagging”\
     #: method.
     #: The general idea of the bagging method is that a combination of learning models increases the
-    #: overall result.
-    RANDOM_FOREST = "RandomForest"
-    #: Extreme Trees is an ensemble machine learning algorithm that combines the predictions from many
-    #: decision trees. It is related to the widely used random forest algorithm.
+    #: overall result."""
     EXTREME_RANDOM_TREES = "ExtremeRandomTrees"
-    #: LightGBM is a gradient boosting framework that uses tree based learning algorithms.
+    """Extreme Trees is an ensemble machine learning algorithm that combines the predictions from many
+    #: decision trees. It is related to the widely used random forest algorithm."""
     LIGHT_GBM = "LightGBM"
-    #: XGBoostRegressor: Extreme Gradient Boosting Regressor is a supervised machine learning model
-    #: using ensemble of base learners.
+    """LightGBM is a gradient boosting framework that uses tree based learning algorithms."""
     XG_BOOST_REGRESSOR = "XGBoostRegressor"
+    """XGBoostRegressor: Extreme Gradient Boosting Regressor is a supervised machine learning model
+    #: using ensemble of base learners."""
 
 
 class RegressionPrimaryMetrics(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Primary metrics for Regression task."""
 
-    #: The Spearman's rank coefficient of correlation is a nonparametric measure of rank correlation.
     SPEARMAN_CORRELATION = "SpearmanCorrelation"
-    #: The Normalized Root Mean Squared Error (NRMSE) the RMSE facilitates the comparison between
-    #: models with different scales.
+    """The Spearman's rank coefficient of correlation is a nonparametric measure of rank correlation."""
     NORMALIZED_ROOT_MEAN_SQUARED_ERROR = "NormalizedRootMeanSquaredError"
-    #: The R2 score is one of the performance evaluation measures for forecasting-based machine
-    #: learning models.
+    """The Normalized Root Mean Squared Error (NRMSE) the RMSE facilitates the comparison between
+    #: models with different scales."""
     R2_SCORE = "R2Score"
-    #: The Normalized Mean Absolute Error (NMAE) is a validation metric to compare the Mean Absolute
-    #: Error (MAE) of (time) series with different scales.
+    """The R2 score is one of the performance evaluation measures for forecasting-based machine
+    #: learning models."""
     NORMALIZED_MEAN_ABSOLUTE_ERROR = "NormalizedMeanAbsoluteError"
+    """The Normalized Mean Absolute Error (NMAE) is a validation metric to compare the Mean Absolute
+    #: Error (MAE) of (time) series with different scales."""
 
 
 class RemoteLoginPortPublicAccess(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -1070,10 +1217,10 @@ class ScheduleStatus(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 class SeasonalityMode(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Forecasting seasonality mode."""
 
-    #: Seasonality to be determined automatically.
     AUTO = "Auto"
-    #: Use the custom seasonality value.
+    """Seasonality to be determined automatically."""
     CUSTOM = "Custom"
+    """Use the custom seasonality value."""
 
 
 class SecretsType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -1083,42 +1230,44 @@ class SecretsType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     CERTIFICATE = "Certificate"
     SAS = "Sas"
     SERVICE_PRINCIPAL = "ServicePrincipal"
+    KERBEROS_PASSWORD = "KerberosPassword"
+    KERBEROS_KEYTAB = "KerberosKeytab"
 
 
 class ServiceDataAccessAuthIdentity(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """ServiceDataAccessAuthIdentity."""
 
-    #: Do not use any identity for service data access.
     NONE = "None"
-    #: Use the system assigned managed identity of the Workspace to authenticate service data access.
+    """Do not use any identity for service data access."""
     WORKSPACE_SYSTEM_ASSIGNED_IDENTITY = "WorkspaceSystemAssignedIdentity"
-    #: Use the user assigned managed identity of the Workspace to authenticate service data access.
+    """Use the system assigned managed identity of the Workspace to authenticate service data access."""
     WORKSPACE_USER_ASSIGNED_IDENTITY = "WorkspaceUserAssignedIdentity"
+    """Use the user assigned managed identity of the Workspace to authenticate service data access."""
 
 
 class ShortSeriesHandlingConfiguration(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """The parameter defining how if AutoML should handle short time series."""
 
-    #: Represents no/null value.
     NONE = "None"
-    #: Short series will be padded if there are no long series, otherwise short series will be
-    #: dropped.
+    """Represents no/null value."""
     AUTO = "Auto"
-    #: All the short series will be padded.
+    """Short series will be padded if there are no long series, otherwise short series will be
+    #: dropped."""
     PAD = "Pad"
-    #: All the short series will be dropped.
+    """All the short series will be padded."""
     DROP = "Drop"
+    """All the short series will be dropped."""
 
 
 class SkuScaleType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Node scaling setting for the compute sku."""
 
-    #: Automatically scales node count.
     AUTOMATIC = "Automatic"
-    #: Node count scaled upon user request.
+    """Automatically scales node count."""
     MANUAL = "Manual"
-    #: Fixed set of nodes.
+    """Node count scaled upon user request."""
     NONE = "None"
+    """Fixed set of nodes."""
 
 
 class SkuTier(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -1140,6 +1289,13 @@ class SourceType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     URI = "URI"
 
 
+class SparkJobEntryType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """SparkJobEntryType."""
+
+    SPARK_JOB_PYTHON_ENTRY = "SparkJobPythonEntry"
+    SPARK_JOB_SCALA_ENTRY = "SparkJobScalaEntry"
+
+
 class SshPublicAccess(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """State of the public SSH port. Possible values are: Disabled - Indicates that the public ssh
     port is closed on this instance. Enabled - Indicates that the public ssh port is open and
@@ -1168,15 +1324,15 @@ class StackMetaLearnerType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """
 
     NONE = "None"
-    #: Default meta-learners are LogisticRegression for classification tasks.
     LOGISTIC_REGRESSION = "LogisticRegression"
-    #: Default meta-learners are LogisticRegression for classification task when CV is on.
+    """Default meta-learners are LogisticRegression for classification tasks."""
     LOGISTIC_REGRESSION_CV = "LogisticRegressionCV"
+    """Default meta-learners are LogisticRegression for classification task when CV is on."""
     LIGHT_GBM_CLASSIFIER = "LightGBMClassifier"
-    #: Default meta-learners are LogisticRegression for regression task.
     ELASTIC_NET = "ElasticNet"
-    #: Default meta-learners are LogisticRegression for regression task when CV is on.
+    """Default meta-learners are LogisticRegression for regression task."""
     ELASTIC_NET_CV = "ElasticNetCV"
+    """Default meta-learners are LogisticRegression for regression task when CV is on."""
     LIGHT_GBM_REGRESSOR = "LightGBMRegressor"
     LINEAR_REGRESSION = "LinearRegression"
 
@@ -1194,18 +1350,26 @@ class Status(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     OPERATION_NOT_ENABLED_FOR_REGION = "OperationNotEnabledForRegion"
 
 
+class StatusMessageLevel(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """StatusMessageLevel."""
+
+    ERROR = "Error"
+    INFORMATION = "Information"
+    WARNING = "Warning"
+
+
 class StochasticOptimizer(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Stochastic optimizer for image models."""
 
-    #: No optimizer selected.
     NONE = "None"
-    #: Stochastic Gradient Descent optimizer.
+    """No optimizer selected."""
     SGD = "Sgd"
-    #: Adam is algorithm the optimizes stochastic objective functions based on adaptive estimates of
-    #: moments
+    """Stochastic Gradient Descent optimizer."""
     ADAM = "Adam"
-    #: AdamW is a variant of the optimizer Adam that has an improved implementation of weight decay.
+    """Adam is algorithm the optimizes stochastic objective functions based on adaptive estimates of
+    #: moments"""
     ADAMW = "Adamw"
+    """AdamW is a variant of the optimizer Adam that has an improved implementation of weight decay."""
 
 
 class StorageAccountType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -1218,8 +1382,8 @@ class StorageAccountType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 class TargetAggregationFunction(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Target aggregate function."""
 
-    #: Represent no value set.
     NONE = "None"
+    """Represent no value set."""
     SUM = "Sum"
     MAX = "Max"
     MIN = "Min"
@@ -1229,62 +1393,80 @@ class TargetAggregationFunction(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 class TargetLagsMode(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Target lags selection modes."""
 
-    #: Target lags to be determined automatically.
     AUTO = "Auto"
-    #: Use the custom target lags.
+    """Target lags to be determined automatically."""
     CUSTOM = "Custom"
+    """Use the custom target lags."""
 
 
 class TargetRollingWindowSizeMode(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Target rolling windows size mode."""
 
-    #: Determine rolling windows size automatically.
     AUTO = "Auto"
-    #: Use the specified rolling window size.
+    """Determine rolling windows size automatically."""
     CUSTOM = "Custom"
+    """Use the specified rolling window size."""
 
 
 class TaskType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """AutoMLJob Task type."""
 
-    #: Classification in machine learning and statistics is a supervised learning approach in which
-    #: the computer program learns from the data given to it and make new observations or
-    #: classifications.
     CLASSIFICATION = "Classification"
-    #: Regression means to predict the value using the input data. Regression models are used to
-    #: predict a continuous value.
+    """Classification in machine learning and statistics is a supervised learning approach in which
+    #: the computer program learns from the data given to it and make new observations or
+    #: classifications."""
     REGRESSION = "Regression"
-    #: Forecasting is a special kind of regression task that deals with time-series data and creates
-    #: forecasting model
-    #: that can be used to predict the near future values based on the inputs.
+    """Regression means to predict the value using the input data. Regression models are used to
+    #: predict a continuous value."""
     FORECASTING = "Forecasting"
-    #: Image Classification. Multi-class image classification is used when an image is classified with
+    """Forecasting is a special kind of regression task that deals with time-series data and creates
+    #: forecasting model
+    #: that can be used to predict the near future values based on the inputs."""
+    IMAGE_CLASSIFICATION = "ImageClassification"
+    """Image Classification. Multi-class image classification is used when an image is classified with
     #: only a single label
     #: from a set of classes - e.g. each image is classified as either an image of a 'cat' or a 'dog'
-    #: or a 'duck'.
-    IMAGE_CLASSIFICATION = "ImageClassification"
-    #: Image Classification Multilabel. Multi-label image classification is used when an image could
-    #: have one or more labels
-    #: from a set of labels - e.g. an image could be labeled with both 'cat' and 'dog'.
+    #: or a 'duck'."""
     IMAGE_CLASSIFICATION_MULTILABEL = "ImageClassificationMultilabel"
-    #: Image Object Detection. Object detection is used to identify objects in an image and locate
-    #: each object with a
-    #: bounding box e.g. locate all dogs and cats in an image and draw a bounding box around each.
+    """Image Classification Multilabel. Multi-label image classification is used when an image could
+    #: have one or more labels
+    #: from a set of labels - e.g. an image could be labeled with both 'cat' and 'dog'."""
     IMAGE_OBJECT_DETECTION = "ImageObjectDetection"
-    #: Image Instance Segmentation. Instance segmentation is used to identify objects in an image at
-    #: the pixel level,
-    #: drawing a polygon around each object in the image.
+    """Image Object Detection. Object detection is used to identify objects in an image and locate
+    #: each object with a
+    #: bounding box e.g. locate all dogs and cats in an image and draw a bounding box around each."""
     IMAGE_INSTANCE_SEGMENTATION = "ImageInstanceSegmentation"
-    #: Text classification (also known as text tagging or text categorization) is the process of
-    #: sorting texts into categories.
-    #: Categories are mutually exclusive.
+    """Image Instance Segmentation. Instance segmentation is used to identify objects in an image at
+    #: the pixel level,
+    #: drawing a polygon around each object in the image."""
     TEXT_CLASSIFICATION = "TextClassification"
-    #: Multilabel classification task assigns each sample to a group (zero or more) of target labels.
+    """Text classification (also known as text tagging or text categorization) is the process of
+    #: sorting texts into categories.
+    #: Categories are mutually exclusive."""
     TEXT_CLASSIFICATION_MULTILABEL = "TextClassificationMultilabel"
-    #: Text Named Entity Recognition a.k.a. TextNER.
-    #: Named Entity Recognition (NER) is the ability to take free-form text and identify the
-    #: occurrences of entities such as people, locations, organizations, and more.
+    """Multilabel classification task assigns each sample to a group (zero or more) of target labels."""
     TEXT_NER = "TextNER"
+    """Text Named Entity Recognition a.k.a. TextNER.
+    #: Named Entity Recognition (NER) is the ability to take free-form text and identify the
+    #: occurrences of entities such as people, locations, organizations, and more."""
+
+
+class TextAnnotationType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Annotation type of text data."""
+
+    CLASSIFICATION = "Classification"
+    NAMED_ENTITY_RECOGNITION = "NamedEntityRecognition"
+
+
+class TrainingMode(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Training mode dictates whether to use distributed training or not."""
+
+    AUTO = "Auto"
+    """Auto mode"""
+    DISTRIBUTED = "Distributed"
+    """Distributed training mode"""
+    NON_DISTRIBUTED = "NonDistributed"
+    """Non distributed training mode"""
 
 
 class TriggerType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -1316,8 +1498,8 @@ class UsageUnit(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 class UseStl(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Configure STL Decomposition of the time-series target column."""
 
-    #: No stl decomposition.
     NONE = "None"
+    """No stl decomposition."""
     SEASON = "Season"
     SEASON_TREND = "SeasonTrend"
 
@@ -1325,14 +1507,14 @@ class UseStl(str, Enum, metaclass=CaseInsensitiveEnumMeta):
 class ValidationMetricType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Metric computation method to use for validation metrics in image tasks."""
 
-    #: No metric.
     NONE = "None"
-    #: Coco metric.
+    """No metric."""
     COCO = "Coco"
-    #: Voc metric.
+    """Coco metric."""
     VOC = "Voc"
-    #: CocoVoc metric.
+    """Voc metric."""
     COCO_VOC = "CocoVoc"
+    """CocoVoc metric."""
 
 
 class ValueFormat(str, Enum, metaclass=CaseInsensitiveEnumMeta):
@@ -1363,20 +1545,29 @@ class VMTier(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     SPOT = "Spot"
 
 
+class VolumeDefinitionType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
+    """Type of Volume Definition. Possible Values: bind,volume,tmpfs,npipe."""
+
+    BIND = "bind"
+    VOLUME = "volume"
+    TMPFS = "tmpfs"
+    NPIPE = "npipe"
+
+
 class WeekDay(str, Enum, metaclass=CaseInsensitiveEnumMeta):
     """Enum of weekday."""
 
-    #: Monday weekday
     MONDAY = "Monday"
-    #: Tuesday weekday
+    """Monday weekday"""
     TUESDAY = "Tuesday"
-    #: Wednesday weekday
+    """Tuesday weekday"""
     WEDNESDAY = "Wednesday"
-    #: Thursday weekday
+    """Wednesday weekday"""
     THURSDAY = "Thursday"
-    #: Friday weekday
+    """Thursday weekday"""
     FRIDAY = "Friday"
-    #: Saturday weekday
+    """Friday weekday"""
     SATURDAY = "Saturday"
-    #: Sunday weekday
+    """Saturday weekday"""
     SUNDAY = "Sunday"
+    """Sunday weekday"""
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/models/_models_py3.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/models/_models_py3.py
index a0c8196e7297b..3618991521bb0 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/models/_models_py3.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/models/_models_py3.py
@@ -24,17 +24,166 @@
 JSON = MutableMapping[str, Any]  # pylint: disable=unsubscriptable-object
 
 
+class WorkspaceConnectionPropertiesV2(_serialization.Model):
+    """WorkspaceConnectionPropertiesV2.
+
+    You probably want to use the sub-classes and not this class directly. Known sub-classes are:
+    AccessKeyAuthTypeWorkspaceConnectionProperties,
+    ManagedIdentityAuthTypeWorkspaceConnectionProperties,
+    NoneAuthTypeWorkspaceConnectionProperties, PATAuthTypeWorkspaceConnectionProperties,
+    SASAuthTypeWorkspaceConnectionProperties,
+    ServicePrincipalAuthTypeWorkspaceConnectionProperties,
+    UsernamePasswordAuthTypeWorkspaceConnectionProperties
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar auth_type: Authentication type of the connection target. Required. Known values are:
+     "PAT", "ManagedIdentity", "UsernamePassword", "None", "SAS", "ServicePrincipal", and
+     "AccessKey".
+    :vartype auth_type: str or ~azure.mgmt.machinelearningservices.models.ConnectionAuthType
+    :ivar category: Category of the connection. Known values are: "PythonFeed",
+     "ContainerRegistry", "Git", "FeatureStore", "S3", "Snowflake", "AzureSqlDb", and
+     "AzureSynapseAnalytics".
+    :vartype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
+    :ivar target:
+    :vartype target: str
+    :ivar value: Value details of the workspace connection.
+    :vartype value: str
+    :ivar value_format: format for the workspace connection value. "JSON"
+    :vartype value_format: str or ~azure.mgmt.machinelearningservices.models.ValueFormat
+    """
+
+    _validation = {
+        "auth_type": {"required": True},
+    }
+
+    _attribute_map = {
+        "auth_type": {"key": "authType", "type": "str"},
+        "category": {"key": "category", "type": "str"},
+        "target": {"key": "target", "type": "str"},
+        "value": {"key": "value", "type": "str"},
+        "value_format": {"key": "valueFormat", "type": "str"},
+    }
+
+    _subtype_map = {
+        "auth_type": {
+            "AccessKey": "AccessKeyAuthTypeWorkspaceConnectionProperties",
+            "ManagedIdentity": "ManagedIdentityAuthTypeWorkspaceConnectionProperties",
+            "None": "NoneAuthTypeWorkspaceConnectionProperties",
+            "PAT": "PATAuthTypeWorkspaceConnectionProperties",
+            "SAS": "SASAuthTypeWorkspaceConnectionProperties",
+            "ServicePrincipal": "ServicePrincipalAuthTypeWorkspaceConnectionProperties",
+            "UsernamePassword": "UsernamePasswordAuthTypeWorkspaceConnectionProperties",
+        }
+    }
+
+    def __init__(
+        self,
+        *,
+        category: Optional[Union[str, "_models.ConnectionCategory"]] = None,
+        target: Optional[str] = None,
+        value: Optional[str] = None,
+        value_format: Optional[Union[str, "_models.ValueFormat"]] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword category: Category of the connection. Known values are: "PythonFeed",
+         "ContainerRegistry", "Git", "FeatureStore", "S3", "Snowflake", "AzureSqlDb", and
+         "AzureSynapseAnalytics".
+        :paramtype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
+        :keyword target:
+        :paramtype target: str
+        :keyword value: Value details of the workspace connection.
+        :paramtype value: str
+        :keyword value_format: format for the workspace connection value. "JSON"
+        :paramtype value_format: str or ~azure.mgmt.machinelearningservices.models.ValueFormat
+        """
+        super().__init__(**kwargs)
+        self.auth_type: Optional[str] = None
+        self.category = category
+        self.target = target
+        self.value = value
+        self.value_format = value_format
+
+
+class AccessKeyAuthTypeWorkspaceConnectionProperties(WorkspaceConnectionPropertiesV2):
+    """AccessKeyAuthTypeWorkspaceConnectionProperties.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar auth_type: Authentication type of the connection target. Required. Known values are:
+     "PAT", "ManagedIdentity", "UsernamePassword", "None", "SAS", "ServicePrincipal", and
+     "AccessKey".
+    :vartype auth_type: str or ~azure.mgmt.machinelearningservices.models.ConnectionAuthType
+    :ivar category: Category of the connection. Known values are: "PythonFeed",
+     "ContainerRegistry", "Git", "FeatureStore", "S3", "Snowflake", "AzureSqlDb", and
+     "AzureSynapseAnalytics".
+    :vartype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
+    :ivar target:
+    :vartype target: str
+    :ivar value: Value details of the workspace connection.
+    :vartype value: str
+    :ivar value_format: format for the workspace connection value. "JSON"
+    :vartype value_format: str or ~azure.mgmt.machinelearningservices.models.ValueFormat
+    :ivar credentials:
+    :vartype credentials: ~azure.mgmt.machinelearningservices.models.WorkspaceConnectionAccessKey
+    """
+
+    _validation = {
+        "auth_type": {"required": True},
+    }
+
+    _attribute_map = {
+        "auth_type": {"key": "authType", "type": "str"},
+        "category": {"key": "category", "type": "str"},
+        "target": {"key": "target", "type": "str"},
+        "value": {"key": "value", "type": "str"},
+        "value_format": {"key": "valueFormat", "type": "str"},
+        "credentials": {"key": "credentials", "type": "WorkspaceConnectionAccessKey"},
+    }
+
+    def __init__(
+        self,
+        *,
+        category: Optional[Union[str, "_models.ConnectionCategory"]] = None,
+        target: Optional[str] = None,
+        value: Optional[str] = None,
+        value_format: Optional[Union[str, "_models.ValueFormat"]] = None,
+        credentials: Optional["_models.WorkspaceConnectionAccessKey"] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword category: Category of the connection. Known values are: "PythonFeed",
+         "ContainerRegistry", "Git", "FeatureStore", "S3", "Snowflake", "AzureSqlDb", and
+         "AzureSynapseAnalytics".
+        :paramtype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
+        :keyword target:
+        :paramtype target: str
+        :keyword value: Value details of the workspace connection.
+        :paramtype value: str
+        :keyword value_format: format for the workspace connection value. "JSON"
+        :paramtype value_format: str or ~azure.mgmt.machinelearningservices.models.ValueFormat
+        :keyword credentials:
+        :paramtype credentials: ~azure.mgmt.machinelearningservices.models.WorkspaceConnectionAccessKey
+        """
+        super().__init__(category=category, target=target, value=value, value_format=value_format, **kwargs)
+        self.auth_type: str = "AccessKey"
+        self.credentials = credentials
+
+
 class DatastoreCredentials(_serialization.Model):
     """Base definition for datastore credentials.
 
     You probably want to use the sub-classes and not this class directly. Known sub-classes are:
-    AccountKeyDatastoreCredentials, CertificateDatastoreCredentials, NoneDatastoreCredentials,
-    SasDatastoreCredentials, ServicePrincipalDatastoreCredentials
+    AccountKeyDatastoreCredentials, CertificateDatastoreCredentials, KerberosKeytabCredentials,
+    KerberosPasswordCredentials, NoneDatastoreCredentials, SasDatastoreCredentials,
+    ServicePrincipalDatastoreCredentials
 
     All required parameters must be populated in order to send to Azure.
 
     :ivar credentials_type: [Required] Credential type used to authentication with storage.
-     Required. Known values are: "AccountKey", "Certificate", "None", "Sas", and "ServicePrincipal".
+     Required. Known values are: "AccountKey", "Certificate", "None", "Sas", "ServicePrincipal",
+     "KerberosKeytab", and "KerberosPassword".
     :vartype credentials_type: str or ~azure.mgmt.machinelearningservices.models.CredentialsType
     """
 
@@ -50,13 +199,15 @@ class DatastoreCredentials(_serialization.Model):
         "credentials_type": {
             "AccountKey": "AccountKeyDatastoreCredentials",
             "Certificate": "CertificateDatastoreCredentials",
+            "KerberosKeytab": "KerberosKeytabCredentials",
+            "KerberosPassword": "KerberosPasswordCredentials",
             "None": "NoneDatastoreCredentials",
             "Sas": "SasDatastoreCredentials",
             "ServicePrincipal": "ServicePrincipalDatastoreCredentials",
         }
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.credentials_type: Optional[str] = None
@@ -68,7 +219,8 @@ class AccountKeyDatastoreCredentials(DatastoreCredentials):
     All required parameters must be populated in order to send to Azure.
 
     :ivar credentials_type: [Required] Credential type used to authentication with storage.
-     Required. Known values are: "AccountKey", "Certificate", "None", "Sas", and "ServicePrincipal".
+     Required. Known values are: "AccountKey", "Certificate", "None", "Sas", "ServicePrincipal",
+     "KerberosKeytab", and "KerberosPassword".
     :vartype credentials_type: str or ~azure.mgmt.machinelearningservices.models.CredentialsType
     :ivar secrets: [Required] Storage account secrets. Required.
     :vartype secrets: ~azure.mgmt.machinelearningservices.models.AccountKeyDatastoreSecrets
@@ -84,7 +236,7 @@ class AccountKeyDatastoreCredentials(DatastoreCredentials):
         "secrets": {"key": "secrets", "type": "AccountKeyDatastoreSecrets"},
     }
 
-    def __init__(self, *, secrets: "_models.AccountKeyDatastoreSecrets", **kwargs):
+    def __init__(self, *, secrets: "_models.AccountKeyDatastoreSecrets", **kwargs: Any) -> None:
         """
         :keyword secrets: [Required] Storage account secrets. Required.
         :paramtype secrets: ~azure.mgmt.machinelearningservices.models.AccountKeyDatastoreSecrets
@@ -98,13 +250,14 @@ class DatastoreSecrets(_serialization.Model):
     """Base definition for datastore secrets.
 
     You probably want to use the sub-classes and not this class directly. Known sub-classes are:
-    AccountKeyDatastoreSecrets, CertificateDatastoreSecrets, SasDatastoreSecrets,
-    ServicePrincipalDatastoreSecrets
+    AccountKeyDatastoreSecrets, CertificateDatastoreSecrets, KerberosKeytabSecrets,
+    KerberosPasswordSecrets, SasDatastoreSecrets, ServicePrincipalDatastoreSecrets
 
     All required parameters must be populated in order to send to Azure.
 
     :ivar secrets_type: [Required] Credential type used to authentication with storage. Required.
-     Known values are: "AccountKey", "Certificate", "Sas", and "ServicePrincipal".
+     Known values are: "AccountKey", "Certificate", "Sas", "ServicePrincipal", "KerberosPassword",
+     and "KerberosKeytab".
     :vartype secrets_type: str or ~azure.mgmt.machinelearningservices.models.SecretsType
     """
 
@@ -120,12 +273,14 @@ class DatastoreSecrets(_serialization.Model):
         "secrets_type": {
             "AccountKey": "AccountKeyDatastoreSecrets",
             "Certificate": "CertificateDatastoreSecrets",
+            "KerberosKeytab": "KerberosKeytabSecrets",
+            "KerberosPassword": "KerberosPasswordSecrets",
             "Sas": "SasDatastoreSecrets",
             "ServicePrincipal": "ServicePrincipalDatastoreSecrets",
         }
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.secrets_type: Optional[str] = None
@@ -137,7 +292,8 @@ class AccountKeyDatastoreSecrets(DatastoreSecrets):
     All required parameters must be populated in order to send to Azure.
 
     :ivar secrets_type: [Required] Credential type used to authentication with storage. Required.
-     Known values are: "AccountKey", "Certificate", "Sas", and "ServicePrincipal".
+     Known values are: "AccountKey", "Certificate", "Sas", "ServicePrincipal", "KerberosPassword",
+     and "KerberosKeytab".
     :vartype secrets_type: str or ~azure.mgmt.machinelearningservices.models.SecretsType
     :ivar key: Storage account key.
     :vartype key: str
@@ -152,7 +308,7 @@ class AccountKeyDatastoreSecrets(DatastoreSecrets):
         "key": {"key": "key", "type": "str"},
     }
 
-    def __init__(self, *, key: Optional[str] = None, **kwargs):
+    def __init__(self, *, key: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword key: Storage account key.
         :paramtype key: str
@@ -162,6 +318,42 @@ def __init__(self, *, key: Optional[str] = None, **kwargs):
         self.key = key
 
 
+class AcrDetails(_serialization.Model):
+    """Details of ACR account to be used for the Registry.
+
+    :ivar system_created_acr_account:
+    :vartype system_created_acr_account:
+     ~azure.mgmt.machinelearningservices.models.SystemCreatedAcrAccount
+    :ivar user_created_acr_account:
+    :vartype user_created_acr_account:
+     ~azure.mgmt.machinelearningservices.models.UserCreatedAcrAccount
+    """
+
+    _attribute_map = {
+        "system_created_acr_account": {"key": "systemCreatedAcrAccount", "type": "SystemCreatedAcrAccount"},
+        "user_created_acr_account": {"key": "userCreatedAcrAccount", "type": "UserCreatedAcrAccount"},
+    }
+
+    def __init__(
+        self,
+        *,
+        system_created_acr_account: Optional["_models.SystemCreatedAcrAccount"] = None,
+        user_created_acr_account: Optional["_models.UserCreatedAcrAccount"] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword system_created_acr_account:
+        :paramtype system_created_acr_account:
+         ~azure.mgmt.machinelearningservices.models.SystemCreatedAcrAccount
+        :keyword user_created_acr_account:
+        :paramtype user_created_acr_account:
+         ~azure.mgmt.machinelearningservices.models.UserCreatedAcrAccount
+        """
+        super().__init__(**kwargs)
+        self.system_created_acr_account = system_created_acr_account
+        self.user_created_acr_account = user_created_acr_account
+
+
 class AKSSchema(_serialization.Model):
     """AKSSchema.
 
@@ -173,7 +365,7 @@ class AKSSchema(_serialization.Model):
         "properties": {"key": "properties", "type": "AKSSchemaProperties"},
     }
 
-    def __init__(self, *, properties: Optional["_models.AKSSchemaProperties"] = None, **kwargs):
+    def __init__(self, *, properties: Optional["_models.AKSSchemaProperties"] = None, **kwargs: Any) -> None:
         """
         :keyword properties: AKS properties.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.AKSSchemaProperties
@@ -201,7 +393,7 @@ class Compute(_serialization.Model):
     :vartype compute_location: str
     :ivar provisioning_state: The provision state of the cluster. Valid values are Unknown,
      Updating, Provisioning, Succeeded, and Failed. Known values are: "Unknown", "Updating",
-     "Creating", "Deleting", "Succeeded", "Failed", and "Canceled".
+     "Creating", "Deleting", "Succeeded", "Failed", "Canceled", and "SoftDeleted".
     :vartype provisioning_state: str or
      ~azure.mgmt.machinelearningservices.models.ProvisioningState
     :ivar description: The description of the Machine Learning compute.
@@ -266,8 +458,8 @@ def __init__(
         description: Optional[str] = None,
         resource_id: Optional[str] = None,
         disable_local_auth: Optional[bool] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword compute_location: Location for the underlying compute.
         :paramtype compute_location: str
@@ -309,7 +501,7 @@ class AKS(Compute, AKSSchema):  # pylint: disable=too-many-instance-attributes
     :vartype compute_location: str
     :ivar provisioning_state: The provision state of the cluster. Valid values are Unknown,
      Updating, Provisioning, Succeeded, and Failed. Known values are: "Unknown", "Updating",
-     "Creating", "Deleting", "Succeeded", "Failed", and "Canceled".
+     "Creating", "Deleting", "Succeeded", "Failed", "Canceled", and "SoftDeleted".
     :vartype provisioning_state: str or
      ~azure.mgmt.machinelearningservices.models.ProvisioningState
     :ivar description: The description of the Machine Learning compute.
@@ -361,8 +553,8 @@ def __init__(
         description: Optional[str] = None,
         resource_id: Optional[str] = None,
         disable_local_auth: Optional[bool] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword properties: AKS properties.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.AKSSchemaProperties
@@ -422,8 +614,8 @@ def __init__(
         user_kube_config: Optional[str] = None,
         admin_kube_config: Optional[str] = None,
         image_pull_secret_name: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword user_kube_config: Content of kubeconfig file that can be used to connect to the
          Kubernetes cluster.
@@ -470,7 +662,7 @@ class ComputeSecrets(_serialization.Model):
         }
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.compute_type: Optional[str] = None
@@ -512,8 +704,8 @@ def __init__(
         user_kube_config: Optional[str] = None,
         admin_kube_config: Optional[str] = None,
         image_pull_secret_name: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword user_kube_config: Content of kubeconfig file that can be used to connect to the
          Kubernetes cluster.
@@ -574,8 +766,8 @@ def __init__(
         service_cidr: Optional[str] = None,
         dns_service_ip: Optional[str] = None,
         docker_bridge_cidr: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword subnet_id: Virtual network subnet resource ID the compute nodes belong to.
         :paramtype subnet_id: str
@@ -652,8 +844,8 @@ def __init__(
         aks_networking_configuration: Optional["_models.AksNetworkingConfiguration"] = None,
         load_balancer_type: Union[str, "_models.LoadBalancerType"] = "PublicIp",
         load_balancer_subnet: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword cluster_fqdn: Cluster full qualified domain name.
         :paramtype cluster_fqdn: str
@@ -688,6 +880,59 @@ def __init__(
         self.load_balancer_subnet = load_balancer_subnet
 
 
+class Nodes(_serialization.Model):
+    """Abstract Nodes definition.
+
+    You probably want to use the sub-classes and not this class directly. Known sub-classes are:
+    AllNodes
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar nodes_value_type: [Required] Type of the Nodes value. Required. Known values are: "All"
+     and "Custom".
+    :vartype nodes_value_type: str or ~azure.mgmt.machinelearningservices.models.NodesValueType
+    """
+
+    _validation = {
+        "nodes_value_type": {"required": True},
+    }
+
+    _attribute_map = {
+        "nodes_value_type": {"key": "nodesValueType", "type": "str"},
+    }
+
+    _subtype_map = {"nodes_value_type": {"All": "AllNodes"}}
+
+    def __init__(self, **kwargs: Any) -> None:
+        """ """
+        super().__init__(**kwargs)
+        self.nodes_value_type: Optional[str] = None
+
+
+class AllNodes(Nodes):
+    """All nodes means the service will be running on all of the nodes of the job.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar nodes_value_type: [Required] Type of the Nodes value. Required. Known values are: "All"
+     and "Custom".
+    :vartype nodes_value_type: str or ~azure.mgmt.machinelearningservices.models.NodesValueType
+    """
+
+    _validation = {
+        "nodes_value_type": {"required": True},
+    }
+
+    _attribute_map = {
+        "nodes_value_type": {"key": "nodesValueType", "type": "str"},
+    }
+
+    def __init__(self, **kwargs: Any) -> None:
+        """ """
+        super().__init__(**kwargs)
+        self.nodes_value_type: str = "All"
+
+
 class AmlComputeSchema(_serialization.Model):
     """Properties(top level) of AmlCompute.
 
@@ -699,7 +944,7 @@ class AmlComputeSchema(_serialization.Model):
         "properties": {"key": "properties", "type": "AmlComputeProperties"},
     }
 
-    def __init__(self, *, properties: Optional["_models.AmlComputeProperties"] = None, **kwargs):
+    def __init__(self, *, properties: Optional["_models.AmlComputeProperties"] = None, **kwargs: Any) -> None:
         """
         :keyword properties: Properties of AmlCompute.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.AmlComputeProperties
@@ -725,7 +970,7 @@ class AmlCompute(Compute, AmlComputeSchema):  # pylint: disable=too-many-instanc
     :vartype compute_location: str
     :ivar provisioning_state: The provision state of the cluster. Valid values are Unknown,
      Updating, Provisioning, Succeeded, and Failed. Known values are: "Unknown", "Updating",
-     "Creating", "Deleting", "Succeeded", "Failed", and "Canceled".
+     "Creating", "Deleting", "Succeeded", "Failed", "Canceled", and "SoftDeleted".
     :vartype provisioning_state: str or
      ~azure.mgmt.machinelearningservices.models.ProvisioningState
     :ivar description: The description of the Machine Learning compute.
@@ -777,8 +1022,8 @@ def __init__(
         description: Optional[str] = None,
         resource_id: Optional[str] = None,
         disable_local_auth: Optional[bool] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword properties: Properties of AmlCompute.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.AmlComputeProperties
@@ -852,7 +1097,7 @@ class AmlComputeNodeInformation(_serialization.Model):
         "run_id": {"key": "runId", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.node_id = None
@@ -884,7 +1129,7 @@ class AmlComputeNodesInformation(_serialization.Model):
         "next_link": {"key": "nextLink", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.nodes = None
@@ -996,8 +1241,8 @@ def __init__(
         remote_login_port_public_access: Union[str, "_models.RemoteLoginPortPublicAccess"] = "NotSpecified",
         enable_node_public_ip: bool = True,
         property_bag: Optional[JSON] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword os_type: Compute OS Type. Known values are: "Linux" and "Windows".
         :paramtype os_type: str or ~azure.mgmt.machinelearningservices.models.OsType
@@ -1057,7 +1302,7 @@ def __init__(
 
 
 class AmlOperation(_serialization.Model):
-    """Azure Machine Learning workspace REST API operation.
+    """Azure Machine Learning REST API operation.
 
     :ivar name: Operation name: {provider}/{resource}/{operation}.
     :vartype name: str
@@ -1079,8 +1324,8 @@ def __init__(
         name: Optional[str] = None,
         display: Optional["_models.AmlOperationDisplay"] = None,
         is_data_action: Optional[bool] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword name: Operation name: {provider}/{resource}/{operation}.
         :paramtype name: str
@@ -1122,8 +1367,8 @@ def __init__(
         resource: Optional[str] = None,
         operation: Optional[str] = None,
         description: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword provider: The resource provider name: Microsoft.MachineLearningExperimentation.
         :paramtype provider: str
@@ -1144,7 +1389,7 @@ def __init__(
 class AmlOperationListResult(_serialization.Model):
     """An array of operations supported by the resource provider.
 
-    :ivar value: List of AML workspace operations supported by the AML workspace resource provider.
+    :ivar value: List of AML operations supported by the AML resource provider.
     :vartype value: list[~azure.mgmt.machinelearningservices.models.AmlOperation]
     """
 
@@ -1152,10 +1397,9 @@ class AmlOperationListResult(_serialization.Model):
         "value": {"key": "value", "type": "[AmlOperation]"},
     }
 
-    def __init__(self, *, value: Optional[List["_models.AmlOperation"]] = None, **kwargs):
+    def __init__(self, *, value: Optional[List["_models.AmlOperation"]] = None, **kwargs: Any) -> None:
         """
-        :keyword value: List of AML workspace operations supported by the AML workspace resource
-         provider.
+        :keyword value: List of AML operations supported by the AML resource provider.
         :paramtype value: list[~azure.mgmt.machinelearningservices.models.AmlOperation]
         """
         super().__init__(**kwargs)
@@ -1188,7 +1432,7 @@ class IdentityConfiguration(_serialization.Model):
         "identity_type": {"AMLToken": "AmlToken", "Managed": "ManagedIdentity", "UserIdentity": "UserIdentity"}
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.identity_type: Optional[str] = None
@@ -1213,7 +1457,7 @@ class AmlToken(IdentityConfiguration):
         "identity_type": {"key": "identityType", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.identity_type: str = "AMLToken"
@@ -1242,8 +1486,8 @@ def __init__(
         id: Optional[str] = None,  # pylint: disable=redefined-builtin
         display_name: Optional[str] = None,
         description: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword id: Specifies the feature ID.
         :paramtype id: str
@@ -1258,6 +1502,32 @@ def __init__(
         self.description = description
 
 
+class ArmResourceId(_serialization.Model):
+    """ARM ResourceId of a resource.
+
+    :ivar resource_id: Arm ResourceId is in the format
+     "/subscriptions/{SubscriptionId}/resourceGroups/{ResourceGroupName}/providers/Microsoft.Storage/storageAccounts/{StorageAccountName}"
+     or
+     "/subscriptions/{SubscriptionId}/resourceGroups/{ResourceGroupName}/providers/Microsoft.ContainerRegistry/registries/{AcrName}".
+    :vartype resource_id: str
+    """
+
+    _attribute_map = {
+        "resource_id": {"key": "resourceId", "type": "str"},
+    }
+
+    def __init__(self, *, resource_id: Optional[str] = None, **kwargs: Any) -> None:
+        """
+        :keyword resource_id: Arm ResourceId is in the format
+         "/subscriptions/{SubscriptionId}/resourceGroups/{ResourceGroupName}/providers/Microsoft.Storage/storageAccounts/{StorageAccountName}"
+         or
+         "/subscriptions/{SubscriptionId}/resourceGroups/{ResourceGroupName}/providers/Microsoft.ContainerRegistry/registries/{AcrName}".
+        :paramtype resource_id: str
+        """
+        super().__init__(**kwargs)
+        self.resource_id = resource_id
+
+
 class ResourceBase(_serialization.Model):
     """ResourceBase.
 
@@ -1281,8 +1551,8 @@ def __init__(
         description: Optional[str] = None,
         properties: Optional[Dict[str, str]] = None,
         tags: Optional[Dict[str, str]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -1328,8 +1598,8 @@ def __init__(
         tags: Optional[Dict[str, str]] = None,
         is_anonymous: bool = False,
         is_archived: bool = False,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -1387,8 +1657,8 @@ def __init__(
         properties: Optional[Dict[str, str]] = None,
         tags: Optional[Dict[str, str]] = None,
         is_archived: bool = False,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -1426,7 +1696,9 @@ class AssetJobInput(_serialization.Model):
         "uri": {"key": "uri", "type": "str"},
     }
 
-    def __init__(self, *, uri: str, mode: Optional[Union[str, "_models.InputDeliveryMode"]] = None, **kwargs):
+    def __init__(
+        self, *, uri: str, mode: Optional[Union[str, "_models.InputDeliveryMode"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword mode: Input Asset Delivery Mode. Known values are: "ReadOnlyMount", "ReadWriteMount",
          "Download", "Direct", "EvalMount", and "EvalDownload".
@@ -1442,27 +1714,47 @@ def __init__(self, *, uri: str, mode: Optional[Union[str, "_models.InputDelivery
 class AssetJobOutput(_serialization.Model):
     """Asset output type.
 
-    :ivar mode: Output Asset Delivery Mode. Known values are: "ReadWriteMount" and "Upload".
+    :ivar asset_name: Output Asset Name.
+    :vartype asset_name: str
+    :ivar asset_version: Output Asset Version.
+    :vartype asset_version: str
+    :ivar mode: Output Asset Delivery Mode. Known values are: "ReadWriteMount", "Upload", and
+     "Direct".
     :vartype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
     :ivar uri: Output Asset URI.
     :vartype uri: str
     """
 
     _attribute_map = {
+        "asset_name": {"key": "assetName", "type": "str"},
+        "asset_version": {"key": "assetVersion", "type": "str"},
         "mode": {"key": "mode", "type": "str"},
         "uri": {"key": "uri", "type": "str"},
     }
 
     def __init__(
-        self, *, mode: Optional[Union[str, "_models.OutputDeliveryMode"]] = None, uri: Optional[str] = None, **kwargs
-    ):
-        """
-        :keyword mode: Output Asset Delivery Mode. Known values are: "ReadWriteMount" and "Upload".
+        self,
+        *,
+        asset_name: Optional[str] = None,
+        asset_version: Optional[str] = None,
+        mode: Optional[Union[str, "_models.OutputDeliveryMode"]] = None,
+        uri: Optional[str] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword asset_name: Output Asset Name.
+        :paramtype asset_name: str
+        :keyword asset_version: Output Asset Version.
+        :paramtype asset_version: str
+        :keyword mode: Output Asset Delivery Mode. Known values are: "ReadWriteMount", "Upload", and
+         "Direct".
         :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
         :keyword uri: Output Asset URI.
         :paramtype uri: str
         """
         super().__init__(**kwargs)
+        self.asset_name = asset_name
+        self.asset_version = asset_version
         self.mode = mode
         self.uri = uri
 
@@ -1496,7 +1788,7 @@ class AssetReferenceBase(_serialization.Model):
         }
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.reference_type: Optional[str] = None
@@ -1523,7 +1815,7 @@ class AssignedUser(_serialization.Model):
         "tenant_id": {"key": "tenantId", "type": "str"},
     }
 
-    def __init__(self, *, object_id: str, tenant_id: str, **kwargs):
+    def __init__(self, *, object_id: str, tenant_id: str, **kwargs: Any) -> None:
         """
         :keyword object_id: User’s AAD Object Id. Required.
         :paramtype object_id: str
@@ -1558,7 +1850,7 @@ class ForecastHorizon(_serialization.Model):
 
     _subtype_map = {"mode": {"Auto": "AutoForecastHorizon", "Custom": "CustomForecastHorizon"}}
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.mode: Optional[str] = None
@@ -1582,17 +1874,47 @@ class AutoForecastHorizon(ForecastHorizon):
         "mode": {"key": "mode", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.mode: str = "Auto"
 
 
+class AutologgerSettings(_serialization.Model):
+    """Settings for Autologger.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar mlflow_autologger: [Required] Indicates whether mlflow autologger is enabled. Required.
+     Known values are: "Enabled" and "Disabled".
+    :vartype mlflow_autologger: str or
+     ~azure.mgmt.machinelearningservices.models.MLFlowAutologgerState
+    """
+
+    _validation = {
+        "mlflow_autologger": {"required": True},
+    }
+
+    _attribute_map = {
+        "mlflow_autologger": {"key": "mlflowAutologger", "type": "str"},
+    }
+
+    def __init__(self, *, mlflow_autologger: Union[str, "_models.MLFlowAutologgerState"], **kwargs: Any) -> None:
+        """
+        :keyword mlflow_autologger: [Required] Indicates whether mlflow autologger is enabled.
+         Required. Known values are: "Enabled" and "Disabled".
+        :paramtype mlflow_autologger: str or
+         ~azure.mgmt.machinelearningservices.models.MLFlowAutologgerState
+        """
+        super().__init__(**kwargs)
+        self.mlflow_autologger = mlflow_autologger
+
+
 class JobBaseProperties(ResourceBase):  # pylint: disable=too-many-instance-attributes
     """Base definition for a job.
 
     You probably want to use the sub-classes and not this class directly. Known sub-classes are:
-    AutoMLJob, CommandJob, PipelineJob, SweepJob
+    AutoMLJob, CommandJob, LabelingJobProperties, PipelineJob, SparkJob, SweepJob
 
     Variables are only populated by the server, and will be ignored when sending a request.
 
@@ -1620,14 +1942,14 @@ class JobBaseProperties(ResourceBase):  # pylint: disable=too-many-instance-attr
     :ivar is_archived: Is the asset archived?.
     :vartype is_archived: bool
     :ivar job_type: [Required] Specifies the type of job. Required. Known values are: "AutoML",
-     "Command", "Sweep", and "Pipeline".
+     "Command", "Labeling", "Sweep", "Pipeline", and "Spark".
     :vartype job_type: str or ~azure.mgmt.machinelearningservices.models.JobType
     :ivar services: List of JobEndpoints.
      For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
     :vartype services: dict[str, ~azure.mgmt.machinelearningservices.models.JobService]
     :ivar status: Status of the job. Known values are: "NotStarted", "Starting", "Provisioning",
      "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed", "Failed",
-     "Canceled", "NotResponding", "Paused", and "Unknown".
+     "Canceled", "NotResponding", "Paused", "Unknown", and "Scheduled".
     :vartype status: str or ~azure.mgmt.machinelearningservices.models.JobStatus
     """
 
@@ -1652,7 +1974,14 @@ class JobBaseProperties(ResourceBase):  # pylint: disable=too-many-instance-attr
     }
 
     _subtype_map = {
-        "job_type": {"AutoML": "AutoMLJob", "Command": "CommandJob", "Pipeline": "PipelineJob", "Sweep": "SweepJob"}
+        "job_type": {
+            "AutoML": "AutoMLJob",
+            "Command": "CommandJob",
+            "Labeling": "LabelingJobProperties",
+            "Pipeline": "PipelineJob",
+            "Spark": "SparkJob",
+            "Sweep": "SweepJob",
+        }
     }
 
     def __init__(
@@ -1668,8 +1997,8 @@ def __init__(
         identity: Optional["_models.IdentityConfiguration"] = None,
         is_archived: bool = False,
         services: Optional[Dict[str, "_models.JobService"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -1713,54 +2042,54 @@ class AutoMLJob(JobBaseProperties):  # pylint: disable=too-many-instance-attribu
     Use this class for executing AutoML tasks like Classification/Regression etc.
     See TaskType enum for all the tasks supported.
 
-        Variables are only populated by the server, and will be ignored when sending a request.
-
-        All required parameters must be populated in order to send to Azure.
-
-        :ivar description: The asset description text.
-        :vartype description: str
-        :ivar properties: The asset property dictionary.
-        :vartype properties: dict[str, str]
-        :ivar tags: Tag dictionary. Tags can be added, removed, and updated.
-        :vartype tags: dict[str, str]
-        :ivar component_id: ARM resource ID of the component resource.
-        :vartype component_id: str
-        :ivar compute_id: ARM resource ID of the compute resource.
-        :vartype compute_id: str
-        :ivar display_name: Display name of job.
-        :vartype display_name: str
-        :ivar experiment_name: The name of the experiment the job belongs to. If not set, the job is
-         placed in the "Default" experiment.
-        :vartype experiment_name: str
-        :ivar identity: Identity configuration. If set, this should be one of AmlToken,
-         ManagedIdentity, UserIdentity or null.
-         Defaults to AmlToken if null.
-        :vartype identity: ~azure.mgmt.machinelearningservices.models.IdentityConfiguration
-        :ivar is_archived: Is the asset archived?.
-        :vartype is_archived: bool
-        :ivar job_type: [Required] Specifies the type of job. Required. Known values are: "AutoML",
-         "Command", "Sweep", and "Pipeline".
-        :vartype job_type: str or ~azure.mgmt.machinelearningservices.models.JobType
-        :ivar services: List of JobEndpoints.
-         For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
-        :vartype services: dict[str, ~azure.mgmt.machinelearningservices.models.JobService]
-        :ivar status: Status of the job. Known values are: "NotStarted", "Starting", "Provisioning",
-         "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed", "Failed",
-         "Canceled", "NotResponding", "Paused", and "Unknown".
-        :vartype status: str or ~azure.mgmt.machinelearningservices.models.JobStatus
-        :ivar environment_id: The ARM resource ID of the Environment specification for the job.
-         This is optional value to provide, if not provided, AutoML will default this to Production
-         AutoML curated environment version when running the job.
-        :vartype environment_id: str
-        :ivar environment_variables: Environment variables included in the job.
-        :vartype environment_variables: dict[str, str]
-        :ivar outputs: Mapping of output data bindings used in the job.
-        :vartype outputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobOutput]
-        :ivar resources: Compute Resource configuration for the job.
-        :vartype resources: ~azure.mgmt.machinelearningservices.models.JobResourceConfiguration
-        :ivar task_details: [Required] This represents scenario which can be one of Tables/NLP/Image.
-         Required.
-        :vartype task_details: ~azure.mgmt.machinelearningservices.models.AutoMLVertical
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar component_id: ARM resource ID of the component resource.
+    :vartype component_id: str
+    :ivar compute_id: ARM resource ID of the compute resource.
+    :vartype compute_id: str
+    :ivar display_name: Display name of job.
+    :vartype display_name: str
+    :ivar experiment_name: The name of the experiment the job belongs to. If not set, the job is
+     placed in the "Default" experiment.
+    :vartype experiment_name: str
+    :ivar identity: Identity configuration. If set, this should be one of AmlToken,
+     ManagedIdentity, UserIdentity or null.
+     Defaults to AmlToken if null.
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.IdentityConfiguration
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar job_type: [Required] Specifies the type of job. Required. Known values are: "AutoML",
+     "Command", "Labeling", "Sweep", "Pipeline", and "Spark".
+    :vartype job_type: str or ~azure.mgmt.machinelearningservices.models.JobType
+    :ivar services: List of JobEndpoints.
+     For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
+    :vartype services: dict[str, ~azure.mgmt.machinelearningservices.models.JobService]
+    :ivar status: Status of the job. Known values are: "NotStarted", "Starting", "Provisioning",
+     "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed", "Failed",
+     "Canceled", "NotResponding", "Paused", "Unknown", and "Scheduled".
+    :vartype status: str or ~azure.mgmt.machinelearningservices.models.JobStatus
+    :ivar environment_id: The ARM resource ID of the Environment specification for the job.
+     This is optional value to provide, if not provided, AutoML will default this to Production
+     AutoML curated environment version when running the job.
+    :vartype environment_id: str
+    :ivar environment_variables: Environment variables included in the job.
+    :vartype environment_variables: dict[str, str]
+    :ivar outputs: Mapping of output data bindings used in the job.
+    :vartype outputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobOutput]
+    :ivar resources: Compute Resource configuration for the job.
+    :vartype resources: ~azure.mgmt.machinelearningservices.models.JobResourceConfiguration
+    :ivar task_details: [Required] This represents scenario which can be one of Tables/NLP/Image.
+     Required.
+    :vartype task_details: ~azure.mgmt.machinelearningservices.models.AutoMLVertical
     """
 
     _validation = {
@@ -1807,8 +2136,8 @@ def __init__(
         environment_variables: Optional[Dict[str, str]] = None,
         outputs: Optional[Dict[str, "_models.JobOutput"]] = None,
         resources: Optional["_models.JobResourceConfiguration"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -1873,26 +2202,26 @@ class AutoMLVertical(_serialization.Model):
     """AutoML vertical class.
     Base class for AutoML verticals - TableVertical/ImageVertical/NLPVertical.
 
-        You probably want to use the sub-classes and not this class directly. Known sub-classes are:
-        Classification, Forecasting, ImageClassification, ImageClassificationMultilabel,
-        ImageInstanceSegmentation, ImageObjectDetection, Regression, TextClassification,
-        TextClassificationMultilabel, TextNer
+    You probably want to use the sub-classes and not this class directly. Known sub-classes are:
+    Classification, Forecasting, ImageClassification, ImageClassificationMultilabel,
+    ImageInstanceSegmentation, ImageObjectDetection, Regression, TextClassification,
+    TextClassificationMultilabel, TextNer
 
-        All required parameters must be populated in order to send to Azure.
+    All required parameters must be populated in order to send to Azure.
 
-        :ivar log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
-         "Warning", "Error", and "Critical".
-        :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
-        :ivar target_column_name: Target column name: This is prediction values column.
-         Also known as label column name in context of classification tasks.
-        :vartype target_column_name: str
-        :ivar task_type: [Required] Task type for AutoMLJob. Required. Known values are:
-         "Classification", "Regression", "Forecasting", "ImageClassification",
-         "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
-         "TextClassification", "TextClassificationMultilabel", and "TextNER".
-        :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
-        :ivar training_data: [Required] Training data input. Required.
-        :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
+     "Warning", "Error", and "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: [Required] Task type for AutoMLJob. Required. Known values are:
+     "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", and "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: [Required] Training data input. Required.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
     """
 
     _validation = {
@@ -1928,8 +2257,8 @@ def __init__(
         training_data: "_models.MLTableJobInput",
         log_verbosity: Optional[Union[str, "_models.LogVerbosity"]] = None,
         target_column_name: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
          "Warning", "Error", and "Critical".
@@ -1970,7 +2299,7 @@ class NCrossValidations(_serialization.Model):
 
     _subtype_map = {"mode": {"Auto": "AutoNCrossValidations", "Custom": "CustomNCrossValidations"}}
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.mode: Optional[str] = None
@@ -1994,7 +2323,7 @@ class AutoNCrossValidations(NCrossValidations):
         "mode": {"key": "mode", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.mode: str = "Auto"
@@ -2014,7 +2343,9 @@ class AutoPauseProperties(_serialization.Model):
         "enabled": {"key": "enabled", "type": "bool"},
     }
 
-    def __init__(self, *, delay_in_minutes: Optional[int] = None, enabled: Optional[bool] = None, **kwargs):
+    def __init__(
+        self, *, delay_in_minutes: Optional[int] = None, enabled: Optional[bool] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword delay_in_minutes:
         :paramtype delay_in_minutes: int
@@ -2049,8 +2380,8 @@ def __init__(
         min_node_count: Optional[int] = None,
         enabled: Optional[bool] = None,
         max_node_count: Optional[int] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword min_node_count:
         :paramtype min_node_count: int
@@ -2087,7 +2418,7 @@ class Seasonality(_serialization.Model):
 
     _subtype_map = {"mode": {"Auto": "AutoSeasonality", "Custom": "CustomSeasonality"}}
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.mode: Optional[str] = None
@@ -2110,7 +2441,7 @@ class AutoSeasonality(Seasonality):
         "mode": {"key": "mode", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.mode: str = "Auto"
@@ -2139,7 +2470,7 @@ class TargetLags(_serialization.Model):
 
     _subtype_map = {"mode": {"Auto": "AutoTargetLags", "Custom": "CustomTargetLags"}}
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.mode: Optional[str] = None
@@ -2163,7 +2494,7 @@ class AutoTargetLags(TargetLags):
         "mode": {"key": "mode", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.mode: str = "Auto"
@@ -2192,7 +2523,7 @@ class TargetRollingWindowSize(_serialization.Model):
 
     _subtype_map = {"mode": {"Auto": "AutoTargetRollingWindowSize", "Custom": "CustomTargetRollingWindowSize"}}
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.mode: Optional[str] = None
@@ -2216,7 +2547,7 @@ class AutoTargetRollingWindowSize(TargetRollingWindowSize):
         "mode": {"key": "mode", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.mode: str = "Auto"
@@ -2226,7 +2557,8 @@ class DatastoreProperties(ResourceBase):
     """Base definition for datastore contents configuration.
 
     You probably want to use the sub-classes and not this class directly. Known sub-classes are:
-    AzureBlobDatastore, AzureDataLakeGen1Datastore, AzureDataLakeGen2Datastore, AzureFileDatastore
+    AzureBlobDatastore, AzureDataLakeGen1Datastore, AzureDataLakeGen2Datastore, AzureFileDatastore,
+    HdfsDatastore
 
     Variables are only populated by the server, and will be ignored when sending a request.
 
@@ -2241,7 +2573,7 @@ class DatastoreProperties(ResourceBase):
     :ivar credentials: [Required] Account credentials. Required.
     :vartype credentials: ~azure.mgmt.machinelearningservices.models.DatastoreCredentials
     :ivar datastore_type: [Required] Storage type backing the datastore. Required. Known values
-     are: "AzureBlob", "AzureDataLakeGen1", "AzureDataLakeGen2", and "AzureFile".
+     are: "AzureBlob", "AzureDataLakeGen1", "AzureDataLakeGen2", "AzureFile", and "Hdfs".
     :vartype datastore_type: str or ~azure.mgmt.machinelearningservices.models.DatastoreType
     :ivar is_default: Readonly property to indicate if datastore is the workspace default
      datastore.
@@ -2269,6 +2601,7 @@ class DatastoreProperties(ResourceBase):
             "AzureDataLakeGen1": "AzureDataLakeGen1Datastore",
             "AzureDataLakeGen2": "AzureDataLakeGen2Datastore",
             "AzureFile": "AzureFileDatastore",
+            "Hdfs": "HdfsDatastore",
         }
     }
 
@@ -2279,8 +2612,8 @@ def __init__(
         description: Optional[str] = None,
         properties: Optional[Dict[str, str]] = None,
         tags: Optional[Dict[str, str]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -2297,7 +2630,35 @@ def __init__(
         self.is_default = None
 
 
-class AzureBlobDatastore(DatastoreProperties):  # pylint: disable=too-many-instance-attributes
+class AzureDatastore(_serialization.Model):
+    """Base definition for Azure datastore contents configuration.
+
+    :ivar resource_group: Azure Resource Group name.
+    :vartype resource_group: str
+    :ivar subscription_id: Azure Subscription Id.
+    :vartype subscription_id: str
+    """
+
+    _attribute_map = {
+        "resource_group": {"key": "resourceGroup", "type": "str"},
+        "subscription_id": {"key": "subscriptionId", "type": "str"},
+    }
+
+    def __init__(
+        self, *, resource_group: Optional[str] = None, subscription_id: Optional[str] = None, **kwargs: Any
+    ) -> None:
+        """
+        :keyword resource_group: Azure Resource Group name.
+        :paramtype resource_group: str
+        :keyword subscription_id: Azure Subscription Id.
+        :paramtype subscription_id: str
+        """
+        super().__init__(**kwargs)
+        self.resource_group = resource_group
+        self.subscription_id = subscription_id
+
+
+class AzureBlobDatastore(AzureDatastore, DatastoreProperties):  # pylint: disable=too-many-instance-attributes
     """Azure Blob datastore configuration.
 
     Variables are only populated by the server, and will be ignored when sending a request.
@@ -2313,11 +2674,15 @@ class AzureBlobDatastore(DatastoreProperties):  # pylint: disable=too-many-insta
     :ivar credentials: [Required] Account credentials. Required.
     :vartype credentials: ~azure.mgmt.machinelearningservices.models.DatastoreCredentials
     :ivar datastore_type: [Required] Storage type backing the datastore. Required. Known values
-     are: "AzureBlob", "AzureDataLakeGen1", "AzureDataLakeGen2", and "AzureFile".
+     are: "AzureBlob", "AzureDataLakeGen1", "AzureDataLakeGen2", "AzureFile", and "Hdfs".
     :vartype datastore_type: str or ~azure.mgmt.machinelearningservices.models.DatastoreType
     :ivar is_default: Readonly property to indicate if datastore is the workspace default
      datastore.
     :vartype is_default: bool
+    :ivar resource_group: Azure Resource Group name.
+    :vartype resource_group: str
+    :ivar subscription_id: Azure Subscription Id.
+    :vartype subscription_id: str
     :ivar account_name: Storage account name.
     :vartype account_name: str
     :ivar container_name: Storage account container name.
@@ -2346,6 +2711,8 @@ class AzureBlobDatastore(DatastoreProperties):  # pylint: disable=too-many-insta
         "credentials": {"key": "credentials", "type": "DatastoreCredentials"},
         "datastore_type": {"key": "datastoreType", "type": "str"},
         "is_default": {"key": "isDefault", "type": "bool"},
+        "resource_group": {"key": "resourceGroup", "type": "str"},
+        "subscription_id": {"key": "subscriptionId", "type": "str"},
         "account_name": {"key": "accountName", "type": "str"},
         "container_name": {"key": "containerName", "type": "str"},
         "endpoint": {"key": "endpoint", "type": "str"},
@@ -2360,13 +2727,15 @@ def __init__(
         description: Optional[str] = None,
         properties: Optional[Dict[str, str]] = None,
         tags: Optional[Dict[str, str]] = None,
+        resource_group: Optional[str] = None,
+        subscription_id: Optional[str] = None,
         account_name: Optional[str] = None,
         container_name: Optional[str] = None,
         endpoint: Optional[str] = None,
         protocol: Optional[str] = None,
         service_data_access_auth_identity: Optional[Union[str, "_models.ServiceDataAccessAuthIdentity"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -2376,6 +2745,10 @@ def __init__(
         :paramtype tags: dict[str, str]
         :keyword credentials: [Required] Account credentials. Required.
         :paramtype credentials: ~azure.mgmt.machinelearningservices.models.DatastoreCredentials
+        :keyword resource_group: Azure Resource Group name.
+        :paramtype resource_group: str
+        :keyword subscription_id: Azure Subscription Id.
+        :paramtype subscription_id: str
         :keyword account_name: Storage account name.
         :paramtype account_name: str
         :keyword container_name: Storage account container name.
@@ -2390,16 +2763,31 @@ def __init__(
         :paramtype service_data_access_auth_identity: str or
          ~azure.mgmt.machinelearningservices.models.ServiceDataAccessAuthIdentity
         """
-        super().__init__(description=description, properties=properties, tags=tags, credentials=credentials, **kwargs)
+        super().__init__(
+            resource_group=resource_group,
+            subscription_id=subscription_id,
+            description=description,
+            properties=properties,
+            tags=tags,
+            credentials=credentials,
+            **kwargs
+        )
+        self.description = description
+        self.properties = properties
+        self.tags = tags
+        self.credentials = credentials
         self.datastore_type: str = "AzureBlob"
+        self.is_default = None
         self.account_name = account_name
         self.container_name = container_name
         self.endpoint = endpoint
         self.protocol = protocol
         self.service_data_access_auth_identity = service_data_access_auth_identity
+        self.resource_group = resource_group
+        self.subscription_id = subscription_id
 
 
-class AzureDataLakeGen1Datastore(DatastoreProperties):
+class AzureDataLakeGen1Datastore(AzureDatastore, DatastoreProperties):
     """Azure Data Lake Gen1 datastore configuration.
 
     Variables are only populated by the server, and will be ignored when sending a request.
@@ -2415,11 +2803,15 @@ class AzureDataLakeGen1Datastore(DatastoreProperties):
     :ivar credentials: [Required] Account credentials. Required.
     :vartype credentials: ~azure.mgmt.machinelearningservices.models.DatastoreCredentials
     :ivar datastore_type: [Required] Storage type backing the datastore. Required. Known values
-     are: "AzureBlob", "AzureDataLakeGen1", "AzureDataLakeGen2", and "AzureFile".
+     are: "AzureBlob", "AzureDataLakeGen1", "AzureDataLakeGen2", "AzureFile", and "Hdfs".
     :vartype datastore_type: str or ~azure.mgmt.machinelearningservices.models.DatastoreType
     :ivar is_default: Readonly property to indicate if datastore is the workspace default
      datastore.
     :vartype is_default: bool
+    :ivar resource_group: Azure Resource Group name.
+    :vartype resource_group: str
+    :ivar subscription_id: Azure Subscription Id.
+    :vartype subscription_id: str
     :ivar service_data_access_auth_identity: Indicates which identity to use to authenticate
      service data access to customer's storage. Known values are: "None",
      "WorkspaceSystemAssignedIdentity", and "WorkspaceUserAssignedIdentity".
@@ -2443,6 +2835,8 @@ class AzureDataLakeGen1Datastore(DatastoreProperties):
         "credentials": {"key": "credentials", "type": "DatastoreCredentials"},
         "datastore_type": {"key": "datastoreType", "type": "str"},
         "is_default": {"key": "isDefault", "type": "bool"},
+        "resource_group": {"key": "resourceGroup", "type": "str"},
+        "subscription_id": {"key": "subscriptionId", "type": "str"},
         "service_data_access_auth_identity": {"key": "serviceDataAccessAuthIdentity", "type": "str"},
         "store_name": {"key": "storeName", "type": "str"},
     }
@@ -2455,9 +2849,11 @@ def __init__(
         description: Optional[str] = None,
         properties: Optional[Dict[str, str]] = None,
         tags: Optional[Dict[str, str]] = None,
+        resource_group: Optional[str] = None,
+        subscription_id: Optional[str] = None,
         service_data_access_auth_identity: Optional[Union[str, "_models.ServiceDataAccessAuthIdentity"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -2467,6 +2863,10 @@ def __init__(
         :paramtype tags: dict[str, str]
         :keyword credentials: [Required] Account credentials. Required.
         :paramtype credentials: ~azure.mgmt.machinelearningservices.models.DatastoreCredentials
+        :keyword resource_group: Azure Resource Group name.
+        :paramtype resource_group: str
+        :keyword subscription_id: Azure Subscription Id.
+        :paramtype subscription_id: str
         :keyword service_data_access_auth_identity: Indicates which identity to use to authenticate
          service data access to customer's storage. Known values are: "None",
          "WorkspaceSystemAssignedIdentity", and "WorkspaceUserAssignedIdentity".
@@ -2475,13 +2875,28 @@ def __init__(
         :keyword store_name: [Required] Azure Data Lake store name. Required.
         :paramtype store_name: str
         """
-        super().__init__(description=description, properties=properties, tags=tags, credentials=credentials, **kwargs)
+        super().__init__(
+            resource_group=resource_group,
+            subscription_id=subscription_id,
+            description=description,
+            properties=properties,
+            tags=tags,
+            credentials=credentials,
+            **kwargs
+        )
+        self.description = description
+        self.properties = properties
+        self.tags = tags
+        self.credentials = credentials
         self.datastore_type: str = "AzureDataLakeGen1"
+        self.is_default = None
         self.service_data_access_auth_identity = service_data_access_auth_identity
         self.store_name = store_name
+        self.resource_group = resource_group
+        self.subscription_id = subscription_id
 
 
-class AzureDataLakeGen2Datastore(DatastoreProperties):  # pylint: disable=too-many-instance-attributes
+class AzureDataLakeGen2Datastore(AzureDatastore, DatastoreProperties):  # pylint: disable=too-many-instance-attributes
     """Azure Data Lake Gen2 datastore configuration.
 
     Variables are only populated by the server, and will be ignored when sending a request.
@@ -2497,11 +2912,15 @@ class AzureDataLakeGen2Datastore(DatastoreProperties):  # pylint: disable=too-ma
     :ivar credentials: [Required] Account credentials. Required.
     :vartype credentials: ~azure.mgmt.machinelearningservices.models.DatastoreCredentials
     :ivar datastore_type: [Required] Storage type backing the datastore. Required. Known values
-     are: "AzureBlob", "AzureDataLakeGen1", "AzureDataLakeGen2", and "AzureFile".
+     are: "AzureBlob", "AzureDataLakeGen1", "AzureDataLakeGen2", "AzureFile", and "Hdfs".
     :vartype datastore_type: str or ~azure.mgmt.machinelearningservices.models.DatastoreType
     :ivar is_default: Readonly property to indicate if datastore is the workspace default
      datastore.
     :vartype is_default: bool
+    :ivar resource_group: Azure Resource Group name.
+    :vartype resource_group: str
+    :ivar subscription_id: Azure Subscription Id.
+    :vartype subscription_id: str
     :ivar account_name: [Required] Storage account name. Required.
     :vartype account_name: str
     :ivar endpoint: Azure cloud endpoint for the storage account.
@@ -2532,6 +2951,8 @@ class AzureDataLakeGen2Datastore(DatastoreProperties):  # pylint: disable=too-ma
         "credentials": {"key": "credentials", "type": "DatastoreCredentials"},
         "datastore_type": {"key": "datastoreType", "type": "str"},
         "is_default": {"key": "isDefault", "type": "bool"},
+        "resource_group": {"key": "resourceGroup", "type": "str"},
+        "subscription_id": {"key": "subscriptionId", "type": "str"},
         "account_name": {"key": "accountName", "type": "str"},
         "endpoint": {"key": "endpoint", "type": "str"},
         "filesystem": {"key": "filesystem", "type": "str"},
@@ -2548,11 +2969,13 @@ def __init__(
         description: Optional[str] = None,
         properties: Optional[Dict[str, str]] = None,
         tags: Optional[Dict[str, str]] = None,
+        resource_group: Optional[str] = None,
+        subscription_id: Optional[str] = None,
         endpoint: Optional[str] = None,
         protocol: Optional[str] = None,
         service_data_access_auth_identity: Optional[Union[str, "_models.ServiceDataAccessAuthIdentity"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -2562,6 +2985,10 @@ def __init__(
         :paramtype tags: dict[str, str]
         :keyword credentials: [Required] Account credentials. Required.
         :paramtype credentials: ~azure.mgmt.machinelearningservices.models.DatastoreCredentials
+        :keyword resource_group: Azure Resource Group name.
+        :paramtype resource_group: str
+        :keyword subscription_id: Azure Subscription Id.
+        :paramtype subscription_id: str
         :keyword account_name: [Required] Storage account name. Required.
         :paramtype account_name: str
         :keyword endpoint: Azure cloud endpoint for the storage account.
@@ -2576,16 +3003,31 @@ def __init__(
         :paramtype service_data_access_auth_identity: str or
          ~azure.mgmt.machinelearningservices.models.ServiceDataAccessAuthIdentity
         """
-        super().__init__(description=description, properties=properties, tags=tags, credentials=credentials, **kwargs)
+        super().__init__(
+            resource_group=resource_group,
+            subscription_id=subscription_id,
+            description=description,
+            properties=properties,
+            tags=tags,
+            credentials=credentials,
+            **kwargs
+        )
+        self.description = description
+        self.properties = properties
+        self.tags = tags
+        self.credentials = credentials
         self.datastore_type: str = "AzureDataLakeGen2"
+        self.is_default = None
         self.account_name = account_name
         self.endpoint = endpoint
         self.filesystem = filesystem
         self.protocol = protocol
         self.service_data_access_auth_identity = service_data_access_auth_identity
+        self.resource_group = resource_group
+        self.subscription_id = subscription_id
 
 
-class AzureFileDatastore(DatastoreProperties):  # pylint: disable=too-many-instance-attributes
+class AzureFileDatastore(AzureDatastore, DatastoreProperties):  # pylint: disable=too-many-instance-attributes
     """Azure File datastore configuration.
 
     Variables are only populated by the server, and will be ignored when sending a request.
@@ -2601,11 +3043,15 @@ class AzureFileDatastore(DatastoreProperties):  # pylint: disable=too-many-insta
     :ivar credentials: [Required] Account credentials. Required.
     :vartype credentials: ~azure.mgmt.machinelearningservices.models.DatastoreCredentials
     :ivar datastore_type: [Required] Storage type backing the datastore. Required. Known values
-     are: "AzureBlob", "AzureDataLakeGen1", "AzureDataLakeGen2", and "AzureFile".
+     are: "AzureBlob", "AzureDataLakeGen1", "AzureDataLakeGen2", "AzureFile", and "Hdfs".
     :vartype datastore_type: str or ~azure.mgmt.machinelearningservices.models.DatastoreType
     :ivar is_default: Readonly property to indicate if datastore is the workspace default
      datastore.
     :vartype is_default: bool
+    :ivar resource_group: Azure Resource Group name.
+    :vartype resource_group: str
+    :ivar subscription_id: Azure Subscription Id.
+    :vartype subscription_id: str
     :ivar account_name: [Required] Storage account name. Required.
     :vartype account_name: str
     :ivar endpoint: Azure cloud endpoint for the storage account.
@@ -2637,6 +3083,8 @@ class AzureFileDatastore(DatastoreProperties):  # pylint: disable=too-many-insta
         "credentials": {"key": "credentials", "type": "DatastoreCredentials"},
         "datastore_type": {"key": "datastoreType", "type": "str"},
         "is_default": {"key": "isDefault", "type": "bool"},
+        "resource_group": {"key": "resourceGroup", "type": "str"},
+        "subscription_id": {"key": "subscriptionId", "type": "str"},
         "account_name": {"key": "accountName", "type": "str"},
         "endpoint": {"key": "endpoint", "type": "str"},
         "file_share_name": {"key": "fileShareName", "type": "str"},
@@ -2653,11 +3101,13 @@ def __init__(
         description: Optional[str] = None,
         properties: Optional[Dict[str, str]] = None,
         tags: Optional[Dict[str, str]] = None,
+        resource_group: Optional[str] = None,
+        subscription_id: Optional[str] = None,
         endpoint: Optional[str] = None,
         protocol: Optional[str] = None,
         service_data_access_auth_identity: Optional[Union[str, "_models.ServiceDataAccessAuthIdentity"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -2667,6 +3117,10 @@ def __init__(
         :paramtype tags: dict[str, str]
         :keyword credentials: [Required] Account credentials. Required.
         :paramtype credentials: ~azure.mgmt.machinelearningservices.models.DatastoreCredentials
+        :keyword resource_group: Azure Resource Group name.
+        :paramtype resource_group: str
+        :keyword subscription_id: Azure Subscription Id.
+        :paramtype subscription_id: str
         :keyword account_name: [Required] Storage account name. Required.
         :paramtype account_name: str
         :keyword endpoint: Azure cloud endpoint for the storage account.
@@ -2682,13 +3136,28 @@ def __init__(
         :paramtype service_data_access_auth_identity: str or
          ~azure.mgmt.machinelearningservices.models.ServiceDataAccessAuthIdentity
         """
-        super().__init__(description=description, properties=properties, tags=tags, credentials=credentials, **kwargs)
+        super().__init__(
+            resource_group=resource_group,
+            subscription_id=subscription_id,
+            description=description,
+            properties=properties,
+            tags=tags,
+            credentials=credentials,
+            **kwargs
+        )
+        self.description = description
+        self.properties = properties
+        self.tags = tags
+        self.credentials = credentials
         self.datastore_type: str = "AzureFile"
+        self.is_default = None
         self.account_name = account_name
         self.endpoint = endpoint
         self.file_share_name = file_share_name
         self.protocol = protocol
         self.service_data_access_auth_identity = service_data_access_auth_identity
+        self.resource_group = resource_group
+        self.subscription_id = subscription_id
 
 
 class EarlyTerminationPolicy(_serialization.Model):
@@ -2727,7 +3196,7 @@ class EarlyTerminationPolicy(_serialization.Model):
         }
     }
 
-    def __init__(self, *, delay_evaluation: int = 0, evaluation_interval: int = 0, **kwargs):
+    def __init__(self, *, delay_evaluation: int = 0, evaluation_interval: int = 0, **kwargs: Any) -> None:
         """
         :keyword delay_evaluation: Number of intervals by which to delay the first evaluation.
         :paramtype delay_evaluation: int
@@ -2741,7 +3210,8 @@ def __init__(self, *, delay_evaluation: int = 0, evaluation_interval: int = 0, *
 
 
 class BanditPolicy(EarlyTerminationPolicy):
-    """Defines an early termination policy based on slack criteria, and a frequency and delay interval for evaluation.
+    """Defines an early termination policy based on slack criteria, and a frequency and delay interval
+    for evaluation.
 
     All required parameters must be populated in order to send to Azure.
 
@@ -2778,8 +3248,8 @@ def __init__(
         evaluation_interval: int = 0,
         slack_amount: float = 0,
         slack_factor: float = 0,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword delay_evaluation: Number of intervals by which to delay the first evaluation.
         :paramtype delay_evaluation: int
@@ -2828,7 +3298,7 @@ class Resource(_serialization.Model):
         "system_data": {"key": "systemData", "type": "SystemData"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.id = None
@@ -2838,7 +3308,8 @@ def __init__(self, **kwargs):
 
 
 class TrackedResource(Resource):
-    """The resource model definition for an Azure Resource Manager tracked top level resource which has 'tags' and a 'location'.
+    """The resource model definition for an Azure Resource Manager tracked top level resource which
+    has 'tags' and a 'location'.
 
     Variables are only populated by the server, and will be ignored when sending a request.
 
@@ -2878,7 +3349,7 @@ class TrackedResource(Resource):
         "location": {"key": "location", "type": "str"},
     }
 
-    def __init__(self, *, location: str, tags: Optional[Dict[str, str]] = None, **kwargs):
+    def __init__(self, *, location: str, tags: Optional[Dict[str, str]] = None, **kwargs: Any) -> None:
         """
         :keyword tags: Resource tags.
         :paramtype tags: dict[str, str]
@@ -2954,8 +3425,8 @@ def __init__(
         identity: Optional["_models.ManagedServiceIdentity"] = None,
         kind: Optional[str] = None,
         sku: Optional["_models.Sku"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword tags: Resource tags.
         :paramtype tags: dict[str, str]
@@ -2985,8 +3456,8 @@ class EndpointDeploymentPropertiesBase(_serialization.Model):
     :vartype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
     :ivar description: Description of the endpoint deployment.
     :vartype description: str
-    :ivar environment_id: ARM resource ID or AssetId of the environment specification for the
-     endpoint deployment.
+    :ivar environment_id: ARM resource ID of the environment specification for the endpoint
+     deployment.
     :vartype environment_id: str
     :ivar environment_variables: Environment variables configuration for the deployment.
     :vartype environment_variables: dict[str, str]
@@ -3010,15 +3481,15 @@ def __init__(
         environment_id: Optional[str] = None,
         environment_variables: Optional[Dict[str, str]] = None,
         properties: Optional[Dict[str, str]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword code_configuration: Code configuration for the endpoint deployment.
         :paramtype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
         :keyword description: Description of the endpoint deployment.
         :paramtype description: str
-        :keyword environment_id: ARM resource ID or AssetId of the environment specification for the
-         endpoint deployment.
+        :keyword environment_id: ARM resource ID of the environment specification for the endpoint
+         deployment.
         :paramtype environment_id: str
         :keyword environment_variables: Environment variables configuration for the deployment.
         :paramtype environment_variables: dict[str, str]
@@ -3042,8 +3513,8 @@ class BatchDeploymentProperties(EndpointDeploymentPropertiesBase):  # pylint: di
     :vartype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
     :ivar description: Description of the endpoint deployment.
     :vartype description: str
-    :ivar environment_id: ARM resource ID or AssetId of the environment specification for the
-     endpoint deployment.
+    :ivar environment_id: ARM resource ID of the environment specification for the endpoint
+     deployment.
     :vartype environment_id: str
     :ivar environment_variables: Environment variables configuration for the deployment.
     :vartype environment_variables: dict[str, str]
@@ -3127,15 +3598,15 @@ def __init__(
         output_file_name: str = "predictions.csv",
         resources: Optional["_models.DeploymentResourceConfiguration"] = None,
         retry_settings: Optional["_models.BatchRetrySettings"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword code_configuration: Code configuration for the endpoint deployment.
         :paramtype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
         :keyword description: Description of the endpoint deployment.
         :paramtype description: str
-        :keyword environment_id: ARM resource ID or AssetId of the environment specification for the
-         endpoint deployment.
+        :keyword environment_id: ARM resource ID of the environment specification for the endpoint
+         deployment.
         :paramtype environment_id: str
         :keyword environment_variables: Environment variables configuration for the deployment.
         :paramtype environment_variables: dict[str, str]
@@ -3211,8 +3682,8 @@ class BatchDeploymentTrackedResourceArmPaginatedResult(_serialization.Model):
     }
 
     def __init__(
-        self, *, next_link: Optional[str] = None, value: Optional[List["_models.BatchDeployment"]] = None, **kwargs
-    ):
+        self, *, next_link: Optional[str] = None, value: Optional[List["_models.BatchDeployment"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of BatchDeployment objects. If null, there are no
          additional pages.
@@ -3289,8 +3760,8 @@ def __init__(
         identity: Optional["_models.ManagedServiceIdentity"] = None,
         kind: Optional[str] = None,
         sku: Optional["_models.Sku"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword tags: Resource tags.
         :paramtype tags: dict[str, str]
@@ -3325,7 +3796,7 @@ class BatchEndpointDefaults(_serialization.Model):
         "deployment_name": {"key": "deploymentName", "type": "str"},
     }
 
-    def __init__(self, *, deployment_name: Optional[str] = None, **kwargs):
+    def __init__(self, *, deployment_name: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword deployment_name: Name of the deployment that will be default for the endpoint.
          This deployment will end up getting 100% traffic when the endpoint scoring URL is invoked.
@@ -3382,8 +3853,8 @@ def __init__(
         description: Optional[str] = None,
         keys: Optional["_models.EndpointAuthKeys"] = None,
         properties: Optional[Dict[str, str]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword auth_mode: [Required] Use 'Key' for key based authentication and 'AMLToken' for Azure
          Machine Learning token-based authentication. 'Key' doesn't expire but 'AMLToken' does.
@@ -3464,8 +3935,8 @@ def __init__(
         keys: Optional["_models.EndpointAuthKeys"] = None,
         properties: Optional[Dict[str, str]] = None,
         defaults: Optional["_models.BatchEndpointDefaults"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword auth_mode: [Required] Use 'Key' for key based authentication and 'AMLToken' for Azure
          Machine Learning token-based authentication. 'Key' doesn't expire but 'AMLToken' does.
@@ -3503,8 +3974,8 @@ class BatchEndpointTrackedResourceArmPaginatedResult(_serialization.Model):
     }
 
     def __init__(
-        self, *, next_link: Optional[str] = None, value: Optional[List["_models.BatchEndpoint"]] = None, **kwargs
-    ):
+        self, *, next_link: Optional[str] = None, value: Optional[List["_models.BatchEndpoint"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of BatchEndpoint objects. If null, there are no
          additional pages.
@@ -3531,7 +4002,7 @@ class BatchRetrySettings(_serialization.Model):
         "timeout": {"key": "timeout", "type": "duration"},
     }
 
-    def __init__(self, *, max_retries: int = 3, timeout: datetime.timedelta = "PT30S", **kwargs):
+    def __init__(self, *, max_retries: int = 3, timeout: datetime.timedelta = "PT30S", **kwargs: Any) -> None:
         """
         :keyword max_retries: Maximum retry count for a mini-batch.
         :paramtype max_retries: int
@@ -3547,16 +4018,16 @@ class SamplingAlgorithm(_serialization.Model):
     """The Sampling Algorithm used to generate hyperparameter values, along with properties to
     configure the algorithm.
 
-        You probably want to use the sub-classes and not this class directly. Known sub-classes are:
-        BayesianSamplingAlgorithm, GridSamplingAlgorithm, RandomSamplingAlgorithm
+    You probably want to use the sub-classes and not this class directly. Known sub-classes are:
+    BayesianSamplingAlgorithm, GridSamplingAlgorithm, RandomSamplingAlgorithm
 
-        All required parameters must be populated in order to send to Azure.
+    All required parameters must be populated in order to send to Azure.
 
-        :ivar sampling_algorithm_type: [Required] The algorithm used for generating hyperparameter
-         values, along with configuration properties. Required. Known values are: "Grid", "Random", and
-         "Bayesian".
-        :vartype sampling_algorithm_type: str or
-         ~azure.mgmt.machinelearningservices.models.SamplingAlgorithmType
+    :ivar sampling_algorithm_type: [Required] The algorithm used for generating hyperparameter
+     values, along with configuration properties. Required. Known values are: "Grid", "Random", and
+     "Bayesian".
+    :vartype sampling_algorithm_type: str or
+     ~azure.mgmt.machinelearningservices.models.SamplingAlgorithmType
     """
 
     _validation = {
@@ -3575,7 +4046,7 @@ class SamplingAlgorithm(_serialization.Model):
         }
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.sampling_algorithm_type: Optional[str] = None
@@ -3601,12 +4072,51 @@ class BayesianSamplingAlgorithm(SamplingAlgorithm):
         "sampling_algorithm_type": {"key": "samplingAlgorithmType", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.sampling_algorithm_type: str = "Bayesian"
 
 
+class BindOptions(_serialization.Model):
+    """BindOptions.
+
+    :ivar propagation: Type of Bind Option.
+    :vartype propagation: str
+    :ivar create_host_path: Indicate whether to create host path.
+    :vartype create_host_path: bool
+    :ivar selinux: Mention the selinux options.
+    :vartype selinux: str
+    """
+
+    _attribute_map = {
+        "propagation": {"key": "propagation", "type": "str"},
+        "create_host_path": {"key": "createHostPath", "type": "bool"},
+        "selinux": {"key": "selinux", "type": "str"},
+    }
+
+    def __init__(
+        self,
+        *,
+        propagation: Optional[str] = None,
+        create_host_path: Optional[bool] = None,
+        selinux: Optional[str] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword propagation: Type of Bind Option.
+        :paramtype propagation: str
+        :keyword create_host_path: Indicate whether to create host path.
+        :paramtype create_host_path: bool
+        :keyword selinux: Mention the selinux options.
+        :paramtype selinux: str
+        """
+        super().__init__(**kwargs)
+        self.propagation = propagation
+        self.create_host_path = create_host_path
+        self.selinux = selinux
+
+
 class BuildContext(_serialization.Model):
     """Configuration settings for Docker build context.
 
@@ -3640,7 +4150,7 @@ class BuildContext(_serialization.Model):
         "dockerfile_path": {"key": "dockerfilePath", "type": "str"},
     }
 
-    def __init__(self, *, context_uri: str, dockerfile_path: str = "Dockerfile", **kwargs):
+    def __init__(self, *, context_uri: str, dockerfile_path: str = "Dockerfile", **kwargs: Any) -> None:
         """
         :keyword context_uri: [Required] URI of the Docker build context used to build the image.
          Supports blob URIs on environment creation and may return blob or Git URIs.
@@ -3671,7 +4181,8 @@ class CertificateDatastoreCredentials(DatastoreCredentials):
     All required parameters must be populated in order to send to Azure.
 
     :ivar credentials_type: [Required] Credential type used to authentication with storage.
-     Required. Known values are: "AccountKey", "Certificate", "None", "Sas", and "ServicePrincipal".
+     Required. Known values are: "AccountKey", "Certificate", "None", "Sas", "ServicePrincipal",
+     "KerberosKeytab", and "KerberosPassword".
     :vartype credentials_type: str or ~azure.mgmt.machinelearningservices.models.CredentialsType
     :ivar authority_url: Authority URL used for authentication.
     :vartype authority_url: str
@@ -3714,8 +4225,8 @@ def __init__(
         thumbprint: str,
         authority_url: Optional[str] = None,
         resource_url: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword authority_url: Authority URL used for authentication.
         :paramtype authority_url: str
@@ -3748,7 +4259,8 @@ class CertificateDatastoreSecrets(DatastoreSecrets):
     All required parameters must be populated in order to send to Azure.
 
     :ivar secrets_type: [Required] Credential type used to authentication with storage. Required.
-     Known values are: "AccountKey", "Certificate", "Sas", and "ServicePrincipal".
+     Known values are: "AccountKey", "Certificate", "Sas", "ServicePrincipal", "KerberosPassword",
+     and "KerberosKeytab".
     :vartype secrets_type: str or ~azure.mgmt.machinelearningservices.models.SecretsType
     :ivar certificate: Service principal certificate.
     :vartype certificate: str
@@ -3763,7 +4275,7 @@ class CertificateDatastoreSecrets(DatastoreSecrets):
         "certificate": {"key": "certificate", "type": "str"},
     }
 
-    def __init__(self, *, certificate: Optional[str] = None, **kwargs):
+    def __init__(self, *, certificate: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword certificate: Service principal certificate.
         :paramtype certificate: str
@@ -3773,19 +4285,28 @@ def __init__(self, *, certificate: Optional[str] = None, **kwargs):
         self.certificate = certificate
 
 
-class TableVertical(_serialization.Model):
-    """Abstract class for AutoML tasks that use table dataset as input - such as Classification/Regression/Forecasting.
+class TableVertical(_serialization.Model):  # pylint: disable=too-many-instance-attributes
+    """Abstract class for AutoML tasks that use table dataset as input - such as
+    Classification/Regression/Forecasting.
 
     :ivar cv_split_column_names: Columns to use for CVSplit data.
     :vartype cv_split_column_names: list[str]
     :ivar featurization_settings: Featurization inputs needed for AutoML job.
     :vartype featurization_settings:
      ~azure.mgmt.machinelearningservices.models.TableVerticalFeaturizationSettings
+    :ivar fixed_parameters: Model/training parameters that will remain constant throughout
+     training.
+    :vartype fixed_parameters: ~azure.mgmt.machinelearningservices.models.TableFixedParameters
     :ivar limit_settings: Execution constraints for AutoMLJob.
     :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.TableVerticalLimitSettings
     :ivar n_cross_validations: Number of cross validation folds to be applied on training dataset
      when validation dataset is not provided.
     :vartype n_cross_validations: ~azure.mgmt.machinelearningservices.models.NCrossValidations
+    :ivar search_space: Search space for sampling different combinations of models and their
+     hyperparameters.
+    :vartype search_space: list[~azure.mgmt.machinelearningservices.models.TableParameterSubspace]
+    :ivar sweep_settings: Settings for model sweeping and hyperparameter tuning.
+    :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.TableSweepSettings
     :ivar test_data: Test data input.
     :vartype test_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
     :ivar test_data_size: The fraction of test dataset that needs to be set aside for validation
@@ -3808,8 +4329,11 @@ class TableVertical(_serialization.Model):
     _attribute_map = {
         "cv_split_column_names": {"key": "cvSplitColumnNames", "type": "[str]"},
         "featurization_settings": {"key": "featurizationSettings", "type": "TableVerticalFeaturizationSettings"},
+        "fixed_parameters": {"key": "fixedParameters", "type": "TableFixedParameters"},
         "limit_settings": {"key": "limitSettings", "type": "TableVerticalLimitSettings"},
         "n_cross_validations": {"key": "nCrossValidations", "type": "NCrossValidations"},
+        "search_space": {"key": "searchSpace", "type": "[TableParameterSubspace]"},
+        "sweep_settings": {"key": "sweepSettings", "type": "TableSweepSettings"},
         "test_data": {"key": "testData", "type": "MLTableJobInput"},
         "test_data_size": {"key": "testDataSize", "type": "float"},
         "validation_data": {"key": "validationData", "type": "MLTableJobInput"},
@@ -3822,21 +4346,27 @@ def __init__(
         *,
         cv_split_column_names: Optional[List[str]] = None,
         featurization_settings: Optional["_models.TableVerticalFeaturizationSettings"] = None,
+        fixed_parameters: Optional["_models.TableFixedParameters"] = None,
         limit_settings: Optional["_models.TableVerticalLimitSettings"] = None,
         n_cross_validations: Optional["_models.NCrossValidations"] = None,
+        search_space: Optional[List["_models.TableParameterSubspace"]] = None,
+        sweep_settings: Optional["_models.TableSweepSettings"] = None,
         test_data: Optional["_models.MLTableJobInput"] = None,
         test_data_size: Optional[float] = None,
         validation_data: Optional["_models.MLTableJobInput"] = None,
         validation_data_size: Optional[float] = None,
         weight_column_name: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword cv_split_column_names: Columns to use for CVSplit data.
         :paramtype cv_split_column_names: list[str]
         :keyword featurization_settings: Featurization inputs needed for AutoML job.
         :paramtype featurization_settings:
          ~azure.mgmt.machinelearningservices.models.TableVerticalFeaturizationSettings
+        :keyword fixed_parameters: Model/training parameters that will remain constant throughout
+         training.
+        :paramtype fixed_parameters: ~azure.mgmt.machinelearningservices.models.TableFixedParameters
         :keyword limit_settings: Execution constraints for AutoMLJob.
         :paramtype limit_settings:
          ~azure.mgmt.machinelearningservices.models.TableVerticalLimitSettings
@@ -3844,6 +4374,12 @@ def __init__(
          dataset
          when validation dataset is not provided.
         :paramtype n_cross_validations: ~azure.mgmt.machinelearningservices.models.NCrossValidations
+        :keyword search_space: Search space for sampling different combinations of models and their
+         hyperparameters.
+        :paramtype search_space:
+         list[~azure.mgmt.machinelearningservices.models.TableParameterSubspace]
+        :keyword sweep_settings: Settings for model sweeping and hyperparameter tuning.
+        :paramtype sweep_settings: ~azure.mgmt.machinelearningservices.models.TableSweepSettings
         :keyword test_data: Test data input.
         :paramtype test_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
         :keyword test_data_size: The fraction of test dataset that needs to be set aside for validation
@@ -3865,8 +4401,11 @@ def __init__(
         super().__init__(**kwargs)
         self.cv_split_column_names = cv_split_column_names
         self.featurization_settings = featurization_settings
+        self.fixed_parameters = fixed_parameters
         self.limit_settings = limit_settings
         self.n_cross_validations = n_cross_validations
+        self.search_space = search_space
+        self.sweep_settings = sweep_settings
         self.test_data = test_data
         self.test_data_size = test_data_size
         self.validation_data = validation_data
@@ -3897,11 +4436,19 @@ class Classification(TableVertical, AutoMLVertical):  # pylint: disable=too-many
     :ivar featurization_settings: Featurization inputs needed for AutoML job.
     :vartype featurization_settings:
      ~azure.mgmt.machinelearningservices.models.TableVerticalFeaturizationSettings
+    :ivar fixed_parameters: Model/training parameters that will remain constant throughout
+     training.
+    :vartype fixed_parameters: ~azure.mgmt.machinelearningservices.models.TableFixedParameters
     :ivar limit_settings: Execution constraints for AutoMLJob.
     :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.TableVerticalLimitSettings
     :ivar n_cross_validations: Number of cross validation folds to be applied on training dataset
      when validation dataset is not provided.
     :vartype n_cross_validations: ~azure.mgmt.machinelearningservices.models.NCrossValidations
+    :ivar search_space: Search space for sampling different combinations of models and their
+     hyperparameters.
+    :vartype search_space: list[~azure.mgmt.machinelearningservices.models.TableParameterSubspace]
+    :ivar sweep_settings: Settings for model sweeping and hyperparameter tuning.
+    :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.TableSweepSettings
     :ivar test_data: Test data input.
     :vartype test_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
     :ivar test_data_size: The fraction of test dataset that needs to be set aside for validation
@@ -3942,8 +4489,11 @@ class Classification(TableVertical, AutoMLVertical):  # pylint: disable=too-many
         "training_data": {"key": "trainingData", "type": "MLTableJobInput"},
         "cv_split_column_names": {"key": "cvSplitColumnNames", "type": "[str]"},
         "featurization_settings": {"key": "featurizationSettings", "type": "TableVerticalFeaturizationSettings"},
+        "fixed_parameters": {"key": "fixedParameters", "type": "TableFixedParameters"},
         "limit_settings": {"key": "limitSettings", "type": "TableVerticalLimitSettings"},
         "n_cross_validations": {"key": "nCrossValidations", "type": "NCrossValidations"},
+        "search_space": {"key": "searchSpace", "type": "[TableParameterSubspace]"},
+        "sweep_settings": {"key": "sweepSettings", "type": "TableSweepSettings"},
         "test_data": {"key": "testData", "type": "MLTableJobInput"},
         "test_data_size": {"key": "testDataSize", "type": "float"},
         "validation_data": {"key": "validationData", "type": "MLTableJobInput"},
@@ -3962,8 +4512,11 @@ def __init__(
         target_column_name: Optional[str] = None,
         cv_split_column_names: Optional[List[str]] = None,
         featurization_settings: Optional["_models.TableVerticalFeaturizationSettings"] = None,
+        fixed_parameters: Optional["_models.TableFixedParameters"] = None,
         limit_settings: Optional["_models.TableVerticalLimitSettings"] = None,
         n_cross_validations: Optional["_models.NCrossValidations"] = None,
+        search_space: Optional[List["_models.TableParameterSubspace"]] = None,
+        sweep_settings: Optional["_models.TableSweepSettings"] = None,
         test_data: Optional["_models.MLTableJobInput"] = None,
         test_data_size: Optional[float] = None,
         validation_data: Optional["_models.MLTableJobInput"] = None,
@@ -3972,8 +4525,8 @@ def __init__(
         positive_label: Optional[str] = None,
         primary_metric: Optional[Union[str, "_models.ClassificationPrimaryMetrics"]] = None,
         training_settings: Optional["_models.ClassificationTrainingSettings"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
          "Warning", "Error", and "Critical".
@@ -3988,6 +4541,9 @@ def __init__(
         :keyword featurization_settings: Featurization inputs needed for AutoML job.
         :paramtype featurization_settings:
          ~azure.mgmt.machinelearningservices.models.TableVerticalFeaturizationSettings
+        :keyword fixed_parameters: Model/training parameters that will remain constant throughout
+         training.
+        :paramtype fixed_parameters: ~azure.mgmt.machinelearningservices.models.TableFixedParameters
         :keyword limit_settings: Execution constraints for AutoMLJob.
         :paramtype limit_settings:
          ~azure.mgmt.machinelearningservices.models.TableVerticalLimitSettings
@@ -3995,6 +4551,12 @@ def __init__(
          dataset
          when validation dataset is not provided.
         :paramtype n_cross_validations: ~azure.mgmt.machinelearningservices.models.NCrossValidations
+        :keyword search_space: Search space for sampling different combinations of models and their
+         hyperparameters.
+        :paramtype search_space:
+         list[~azure.mgmt.machinelearningservices.models.TableParameterSubspace]
+        :keyword sweep_settings: Settings for model sweeping and hyperparameter tuning.
+        :paramtype sweep_settings: ~azure.mgmt.machinelearningservices.models.TableSweepSettings
         :keyword test_data: Test data input.
         :paramtype test_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
         :keyword test_data_size: The fraction of test dataset that needs to be set aside for validation
@@ -4025,8 +4587,11 @@ def __init__(
         super().__init__(
             cv_split_column_names=cv_split_column_names,
             featurization_settings=featurization_settings,
+            fixed_parameters=fixed_parameters,
             limit_settings=limit_settings,
             n_cross_validations=n_cross_validations,
+            search_space=search_space,
+            sweep_settings=sweep_settings,
             test_data=test_data,
             test_data_size=test_data_size,
             validation_data=validation_data,
@@ -4046,8 +4611,11 @@ def __init__(
         self.training_settings = training_settings
         self.cv_split_column_names = cv_split_column_names
         self.featurization_settings = featurization_settings
+        self.fixed_parameters = fixed_parameters
         self.limit_settings = limit_settings
         self.n_cross_validations = n_cross_validations
+        self.search_space = search_space
+        self.sweep_settings = sweep_settings
         self.test_data = test_data
         self.test_data_size = test_data_size
         self.validation_data = validation_data
@@ -4075,6 +4643,14 @@ class TrainingSettings(_serialization.Model):
     :ivar stack_ensemble_settings: Stack ensemble settings for stack ensemble run.
     :vartype stack_ensemble_settings:
      ~azure.mgmt.machinelearningservices.models.StackEnsembleSettings
+    :ivar training_mode: TrainingMode mode - Setting to 'auto' is same as setting it to
+     'non-distributed' for now, however in the future may result in mixed mode or heuristics based
+     mode selection. Default is 'auto'.
+     If 'Distributed' then only distributed featurization is used and distributed algorithms are
+     chosen.
+     If 'NonDistributed' then only non distributed algorithms are chosen. Known values are: "Auto",
+     "Distributed", and "NonDistributed".
+    :vartype training_mode: str or ~azure.mgmt.machinelearningservices.models.TrainingMode
     """
 
     _attribute_map = {
@@ -4085,6 +4661,7 @@ class TrainingSettings(_serialization.Model):
         "enable_vote_ensemble": {"key": "enableVoteEnsemble", "type": "bool"},
         "ensemble_model_download_timeout": {"key": "ensembleModelDownloadTimeout", "type": "duration"},
         "stack_ensemble_settings": {"key": "stackEnsembleSettings", "type": "StackEnsembleSettings"},
+        "training_mode": {"key": "trainingMode", "type": "str"},
     }
 
     def __init__(
@@ -4097,8 +4674,9 @@ def __init__(
         enable_vote_ensemble: bool = True,
         ensemble_model_download_timeout: datetime.timedelta = "PT5M",
         stack_ensemble_settings: Optional["_models.StackEnsembleSettings"] = None,
-        **kwargs
-    ):
+        training_mode: Optional[Union[str, "_models.TrainingMode"]] = None,
+        **kwargs: Any
+    ) -> None:
         """
         :keyword enable_dnn_training: Enable recommendation of DNN models.
         :paramtype enable_dnn_training: bool
@@ -4117,6 +4695,14 @@ def __init__(
         :keyword stack_ensemble_settings: Stack ensemble settings for stack ensemble run.
         :paramtype stack_ensemble_settings:
          ~azure.mgmt.machinelearningservices.models.StackEnsembleSettings
+        :keyword training_mode: TrainingMode mode - Setting to 'auto' is same as setting it to
+         'non-distributed' for now, however in the future may result in mixed mode or heuristics based
+         mode selection. Default is 'auto'.
+         If 'Distributed' then only distributed featurization is used and distributed algorithms are
+         chosen.
+         If 'NonDistributed' then only non distributed algorithms are chosen. Known values are: "Auto",
+         "Distributed", and "NonDistributed".
+        :paramtype training_mode: str or ~azure.mgmt.machinelearningservices.models.TrainingMode
         """
         super().__init__(**kwargs)
         self.enable_dnn_training = enable_dnn_training
@@ -4126,6 +4712,7 @@ def __init__(
         self.enable_vote_ensemble = enable_vote_ensemble
         self.ensemble_model_download_timeout = ensemble_model_download_timeout
         self.stack_ensemble_settings = stack_ensemble_settings
+        self.training_mode = training_mode
 
 
 class ClassificationTrainingSettings(TrainingSettings):
@@ -4148,6 +4735,14 @@ class ClassificationTrainingSettings(TrainingSettings):
     :ivar stack_ensemble_settings: Stack ensemble settings for stack ensemble run.
     :vartype stack_ensemble_settings:
      ~azure.mgmt.machinelearningservices.models.StackEnsembleSettings
+    :ivar training_mode: TrainingMode mode - Setting to 'auto' is same as setting it to
+     'non-distributed' for now, however in the future may result in mixed mode or heuristics based
+     mode selection. Default is 'auto'.
+     If 'Distributed' then only distributed featurization is used and distributed algorithms are
+     chosen.
+     If 'NonDistributed' then only non distributed algorithms are chosen. Known values are: "Auto",
+     "Distributed", and "NonDistributed".
+    :vartype training_mode: str or ~azure.mgmt.machinelearningservices.models.TrainingMode
     :ivar allowed_training_algorithms: Allowed models for classification task.
     :vartype allowed_training_algorithms: list[str or
      ~azure.mgmt.machinelearningservices.models.ClassificationModels]
@@ -4164,6 +4759,7 @@ class ClassificationTrainingSettings(TrainingSettings):
         "enable_vote_ensemble": {"key": "enableVoteEnsemble", "type": "bool"},
         "ensemble_model_download_timeout": {"key": "ensembleModelDownloadTimeout", "type": "duration"},
         "stack_ensemble_settings": {"key": "stackEnsembleSettings", "type": "StackEnsembleSettings"},
+        "training_mode": {"key": "trainingMode", "type": "str"},
         "allowed_training_algorithms": {"key": "allowedTrainingAlgorithms", "type": "[str]"},
         "blocked_training_algorithms": {"key": "blockedTrainingAlgorithms", "type": "[str]"},
     }
@@ -4178,10 +4774,11 @@ def __init__(
         enable_vote_ensemble: bool = True,
         ensemble_model_download_timeout: datetime.timedelta = "PT5M",
         stack_ensemble_settings: Optional["_models.StackEnsembleSettings"] = None,
+        training_mode: Optional[Union[str, "_models.TrainingMode"]] = None,
         allowed_training_algorithms: Optional[List[Union[str, "_models.ClassificationModels"]]] = None,
         blocked_training_algorithms: Optional[List[Union[str, "_models.ClassificationModels"]]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword enable_dnn_training: Enable recommendation of DNN models.
         :paramtype enable_dnn_training: bool
@@ -4200,6 +4797,14 @@ def __init__(
         :keyword stack_ensemble_settings: Stack ensemble settings for stack ensemble run.
         :paramtype stack_ensemble_settings:
          ~azure.mgmt.machinelearningservices.models.StackEnsembleSettings
+        :keyword training_mode: TrainingMode mode - Setting to 'auto' is same as setting it to
+         'non-distributed' for now, however in the future may result in mixed mode or heuristics based
+         mode selection. Default is 'auto'.
+         If 'Distributed' then only distributed featurization is used and distributed algorithms are
+         chosen.
+         If 'NonDistributed' then only non distributed algorithms are chosen. Known values are: "Auto",
+         "Distributed", and "NonDistributed".
+        :paramtype training_mode: str or ~azure.mgmt.machinelearningservices.models.TrainingMode
         :keyword allowed_training_algorithms: Allowed models for classification task.
         :paramtype allowed_training_algorithms: list[str or
          ~azure.mgmt.machinelearningservices.models.ClassificationModels]
@@ -4215,6 +4820,7 @@ def __init__(
             enable_vote_ensemble=enable_vote_ensemble,
             ensemble_model_download_timeout=ensemble_model_download_timeout,
             stack_ensemble_settings=stack_ensemble_settings,
+            training_mode=training_mode,
             **kwargs
         )
         self.allowed_training_algorithms = allowed_training_algorithms
@@ -4232,7 +4838,7 @@ class ClusterUpdateParameters(_serialization.Model):
         "properties": {"key": "properties.properties", "type": "ScaleSettingsInformation"},
     }
 
-    def __init__(self, *, properties: Optional["_models.ScaleSettingsInformation"] = None, **kwargs):
+    def __init__(self, *, properties: Optional["_models.ScaleSettingsInformation"] = None, **kwargs: Any) -> None:
         """
         :keyword properties: Properties of ClusterUpdate.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.ScaleSettingsInformation
@@ -4241,6 +4847,111 @@ def __init__(self, *, properties: Optional["_models.ScaleSettingsInformation"] =
         self.properties = properties
 
 
+class ExportSummary(_serialization.Model):
+    """ExportSummary.
+
+    You probably want to use the sub-classes and not this class directly. Known sub-classes are:
+    CsvExportSummary, CocoExportSummary, DatasetExportSummary
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar end_date_time: The time when the export was completed.
+    :vartype end_date_time: ~datetime.datetime
+    :ivar exported_row_count: The total number of labeled datapoints exported.
+    :vartype exported_row_count: int
+    :ivar format: [Required] The format of exported labels, also as the discriminator. Required.
+     Known values are: "Dataset", "Coco", and "CSV".
+    :vartype format: str or ~azure.mgmt.machinelearningservices.models.ExportFormatType
+    :ivar labeling_job_id: Name and identifier of the job containing exported labels.
+    :vartype labeling_job_id: str
+    :ivar start_date_time: The time when the export was requested.
+    :vartype start_date_time: ~datetime.datetime
+    """
+
+    _validation = {
+        "end_date_time": {"readonly": True},
+        "exported_row_count": {"readonly": True},
+        "format": {"required": True},
+        "labeling_job_id": {"readonly": True},
+        "start_date_time": {"readonly": True},
+    }
+
+    _attribute_map = {
+        "end_date_time": {"key": "endDateTime", "type": "iso-8601"},
+        "exported_row_count": {"key": "exportedRowCount", "type": "int"},
+        "format": {"key": "format", "type": "str"},
+        "labeling_job_id": {"key": "labelingJobId", "type": "str"},
+        "start_date_time": {"key": "startDateTime", "type": "iso-8601"},
+    }
+
+    _subtype_map = {
+        "format": {"CSV": "CsvExportSummary", "Coco": "CocoExportSummary", "Dataset": "DatasetExportSummary"}
+    }
+
+    def __init__(self, **kwargs: Any) -> None:
+        """ """
+        super().__init__(**kwargs)
+        self.end_date_time = None
+        self.exported_row_count = None
+        self.format: Optional[str] = None
+        self.labeling_job_id = None
+        self.start_date_time = None
+
+
+class CocoExportSummary(ExportSummary):
+    """CocoExportSummary.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar end_date_time: The time when the export was completed.
+    :vartype end_date_time: ~datetime.datetime
+    :ivar exported_row_count: The total number of labeled datapoints exported.
+    :vartype exported_row_count: int
+    :ivar format: [Required] The format of exported labels, also as the discriminator. Required.
+     Known values are: "Dataset", "Coco", and "CSV".
+    :vartype format: str or ~azure.mgmt.machinelearningservices.models.ExportFormatType
+    :ivar labeling_job_id: Name and identifier of the job containing exported labels.
+    :vartype labeling_job_id: str
+    :ivar start_date_time: The time when the export was requested.
+    :vartype start_date_time: ~datetime.datetime
+    :ivar container_name: The container name to which the labels will be exported.
+    :vartype container_name: str
+    :ivar snapshot_path: The output path where the labels will be exported.
+    :vartype snapshot_path: str
+    """
+
+    _validation = {
+        "end_date_time": {"readonly": True},
+        "exported_row_count": {"readonly": True},
+        "format": {"required": True},
+        "labeling_job_id": {"readonly": True},
+        "start_date_time": {"readonly": True},
+        "container_name": {"readonly": True},
+        "snapshot_path": {"readonly": True},
+    }
+
+    _attribute_map = {
+        "end_date_time": {"key": "endDateTime", "type": "iso-8601"},
+        "exported_row_count": {"key": "exportedRowCount", "type": "int"},
+        "format": {"key": "format", "type": "str"},
+        "labeling_job_id": {"key": "labelingJobId", "type": "str"},
+        "start_date_time": {"key": "startDateTime", "type": "iso-8601"},
+        "container_name": {"key": "containerName", "type": "str"},
+        "snapshot_path": {"key": "snapshotPath", "type": "str"},
+    }
+
+    def __init__(self, **kwargs: Any) -> None:
+        """ """
+        super().__init__(**kwargs)
+        self.format: str = "Coco"
+        self.container_name = None
+        self.snapshot_path = None
+
+
 class CodeConfiguration(_serialization.Model):
     """Configuration for a scoring code asset.
 
@@ -4261,7 +4972,7 @@ class CodeConfiguration(_serialization.Model):
         "scoring_script": {"key": "scoringScript", "type": "str"},
     }
 
-    def __init__(self, *, scoring_script: str, code_id: Optional[str] = None, **kwargs):
+    def __init__(self, *, scoring_script: str, code_id: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword code_id: ARM resource ID of the code asset.
         :paramtype code_id: str
@@ -4311,7 +5022,7 @@ class CodeContainer(Resource):
         "properties": {"key": "properties", "type": "CodeContainerProperties"},
     }
 
-    def __init__(self, *, properties: "_models.CodeContainerProperties", **kwargs):
+    def __init__(self, *, properties: "_models.CodeContainerProperties", **kwargs: Any) -> None:
         """
         :keyword properties: [Required] Additional attributes of the entity. Required.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.CodeContainerProperties
@@ -4337,11 +5048,16 @@ class CodeContainerProperties(AssetContainer):
     :vartype latest_version: str
     :ivar next_version: The next auto incremental version.
     :vartype next_version: str
+    :ivar provisioning_state: Provisioning state for the code container. Known values are:
+     "Succeeded", "Failed", "Canceled", "Creating", "Updating", and "Deleting".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.AssetProvisioningState
     """
 
     _validation = {
         "latest_version": {"readonly": True},
         "next_version": {"readonly": True},
+        "provisioning_state": {"readonly": True},
     }
 
     _attribute_map = {
@@ -4351,6 +5067,7 @@ class CodeContainerProperties(AssetContainer):
         "is_archived": {"key": "isArchived", "type": "bool"},
         "latest_version": {"key": "latestVersion", "type": "str"},
         "next_version": {"key": "nextVersion", "type": "str"},
+        "provisioning_state": {"key": "provisioningState", "type": "str"},
     }
 
     def __init__(
@@ -4360,8 +5077,8 @@ def __init__(
         properties: Optional[Dict[str, str]] = None,
         tags: Optional[Dict[str, str]] = None,
         is_archived: bool = False,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -4373,6 +5090,7 @@ def __init__(
         :paramtype is_archived: bool
         """
         super().__init__(description=description, properties=properties, tags=tags, is_archived=is_archived, **kwargs)
+        self.provisioning_state = None
 
 
 class CodeContainerResourceArmPaginatedResult(_serialization.Model):
@@ -4391,8 +5109,8 @@ class CodeContainerResourceArmPaginatedResult(_serialization.Model):
     }
 
     def __init__(
-        self, *, next_link: Optional[str] = None, value: Optional[List["_models.CodeContainer"]] = None, **kwargs
-    ):
+        self, *, next_link: Optional[str] = None, value: Optional[List["_models.CodeContainer"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of CodeContainer objects. If null, there are no
          additional pages.
@@ -4443,7 +5161,7 @@ class CodeVersion(Resource):
         "properties": {"key": "properties", "type": "CodeVersionProperties"},
     }
 
-    def __init__(self, *, properties: "_models.CodeVersionProperties", **kwargs):
+    def __init__(self, *, properties: "_models.CodeVersionProperties", **kwargs: Any) -> None:
         """
         :keyword properties: [Required] Additional attributes of the entity. Required.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.CodeVersionProperties
@@ -4455,6 +5173,8 @@ def __init__(self, *, properties: "_models.CodeVersionProperties", **kwargs):
 class CodeVersionProperties(AssetBase):
     """Code asset version details.
 
+    Variables are only populated by the server, and will be ignored when sending a request.
+
     :ivar description: The asset description text.
     :vartype description: str
     :ivar properties: The asset property dictionary.
@@ -4467,8 +5187,16 @@ class CodeVersionProperties(AssetBase):
     :vartype is_archived: bool
     :ivar code_uri: Uri where code is located.
     :vartype code_uri: str
+    :ivar provisioning_state: Provisioning state for the code version. Known values are:
+     "Succeeded", "Failed", "Canceled", "Creating", "Updating", and "Deleting".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.AssetProvisioningState
     """
 
+    _validation = {
+        "provisioning_state": {"readonly": True},
+    }
+
     _attribute_map = {
         "description": {"key": "description", "type": "str"},
         "properties": {"key": "properties", "type": "{str}"},
@@ -4476,6 +5204,7 @@ class CodeVersionProperties(AssetBase):
         "is_anonymous": {"key": "isAnonymous", "type": "bool"},
         "is_archived": {"key": "isArchived", "type": "bool"},
         "code_uri": {"key": "codeUri", "type": "str"},
+        "provisioning_state": {"key": "provisioningState", "type": "str"},
     }
 
     def __init__(
@@ -4487,8 +5216,8 @@ def __init__(
         is_anonymous: bool = False,
         is_archived: bool = False,
         code_uri: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -4512,6 +5241,7 @@ def __init__(
             **kwargs
         )
         self.code_uri = code_uri
+        self.provisioning_state = None
 
 
 class CodeVersionResourceArmPaginatedResult(_serialization.Model):
@@ -4530,8 +5260,8 @@ class CodeVersionResourceArmPaginatedResult(_serialization.Model):
     }
 
     def __init__(
-        self, *, next_link: Optional[str] = None, value: Optional[List["_models.CodeVersion"]] = None, **kwargs
-    ):
+        self, *, next_link: Optional[str] = None, value: Optional[List["_models.CodeVersion"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of CodeVersion objects. If null, there are no
          additional pages.
@@ -4559,7 +5289,7 @@ class ColumnTransformer(_serialization.Model):
         "parameters": {"key": "parameters", "type": "object"},
     }
 
-    def __init__(self, *, fields: Optional[List[str]] = None, parameters: Optional[JSON] = None, **kwargs):
+    def __init__(self, *, fields: Optional[List[str]] = None, parameters: Optional[JSON] = None, **kwargs: Any) -> None:
         """
         :keyword fields: Fields to apply transformer logic on.
         :paramtype fields: list[str]
@@ -4601,15 +5331,18 @@ class CommandJob(JobBaseProperties):  # pylint: disable=too-many-instance-attrib
     :ivar is_archived: Is the asset archived?.
     :vartype is_archived: bool
     :ivar job_type: [Required] Specifies the type of job. Required. Known values are: "AutoML",
-     "Command", "Sweep", and "Pipeline".
+     "Command", "Labeling", "Sweep", "Pipeline", and "Spark".
     :vartype job_type: str or ~azure.mgmt.machinelearningservices.models.JobType
     :ivar services: List of JobEndpoints.
      For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
     :vartype services: dict[str, ~azure.mgmt.machinelearningservices.models.JobService]
     :ivar status: Status of the job. Known values are: "NotStarted", "Starting", "Provisioning",
      "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed", "Failed",
-     "Canceled", "NotResponding", "Paused", and "Unknown".
+     "Canceled", "NotResponding", "Paused", "Unknown", and "Scheduled".
     :vartype status: str or ~azure.mgmt.machinelearningservices.models.JobStatus
+    :ivar autologger_settings: Distribution configuration of the job. If set, this should be one of
+     Mpi, Tensorflow, PyTorch, or null.
+    :vartype autologger_settings: ~azure.mgmt.machinelearningservices.models.AutologgerSettings
     :ivar code_id: ARM resource ID of the code asset.
     :vartype code_id: str
     :ivar command: [Required] The command to execute on startup of the job. eg. "python train.py".
@@ -4656,6 +5389,7 @@ class CommandJob(JobBaseProperties):  # pylint: disable=too-many-instance-attrib
         "job_type": {"key": "jobType", "type": "str"},
         "services": {"key": "services", "type": "{JobService}"},
         "status": {"key": "status", "type": "str"},
+        "autologger_settings": {"key": "autologgerSettings", "type": "AutologgerSettings"},
         "code_id": {"key": "codeId", "type": "str"},
         "command": {"key": "command", "type": "str"},
         "distribution": {"key": "distribution", "type": "DistributionConfiguration"},
@@ -4683,6 +5417,7 @@ def __init__(
         identity: Optional["_models.IdentityConfiguration"] = None,
         is_archived: bool = False,
         services: Optional[Dict[str, "_models.JobService"]] = None,
+        autologger_settings: Optional["_models.AutologgerSettings"] = None,
         code_id: Optional[str] = None,
         distribution: Optional["_models.DistributionConfiguration"] = None,
         environment_variables: Optional[Dict[str, str]] = None,
@@ -4690,8 +5425,8 @@ def __init__(
         limits: Optional["_models.CommandJobLimits"] = None,
         outputs: Optional[Dict[str, "_models.JobOutput"]] = None,
         resources: Optional["_models.JobResourceConfiguration"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -4717,6 +5452,9 @@ def __init__(
         :keyword services: List of JobEndpoints.
          For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
         :paramtype services: dict[str, ~azure.mgmt.machinelearningservices.models.JobService]
+        :keyword autologger_settings: Distribution configuration of the job. If set, this should be one
+         of Mpi, Tensorflow, PyTorch, or null.
+        :paramtype autologger_settings: ~azure.mgmt.machinelearningservices.models.AutologgerSettings
         :keyword code_id: ARM resource ID of the code asset.
         :paramtype code_id: str
         :keyword command: [Required] The command to execute on startup of the job. eg. "python
@@ -4753,6 +5491,7 @@ def __init__(
             **kwargs
         )
         self.job_type: str = "Command"
+        self.autologger_settings = autologger_settings
         self.code_id = code_id
         self.command = command
         self.distribution = distribution
@@ -4792,7 +5531,7 @@ class JobLimits(_serialization.Model):
 
     _subtype_map = {"job_limits_type": {"Command": "CommandJobLimits", "Sweep": "SweepJobLimits"}}
 
-    def __init__(self, *, timeout: Optional[datetime.timedelta] = None, **kwargs):
+    def __init__(self, *, timeout: Optional[datetime.timedelta] = None, **kwargs: Any) -> None:
         """
         :keyword timeout: The max run duration in ISO 8601 format, after which the job will be
          cancelled. Only supports duration with precision as low as Seconds.
@@ -4825,7 +5564,7 @@ class CommandJobLimits(JobLimits):
         "timeout": {"key": "timeout", "type": "duration"},
     }
 
-    def __init__(self, *, timeout: Optional[datetime.timedelta] = None, **kwargs):
+    def __init__(self, *, timeout: Optional[datetime.timedelta] = None, **kwargs: Any) -> None:
         """
         :keyword timeout: The max run duration in ISO 8601 format, after which the job will be
          cancelled. Only supports duration with precision as low as Seconds.
@@ -4873,7 +5612,7 @@ class ComponentContainer(Resource):
         "properties": {"key": "properties", "type": "ComponentContainerProperties"},
     }
 
-    def __init__(self, *, properties: "_models.ComponentContainerProperties", **kwargs):
+    def __init__(self, *, properties: "_models.ComponentContainerProperties", **kwargs: Any) -> None:
         """
         :keyword properties: [Required] Additional attributes of the entity. Required.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.ComponentContainerProperties
@@ -4888,27 +5627,34 @@ class ComponentContainerProperties(AssetContainer):
 
     .. raw:: html
 
-       <see href="https://docs.microsoft.com/en-us/azure/machine-learning/reference-yaml-component-command" />.
+       <see
+    href="https://docs.microsoft.com/en-us/azure/machine-learning/reference-yaml-component-command"
+    />.
 
-        Variables are only populated by the server, and will be ignored when sending a request.
+    Variables are only populated by the server, and will be ignored when sending a request.
 
-        :ivar description: The asset description text.
-        :vartype description: str
-        :ivar properties: The asset property dictionary.
-        :vartype properties: dict[str, str]
-        :ivar tags: Tag dictionary. Tags can be added, removed, and updated.
-        :vartype tags: dict[str, str]
-        :ivar is_archived: Is the asset archived?.
-        :vartype is_archived: bool
-        :ivar latest_version: The latest version inside this container.
-        :vartype latest_version: str
-        :ivar next_version: The next auto incremental version.
-        :vartype next_version: str
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar latest_version: The latest version inside this container.
+    :vartype latest_version: str
+    :ivar next_version: The next auto incremental version.
+    :vartype next_version: str
+    :ivar provisioning_state: Provisioning state for the component container. Known values are:
+     "Succeeded", "Failed", "Canceled", "Creating", "Updating", and "Deleting".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.AssetProvisioningState
     """
 
     _validation = {
         "latest_version": {"readonly": True},
         "next_version": {"readonly": True},
+        "provisioning_state": {"readonly": True},
     }
 
     _attribute_map = {
@@ -4918,6 +5664,7 @@ class ComponentContainerProperties(AssetContainer):
         "is_archived": {"key": "isArchived", "type": "bool"},
         "latest_version": {"key": "latestVersion", "type": "str"},
         "next_version": {"key": "nextVersion", "type": "str"},
+        "provisioning_state": {"key": "provisioningState", "type": "str"},
     }
 
     def __init__(
@@ -4927,8 +5674,8 @@ def __init__(
         properties: Optional[Dict[str, str]] = None,
         tags: Optional[Dict[str, str]] = None,
         is_archived: bool = False,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -4940,6 +5687,7 @@ def __init__(
         :paramtype is_archived: bool
         """
         super().__init__(description=description, properties=properties, tags=tags, is_archived=is_archived, **kwargs)
+        self.provisioning_state = None
 
 
 class ComponentContainerResourceArmPaginatedResult(_serialization.Model):
@@ -4958,8 +5706,12 @@ class ComponentContainerResourceArmPaginatedResult(_serialization.Model):
     }
 
     def __init__(
-        self, *, next_link: Optional[str] = None, value: Optional[List["_models.ComponentContainer"]] = None, **kwargs
-    ):
+        self,
+        *,
+        next_link: Optional[str] = None,
+        value: Optional[List["_models.ComponentContainer"]] = None,
+        **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of ComponentContainer objects. If null, there are
          no additional pages.
@@ -5010,7 +5762,7 @@ class ComponentVersion(Resource):
         "properties": {"key": "properties", "type": "ComponentVersionProperties"},
     }
 
-    def __init__(self, *, properties: "_models.ComponentVersionProperties", **kwargs):
+    def __init__(self, *, properties: "_models.ComponentVersionProperties", **kwargs: Any) -> None:
         """
         :keyword properties: [Required] Additional attributes of the entity. Required.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.ComponentVersionProperties
@@ -5022,6 +5774,8 @@ def __init__(self, *, properties: "_models.ComponentVersionProperties", **kwargs
 class ComponentVersionProperties(AssetBase):
     """Definition of a component version: defines resources that span component types.
 
+    Variables are only populated by the server, and will be ignored when sending a request.
+
     :ivar description: The asset description text.
     :vartype description: str
     :ivar properties: The asset property dictionary.
@@ -5041,8 +5795,16 @@ class ComponentVersionProperties(AssetBase):
      href="https://docs.microsoft.com/en-us/azure/machine-learning/reference-yaml-component-command"
      />.
     :vartype component_spec: JSON
+    :ivar provisioning_state: Provisioning state for the component version. Known values are:
+     "Succeeded", "Failed", "Canceled", "Creating", "Updating", and "Deleting".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.AssetProvisioningState
     """
 
+    _validation = {
+        "provisioning_state": {"readonly": True},
+    }
+
     _attribute_map = {
         "description": {"key": "description", "type": "str"},
         "properties": {"key": "properties", "type": "{str}"},
@@ -5050,6 +5812,7 @@ class ComponentVersionProperties(AssetBase):
         "is_anonymous": {"key": "isAnonymous", "type": "bool"},
         "is_archived": {"key": "isArchived", "type": "bool"},
         "component_spec": {"key": "componentSpec", "type": "object"},
+        "provisioning_state": {"key": "provisioningState", "type": "str"},
     }
 
     def __init__(
@@ -5061,8 +5824,8 @@ def __init__(
         is_anonymous: bool = False,
         is_archived: bool = False,
         component_spec: Optional[JSON] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -5093,6 +5856,7 @@ def __init__(
             **kwargs
         )
         self.component_spec = component_spec
+        self.provisioning_state = None
 
 
 class ComponentVersionResourceArmPaginatedResult(_serialization.Model):
@@ -5111,8 +5875,12 @@ class ComponentVersionResourceArmPaginatedResult(_serialization.Model):
     }
 
     def __init__(
-        self, *, next_link: Optional[str] = None, value: Optional[List["_models.ComponentVersion"]] = None, **kwargs
-    ):
+        self,
+        *,
+        next_link: Optional[str] = None,
+        value: Optional[List["_models.ComponentVersion"]] = None,
+        **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of ComponentVersion objects. If null, there are
          no additional pages.
@@ -5136,7 +5904,7 @@ class ComputeInstanceSchema(_serialization.Model):
         "properties": {"key": "properties", "type": "ComputeInstanceProperties"},
     }
 
-    def __init__(self, *, properties: Optional["_models.ComputeInstanceProperties"] = None, **kwargs):
+    def __init__(self, *, properties: Optional["_models.ComputeInstanceProperties"] = None, **kwargs: Any) -> None:
         """
         :keyword properties: Properties of ComputeInstance.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.ComputeInstanceProperties
@@ -5162,7 +5930,7 @@ class ComputeInstance(Compute, ComputeInstanceSchema):  # pylint: disable=too-ma
     :vartype compute_location: str
     :ivar provisioning_state: The provision state of the cluster. Valid values are Unknown,
      Updating, Provisioning, Succeeded, and Failed. Known values are: "Unknown", "Updating",
-     "Creating", "Deleting", "Succeeded", "Failed", and "Canceled".
+     "Creating", "Deleting", "Succeeded", "Failed", "Canceled", and "SoftDeleted".
     :vartype provisioning_state: str or
      ~azure.mgmt.machinelearningservices.models.ProvisioningState
     :ivar description: The description of the Machine Learning compute.
@@ -5214,8 +5982,8 @@ def __init__(
         description: Optional[str] = None,
         resource_id: Optional[str] = None,
         disable_local_auth: Optional[bool] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword properties: Properties of ComputeInstance.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.ComputeInstanceProperties
@@ -5264,7 +6032,9 @@ class ComputeInstanceApplication(_serialization.Model):
         "endpoint_uri": {"key": "endpointUri", "type": "str"},
     }
 
-    def __init__(self, *, display_name: Optional[str] = None, endpoint_uri: Optional[str] = None, **kwargs):
+    def __init__(
+        self, *, display_name: Optional[str] = None, endpoint_uri: Optional[str] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword display_name: Name of the ComputeInstance application.
         :paramtype display_name: str
@@ -5276,14 +6046,39 @@ def __init__(self, *, display_name: Optional[str] = None, endpoint_uri: Optional
         self.endpoint_uri = endpoint_uri
 
 
-class ComputeInstanceConnectivityEndpoints(_serialization.Model):
-    """Defines all connectivity endpoints and properties for an ComputeInstance.
+class ComputeInstanceAutologgerSettings(_serialization.Model):
+    """Specifies settings for autologger.
 
-    Variables are only populated by the server, and will be ignored when sending a request.
+    :ivar mlflow_autologger: Indicates whether mlflow autologger is enabled for notebooks. Known
+     values are: "Enabled" and "Disabled".
+    :vartype mlflow_autologger: str or ~azure.mgmt.machinelearningservices.models.MlflowAutologger
+    """
 
-    :ivar public_ip_address: Public IP Address of this ComputeInstance.
-    :vartype public_ip_address: str
-    :ivar private_ip_address: Private IP Address of this ComputeInstance (local to the VNET in
+    _attribute_map = {
+        "mlflow_autologger": {"key": "mlflowAutologger", "type": "str"},
+    }
+
+    def __init__(
+        self, *, mlflow_autologger: Optional[Union[str, "_models.MlflowAutologger"]] = None, **kwargs: Any
+    ) -> None:
+        """
+        :keyword mlflow_autologger: Indicates whether mlflow autologger is enabled for notebooks. Known
+         values are: "Enabled" and "Disabled".
+        :paramtype mlflow_autologger: str or
+         ~azure.mgmt.machinelearningservices.models.MlflowAutologger
+        """
+        super().__init__(**kwargs)
+        self.mlflow_autologger = mlflow_autologger
+
+
+class ComputeInstanceConnectivityEndpoints(_serialization.Model):
+    """Defines all connectivity endpoints and properties for an ComputeInstance.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar public_ip_address: Public IP Address of this ComputeInstance.
+    :vartype public_ip_address: str
+    :ivar private_ip_address: Private IP Address of this ComputeInstance (local to the VNET in
      which the compute instance is deployed).
     :vartype private_ip_address: str
     """
@@ -5298,7 +6093,7 @@ class ComputeInstanceConnectivityEndpoints(_serialization.Model):
         "private_ip_address": {"key": "privateIpAddress", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.public_ip_address = None
@@ -5345,8 +6140,8 @@ def __init__(
         gpu: Optional[str] = None,
         network: Optional[Union[str, "_models.Network"]] = None,
         environment: Optional["_models.ComputeInstanceEnvironmentInfo"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword name: Name of the ComputeInstance container.
         :paramtype name: str
@@ -5394,7 +6189,7 @@ class ComputeInstanceCreatedBy(_serialization.Model):
         "user_id": {"key": "userId", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.user_name = None
@@ -5433,8 +6228,8 @@ def __init__(
         disk_size_gb: Optional[int] = None,
         lun: Optional[int] = None,
         storage_account_type: Union[str, "_models.StorageAccountType"] = "Standard_LRS",
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword caching: Caching type of Data Disk. Known values are: "None", "ReadOnly", and
          "ReadWrite".
@@ -5504,8 +6299,8 @@ def __init__(
         mount_state: Optional[Union[str, "_models.MountState"]] = None,
         mounted_on: Optional[datetime.datetime] = None,
         error: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword source: Source of the ComputeInstance data mount.
         :paramtype source: str
@@ -5553,7 +6348,7 @@ class ComputeInstanceEnvironmentInfo(_serialization.Model):
         "version": {"key": "version", "type": "str"},
     }
 
-    def __init__(self, *, name: Optional[str] = None, version: Optional[str] = None, **kwargs):
+    def __init__(self, *, name: Optional[str] = None, version: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword name: name of environment.
         :paramtype name: str
@@ -5596,8 +6391,8 @@ def __init__(
         operation_time: Optional[datetime.datetime] = None,
         operation_status: Optional[Union[str, "_models.OperationStatus"]] = None,
         operation_trigger: Optional[Union[str, "_models.OperationTrigger"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword operation_name: Name of the last operation. Known values are: "Create", "Start",
          "Stop", "Restart", "Reimage", and "Delete".
@@ -5635,8 +6430,16 @@ class ComputeInstanceProperties(_serialization.Model):  # pylint: disable=too-ma
      depending on his/her assigned role. Known values are: "Personal" and "Shared".
     :vartype application_sharing_policy: str or
      ~azure.mgmt.machinelearningservices.models.ApplicationSharingPolicy
+    :ivar autologger_settings: Specifies settings for autologger.
+    :vartype autologger_settings:
+     ~azure.mgmt.machinelearningservices.models.ComputeInstanceAutologgerSettings
     :ivar ssh_settings: Specifies policy and settings for SSH access.
     :vartype ssh_settings: ~azure.mgmt.machinelearningservices.models.ComputeInstanceSshSettings
+    :ivar custom_services: List of Custom Services added to the compute.
+    :vartype custom_services: list[~azure.mgmt.machinelearningservices.models.CustomService]
+    :ivar os_image_metadata: Returns metadata about the operating system image for this compute
+     instance.
+    :vartype os_image_metadata: ~azure.mgmt.machinelearningservices.models.ImageMetadata
     :ivar connectivity_endpoints: Describes all connectivity endpoints available for this
      ComputeInstance.
     :vartype connectivity_endpoints:
@@ -5668,6 +6471,9 @@ class ComputeInstanceProperties(_serialization.Model):  # pylint: disable=too-ma
      ~azure.mgmt.machinelearningservices.models.ComputeInstanceLastOperation
     :ivar schedules: The list of schedules to be applied on the computes.
     :vartype schedules: ~azure.mgmt.machinelearningservices.models.ComputeSchedules
+    :ivar idle_time_before_shutdown: Stops compute instance after user defined period of
+     inactivity. Time is defined in ISO8601 format. Minimum is 15 min, maximum is 3 days.
+    :vartype idle_time_before_shutdown: str
     :ivar enable_node_public_ip: Enable or disable node public IP address provisioning. Possible
      values are: Possible values are: true - Indicates that the compute nodes will have public IPs
      provisioned. false - Indicates that the compute nodes will have a private endpoint and no
@@ -5684,13 +6490,13 @@ class ComputeInstanceProperties(_serialization.Model):  # pylint: disable=too-ma
     """
 
     _validation = {
+        "os_image_metadata": {"readonly": True},
         "connectivity_endpoints": {"readonly": True},
         "applications": {"readonly": True},
         "created_by": {"readonly": True},
         "errors": {"readonly": True},
         "state": {"readonly": True},
         "last_operation": {"readonly": True},
-        "schedules": {"readonly": True},
         "containers": {"readonly": True},
         "data_disks": {"readonly": True},
         "data_mounts": {"readonly": True},
@@ -5701,7 +6507,10 @@ class ComputeInstanceProperties(_serialization.Model):  # pylint: disable=too-ma
         "vm_size": {"key": "vmSize", "type": "str"},
         "subnet": {"key": "subnet", "type": "ResourceId"},
         "application_sharing_policy": {"key": "applicationSharingPolicy", "type": "str"},
+        "autologger_settings": {"key": "autologgerSettings", "type": "ComputeInstanceAutologgerSettings"},
         "ssh_settings": {"key": "sshSettings", "type": "ComputeInstanceSshSettings"},
+        "custom_services": {"key": "customServices", "type": "[CustomService]"},
+        "os_image_metadata": {"key": "osImageMetadata", "type": "ImageMetadata"},
         "connectivity_endpoints": {"key": "connectivityEndpoints", "type": "ComputeInstanceConnectivityEndpoints"},
         "applications": {"key": "applications", "type": "[ComputeInstanceApplication]"},
         "created_by": {"key": "createdBy", "type": "ComputeInstanceCreatedBy"},
@@ -5715,6 +6524,7 @@ class ComputeInstanceProperties(_serialization.Model):  # pylint: disable=too-ma
         "setup_scripts": {"key": "setupScripts", "type": "SetupScripts"},
         "last_operation": {"key": "lastOperation", "type": "ComputeInstanceLastOperation"},
         "schedules": {"key": "schedules", "type": "ComputeSchedules"},
+        "idle_time_before_shutdown": {"key": "idleTimeBeforeShutdown", "type": "str"},
         "enable_node_public_ip": {"key": "enableNodePublicIp", "type": "bool"},
         "containers": {"key": "containers", "type": "[ComputeInstanceContainer]"},
         "data_disks": {"key": "dataDisks", "type": "[ComputeInstanceDataDisk]"},
@@ -5728,13 +6538,17 @@ def __init__(
         vm_size: Optional[str] = None,
         subnet: Optional["_models.ResourceId"] = None,
         application_sharing_policy: Union[str, "_models.ApplicationSharingPolicy"] = "Shared",
+        autologger_settings: Optional["_models.ComputeInstanceAutologgerSettings"] = None,
         ssh_settings: Optional["_models.ComputeInstanceSshSettings"] = None,
+        custom_services: Optional[List["_models.CustomService"]] = None,
         compute_instance_authorization_type: Union[str, "_models.ComputeInstanceAuthorizationType"] = "personal",
         personal_compute_instance_settings: Optional["_models.PersonalComputeInstanceSettings"] = None,
         setup_scripts: Optional["_models.SetupScripts"] = None,
+        schedules: Optional["_models.ComputeSchedules"] = None,
+        idle_time_before_shutdown: Optional[str] = None,
         enable_node_public_ip: Optional[bool] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword vm_size: Virtual Machine Size.
         :paramtype vm_size: str
@@ -5746,8 +6560,13 @@ def __init__(
          depending on his/her assigned role. Known values are: "Personal" and "Shared".
         :paramtype application_sharing_policy: str or
          ~azure.mgmt.machinelearningservices.models.ApplicationSharingPolicy
+        :keyword autologger_settings: Specifies settings for autologger.
+        :paramtype autologger_settings:
+         ~azure.mgmt.machinelearningservices.models.ComputeInstanceAutologgerSettings
         :keyword ssh_settings: Specifies policy and settings for SSH access.
         :paramtype ssh_settings: ~azure.mgmt.machinelearningservices.models.ComputeInstanceSshSettings
+        :keyword custom_services: List of Custom Services added to the compute.
+        :paramtype custom_services: list[~azure.mgmt.machinelearningservices.models.CustomService]
         :keyword compute_instance_authorization_type: The Compute Instance Authorization type.
          Available values are personal (default). "personal"
         :paramtype compute_instance_authorization_type: str or
@@ -5757,6 +6576,11 @@ def __init__(
          ~azure.mgmt.machinelearningservices.models.PersonalComputeInstanceSettings
         :keyword setup_scripts: Details of customized scripts to execute for setting up the cluster.
         :paramtype setup_scripts: ~azure.mgmt.machinelearningservices.models.SetupScripts
+        :keyword schedules: The list of schedules to be applied on the computes.
+        :paramtype schedules: ~azure.mgmt.machinelearningservices.models.ComputeSchedules
+        :keyword idle_time_before_shutdown: Stops compute instance after user defined period of
+         inactivity. Time is defined in ISO8601 format. Minimum is 15 min, maximum is 3 days.
+        :paramtype idle_time_before_shutdown: str
         :keyword enable_node_public_ip: Enable or disable node public IP address provisioning. Possible
          values are: Possible values are: true - Indicates that the compute nodes will have public IPs
          provisioned. false - Indicates that the compute nodes will have a private endpoint and no
@@ -5767,7 +6591,10 @@ def __init__(
         self.vm_size = vm_size
         self.subnet = subnet
         self.application_sharing_policy = application_sharing_policy
+        self.autologger_settings = autologger_settings
         self.ssh_settings = ssh_settings
+        self.custom_services = custom_services
+        self.os_image_metadata = None
         self.connectivity_endpoints = None
         self.applications = None
         self.created_by = None
@@ -5777,7 +6604,8 @@ def __init__(
         self.personal_compute_instance_settings = personal_compute_instance_settings
         self.setup_scripts = setup_scripts
         self.last_operation = None
-        self.schedules = None
+        self.schedules = schedules
+        self.idle_time_before_shutdown = idle_time_before_shutdown
         self.enable_node_public_ip = enable_node_public_ip
         self.containers = None
         self.data_disks = None
@@ -5821,8 +6649,8 @@ def __init__(
         *,
         ssh_public_access: Union[str, "_models.SshPublicAccess"] = "Disabled",
         admin_public_key: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword ssh_public_access: State of the public SSH port. Possible values are: Disabled -
          Indicates that the public ssh port is closed on this instance. Enabled - Indicates that the
@@ -5851,7 +6679,7 @@ class ComputeInstanceVersion(_serialization.Model):
         "runtime": {"key": "runtime", "type": "str"},
     }
 
-    def __init__(self, *, runtime: Optional[str] = None, **kwargs):
+    def __init__(self, *, runtime: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword runtime: Runtime of compute instance.
         :paramtype runtime: str
@@ -5871,7 +6699,7 @@ class ComputeResourceSchema(_serialization.Model):
         "properties": {"key": "properties", "type": "Compute"},
     }
 
-    def __init__(self, *, properties: Optional["_models.Compute"] = None, **kwargs):
+    def __init__(self, *, properties: Optional["_models.Compute"] = None, **kwargs: Any) -> None:
         """
         :keyword properties: Compute properties.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.Compute
@@ -5935,8 +6763,8 @@ def __init__(
         location: Optional[str] = None,
         tags: Optional[Dict[str, str]] = None,
         sku: Optional["_models.Sku"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword properties: Compute properties.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.Compute
@@ -5973,7 +6801,9 @@ class ComputeSchedules(_serialization.Model):
         "compute_start_stop": {"key": "computeStartStop", "type": "[ComputeStartStopSchedule]"},
     }
 
-    def __init__(self, *, compute_start_stop: Optional[List["_models.ComputeStartStopSchedule"]] = None, **kwargs):
+    def __init__(
+        self, *, compute_start_stop: Optional[List["_models.ComputeStartStopSchedule"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword compute_start_stop: The list of compute start stop schedules to be applied.
         :paramtype compute_start_stop:
@@ -6034,8 +6864,8 @@ def __init__(
         recurrence: Optional["_models.RecurrenceTrigger"] = None,
         cron: Optional["_models.CronTrigger"] = None,
         schedule: Optional["_models.ScheduleBase"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword status: Is the schedule enabled or disabled?. Known values are: "Enabled" and
          "Disabled".
@@ -6084,8 +6914,8 @@ def __init__(
         *,
         container_resource_limits: Optional["_models.ContainerResourceSettings"] = None,
         container_resource_requests: Optional["_models.ContainerResourceSettings"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword container_resource_limits: Container resource limit info:.
         :paramtype container_resource_limits:
@@ -6119,7 +6949,9 @@ class ContainerResourceSettings(_serialization.Model):
         "memory": {"key": "memory", "type": "str"},
     }
 
-    def __init__(self, *, cpu: Optional[str] = None, gpu: Optional[str] = None, memory: Optional[str] = None, **kwargs):
+    def __init__(
+        self, *, cpu: Optional[str] = None, gpu: Optional[str] = None, memory: Optional[str] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword cpu: Number of vCPUs request/limit for container. More info:
          https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container/.
@@ -6148,7 +6980,7 @@ class CosmosDbSettings(_serialization.Model):
         "collections_throughput": {"key": "collectionsThroughput", "type": "int"},
     }
 
-    def __init__(self, *, collections_throughput: Optional[int] = None, **kwargs):
+    def __init__(self, *, collections_throughput: Optional[int] = None, **kwargs: Any) -> None:
         """
         :keyword collections_throughput: The throughput of the collections in cosmosdb database.
         :paramtype collections_throughput: int
@@ -6195,8 +7027,8 @@ class TriggerBase(_serialization.Model):
     _subtype_map = {"trigger_type": {"Cron": "CronTrigger", "Recurrence": "RecurrenceTrigger"}}
 
     def __init__(
-        self, *, end_time: Optional[str] = None, start_time: Optional[str] = None, time_zone: str = "UTC", **kwargs
-    ):
+        self, *, end_time: Optional[str] = None, start_time: Optional[str] = None, time_zone: str = "UTC", **kwargs: Any
+    ) -> None:
         """
         :keyword end_time: Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer
          https://en.wikipedia.org/wiki/ISO_8601.
@@ -6262,8 +7094,8 @@ def __init__(
         end_time: Optional[str] = None,
         start_time: Optional[str] = None,
         time_zone: str = "UTC",
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword end_time: Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer
          https://en.wikipedia.org/wiki/ISO_8601.
@@ -6286,6 +7118,58 @@ def __init__(
         self.expression = expression
 
 
+class CsvExportSummary(ExportSummary):
+    """CsvExportSummary.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar end_date_time: The time when the export was completed.
+    :vartype end_date_time: ~datetime.datetime
+    :ivar exported_row_count: The total number of labeled datapoints exported.
+    :vartype exported_row_count: int
+    :ivar format: [Required] The format of exported labels, also as the discriminator. Required.
+     Known values are: "Dataset", "Coco", and "CSV".
+    :vartype format: str or ~azure.mgmt.machinelearningservices.models.ExportFormatType
+    :ivar labeling_job_id: Name and identifier of the job containing exported labels.
+    :vartype labeling_job_id: str
+    :ivar start_date_time: The time when the export was requested.
+    :vartype start_date_time: ~datetime.datetime
+    :ivar container_name: The container name to which the labels will be exported.
+    :vartype container_name: str
+    :ivar snapshot_path: The output path where the labels will be exported.
+    :vartype snapshot_path: str
+    """
+
+    _validation = {
+        "end_date_time": {"readonly": True},
+        "exported_row_count": {"readonly": True},
+        "format": {"required": True},
+        "labeling_job_id": {"readonly": True},
+        "start_date_time": {"readonly": True},
+        "container_name": {"readonly": True},
+        "snapshot_path": {"readonly": True},
+    }
+
+    _attribute_map = {
+        "end_date_time": {"key": "endDateTime", "type": "iso-8601"},
+        "exported_row_count": {"key": "exportedRowCount", "type": "int"},
+        "format": {"key": "format", "type": "str"},
+        "labeling_job_id": {"key": "labelingJobId", "type": "str"},
+        "start_date_time": {"key": "startDateTime", "type": "iso-8601"},
+        "container_name": {"key": "containerName", "type": "str"},
+        "snapshot_path": {"key": "snapshotPath", "type": "str"},
+    }
+
+    def __init__(self, **kwargs: Any) -> None:
+        """ """
+        super().__init__(**kwargs)
+        self.format: str = "CSV"
+        self.container_name = None
+        self.snapshot_path = None
+
+
 class CustomForecastHorizon(ForecastHorizon):
     """The desired maximum forecast horizon in units of time-series frequency.
 
@@ -6308,7 +7192,7 @@ class CustomForecastHorizon(ForecastHorizon):
         "value": {"key": "value", "type": "int"},
     }
 
-    def __init__(self, *, value: int, **kwargs):
+    def __init__(self, *, value: int, **kwargs: Any) -> None:
         """
         :keyword value: [Required] Forecast horizon value. Required.
         :paramtype value: int
@@ -6356,7 +7240,7 @@ class JobInput(_serialization.Model):
         }
     }
 
-    def __init__(self, *, description: Optional[str] = None, **kwargs):
+    def __init__(self, *, description: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword description: Description for the input.
         :paramtype description: str
@@ -6402,8 +7286,8 @@ def __init__(
         uri: str,
         description: Optional[str] = None,
         mode: Optional[Union[str, "_models.InputDeliveryMode"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: Description for the input.
         :paramtype description: str
@@ -6456,7 +7340,7 @@ class JobOutput(_serialization.Model):
         }
     }
 
-    def __init__(self, *, description: Optional[str] = None, **kwargs):
+    def __init__(self, *, description: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword description: Description for the output.
         :paramtype description: str
@@ -6476,7 +7360,12 @@ class CustomModelJobOutput(AssetJobOutput, JobOutput):
     :ivar job_output_type: [Required] Specifies the type of job. Required. Known values are:
      "uri_file", "uri_folder", "mltable", "custom_model", "mlflow_model", and "triton_model".
     :vartype job_output_type: str or ~azure.mgmt.machinelearningservices.models.JobOutputType
-    :ivar mode: Output Asset Delivery Mode. Known values are: "ReadWriteMount" and "Upload".
+    :ivar asset_name: Output Asset Name.
+    :vartype asset_name: str
+    :ivar asset_version: Output Asset Version.
+    :vartype asset_version: str
+    :ivar mode: Output Asset Delivery Mode. Known values are: "ReadWriteMount", "Upload", and
+     "Direct".
     :vartype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
     :ivar uri: Output Asset URI.
     :vartype uri: str
@@ -6489,6 +7378,8 @@ class CustomModelJobOutput(AssetJobOutput, JobOutput):
     _attribute_map = {
         "description": {"key": "description", "type": "str"},
         "job_output_type": {"key": "jobOutputType", "type": "str"},
+        "asset_name": {"key": "assetName", "type": "str"},
+        "asset_version": {"key": "assetVersion", "type": "str"},
         "mode": {"key": "mode", "type": "str"},
         "uri": {"key": "uri", "type": "str"},
     }
@@ -6497,21 +7388,32 @@ def __init__(
         self,
         *,
         description: Optional[str] = None,
+        asset_name: Optional[str] = None,
+        asset_version: Optional[str] = None,
         mode: Optional[Union[str, "_models.OutputDeliveryMode"]] = None,
         uri: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: Description for the output.
         :paramtype description: str
-        :keyword mode: Output Asset Delivery Mode. Known values are: "ReadWriteMount" and "Upload".
+        :keyword asset_name: Output Asset Name.
+        :paramtype asset_name: str
+        :keyword asset_version: Output Asset Version.
+        :paramtype asset_version: str
+        :keyword mode: Output Asset Delivery Mode. Known values are: "ReadWriteMount", "Upload", and
+         "Direct".
         :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
         :keyword uri: Output Asset URI.
         :paramtype uri: str
         """
-        super().__init__(mode=mode, uri=uri, description=description, **kwargs)
+        super().__init__(
+            asset_name=asset_name, asset_version=asset_version, mode=mode, uri=uri, description=description, **kwargs
+        )
         self.description = description
         self.job_output_type: str = "custom_model"
+        self.asset_name = asset_name
+        self.asset_version = asset_version
         self.mode = mode
         self.uri = uri
 
@@ -6538,7 +7440,7 @@ class CustomNCrossValidations(NCrossValidations):
         "value": {"key": "value", "type": "int"},
     }
 
-    def __init__(self, *, value: int, **kwargs):
+    def __init__(self, *, value: int, **kwargs: Any) -> None:
         """
         :keyword value: [Required] N-Cross validations value. Required.
         :paramtype value: int
@@ -6569,7 +7471,7 @@ class CustomSeasonality(Seasonality):
         "value": {"key": "value", "type": "int"},
     }
 
-    def __init__(self, *, value: int, **kwargs):
+    def __init__(self, *, value: int, **kwargs: Any) -> None:
         """
         :keyword value: [Required] Seasonality value. Required.
         :paramtype value: int
@@ -6579,6 +7481,77 @@ def __init__(self, *, value: int, **kwargs):
         self.value = value
 
 
+class CustomService(_serialization.Model):
+    """Specifies the custom service configuration.
+
+    :ivar additional_properties: Unmatched properties from the message are deserialized to this
+     collection.
+    :vartype additional_properties: dict[str, any]
+    :ivar name: Name of the Custom Service.
+    :vartype name: str
+    :ivar image: Describes the Image Specifications.
+    :vartype image: ~azure.mgmt.machinelearningservices.models.Image
+    :ivar environment_variables: Environment Variable for the container.
+    :vartype environment_variables: dict[str,
+     ~azure.mgmt.machinelearningservices.models.EnvironmentVariable]
+    :ivar docker: Describes the docker settings for the image.
+    :vartype docker: ~azure.mgmt.machinelearningservices.models.Docker
+    :ivar endpoints: Configuring the endpoints for the container.
+    :vartype endpoints: list[~azure.mgmt.machinelearningservices.models.Endpoint]
+    :ivar volumes: Configuring the volumes for the container.
+    :vartype volumes: list[~azure.mgmt.machinelearningservices.models.VolumeDefinition]
+    """
+
+    _attribute_map = {
+        "additional_properties": {"key": "", "type": "{object}"},
+        "name": {"key": "name", "type": "str"},
+        "image": {"key": "image", "type": "Image"},
+        "environment_variables": {"key": "environmentVariables", "type": "{EnvironmentVariable}"},
+        "docker": {"key": "docker", "type": "Docker"},
+        "endpoints": {"key": "endpoints", "type": "[Endpoint]"},
+        "volumes": {"key": "volumes", "type": "[VolumeDefinition]"},
+    }
+
+    def __init__(
+        self,
+        *,
+        additional_properties: Optional[Dict[str, Any]] = None,
+        name: Optional[str] = None,
+        image: Optional["_models.Image"] = None,
+        environment_variables: Optional[Dict[str, "_models.EnvironmentVariable"]] = None,
+        docker: Optional["_models.Docker"] = None,
+        endpoints: Optional[List["_models.Endpoint"]] = None,
+        volumes: Optional[List["_models.VolumeDefinition"]] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword additional_properties: Unmatched properties from the message are deserialized to this
+         collection.
+        :paramtype additional_properties: dict[str, any]
+        :keyword name: Name of the Custom Service.
+        :paramtype name: str
+        :keyword image: Describes the Image Specifications.
+        :paramtype image: ~azure.mgmt.machinelearningservices.models.Image
+        :keyword environment_variables: Environment Variable for the container.
+        :paramtype environment_variables: dict[str,
+         ~azure.mgmt.machinelearningservices.models.EnvironmentVariable]
+        :keyword docker: Describes the docker settings for the image.
+        :paramtype docker: ~azure.mgmt.machinelearningservices.models.Docker
+        :keyword endpoints: Configuring the endpoints for the container.
+        :paramtype endpoints: list[~azure.mgmt.machinelearningservices.models.Endpoint]
+        :keyword volumes: Configuring the volumes for the container.
+        :paramtype volumes: list[~azure.mgmt.machinelearningservices.models.VolumeDefinition]
+        """
+        super().__init__(**kwargs)
+        self.additional_properties = additional_properties
+        self.name = name
+        self.image = image
+        self.environment_variables = environment_variables
+        self.docker = docker
+        self.endpoints = endpoints
+        self.volumes = volumes
+
+
 class CustomTargetLags(TargetLags):
     """CustomTargetLags.
 
@@ -6601,7 +7574,7 @@ class CustomTargetLags(TargetLags):
         "values": {"key": "values", "type": "[int]"},
     }
 
-    def __init__(self, *, values: List[int], **kwargs):
+    def __init__(self, *, values: List[int], **kwargs: Any) -> None:
         """
         :keyword values: [Required] Set target lags values. Required.
         :paramtype values: list[int]
@@ -6633,7 +7606,7 @@ class CustomTargetRollingWindowSize(TargetRollingWindowSize):
         "value": {"key": "value", "type": "int"},
     }
 
-    def __init__(self, *, value: int, **kwargs):
+    def __init__(self, *, value: int, **kwargs: Any) -> None:
         """
         :keyword value: [Required] TargetRollingWindowSize value. Required.
         :paramtype value: int
@@ -6654,7 +7627,7 @@ class DatabricksSchema(_serialization.Model):
         "properties": {"key": "properties", "type": "DatabricksProperties"},
     }
 
-    def __init__(self, *, properties: Optional["_models.DatabricksProperties"] = None, **kwargs):
+    def __init__(self, *, properties: Optional["_models.DatabricksProperties"] = None, **kwargs: Any) -> None:
         """
         :keyword properties: Properties of Databricks.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.DatabricksProperties
@@ -6680,7 +7653,7 @@ class Databricks(Compute, DatabricksSchema):  # pylint: disable=too-many-instanc
     :vartype compute_location: str
     :ivar provisioning_state: The provision state of the cluster. Valid values are Unknown,
      Updating, Provisioning, Succeeded, and Failed. Known values are: "Unknown", "Updating",
-     "Creating", "Deleting", "Succeeded", "Failed", and "Canceled".
+     "Creating", "Deleting", "Succeeded", "Failed", "Canceled", and "SoftDeleted".
     :vartype provisioning_state: str or
      ~azure.mgmt.machinelearningservices.models.ProvisioningState
     :ivar description: The description of the Machine Learning compute.
@@ -6732,8 +7705,8 @@ def __init__(
         description: Optional[str] = None,
         resource_id: Optional[str] = None,
         disable_local_auth: Optional[bool] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword properties: Properties of Databricks.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.DatabricksProperties
@@ -6779,7 +7752,7 @@ class DatabricksComputeSecretsProperties(_serialization.Model):
         "databricks_access_token": {"key": "databricksAccessToken", "type": "str"},
     }
 
-    def __init__(self, *, databricks_access_token: Optional[str] = None, **kwargs):
+    def __init__(self, *, databricks_access_token: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword databricks_access_token: access token for databricks account.
         :paramtype databricks_access_token: str
@@ -6810,7 +7783,7 @@ class DatabricksComputeSecrets(ComputeSecrets, DatabricksComputeSecretsPropertie
         "compute_type": {"key": "computeType", "type": "str"},
     }
 
-    def __init__(self, *, databricks_access_token: Optional[str] = None, **kwargs):
+    def __init__(self, *, databricks_access_token: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword databricks_access_token: access token for databricks account.
         :paramtype databricks_access_token: str
@@ -6834,7 +7807,9 @@ class DatabricksProperties(_serialization.Model):
         "workspace_url": {"key": "workspaceUrl", "type": "str"},
     }
 
-    def __init__(self, *, databricks_access_token: Optional[str] = None, workspace_url: Optional[str] = None, **kwargs):
+    def __init__(
+        self, *, databricks_access_token: Optional[str] = None, workspace_url: Optional[str] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword databricks_access_token: Databricks access token.
         :paramtype databricks_access_token: str
@@ -6884,7 +7859,7 @@ class DataContainer(Resource):
         "properties": {"key": "properties", "type": "DataContainerProperties"},
     }
 
-    def __init__(self, *, properties: "_models.DataContainerProperties", **kwargs):
+    def __init__(self, *, properties: "_models.DataContainerProperties", **kwargs: Any) -> None:
         """
         :keyword properties: [Required] Additional attributes of the entity. Required.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.DataContainerProperties
@@ -6941,8 +7916,8 @@ def __init__(
         properties: Optional[Dict[str, str]] = None,
         tags: Optional[Dict[str, str]] = None,
         is_archived: bool = False,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -6976,8 +7951,8 @@ class DataContainerResourceArmPaginatedResult(_serialization.Model):
     }
 
     def __init__(
-        self, *, next_link: Optional[str] = None, value: Optional[List["_models.DataContainer"]] = None, **kwargs
-    ):
+        self, *, next_link: Optional[str] = None, value: Optional[List["_models.DataContainer"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of DataContainer objects. If null, there are no
          additional pages.
@@ -7005,7 +7980,7 @@ class DataFactory(Compute):
     :vartype compute_location: str
     :ivar provisioning_state: The provision state of the cluster. Valid values are Unknown,
      Updating, Provisioning, Succeeded, and Failed. Known values are: "Unknown", "Updating",
-     "Creating", "Deleting", "Succeeded", "Failed", and "Canceled".
+     "Creating", "Deleting", "Succeeded", "Failed", "Canceled", and "SoftDeleted".
     :vartype provisioning_state: str or
      ~azure.mgmt.machinelearningservices.models.ProvisioningState
     :ivar description: The description of the Machine Learning compute.
@@ -7055,8 +8030,8 @@ def __init__(
         description: Optional[str] = None,
         resource_id: Optional[str] = None,
         disable_local_auth: Optional[bool] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword compute_location: Location for the underlying compute.
         :paramtype compute_location: str
@@ -7090,7 +8065,9 @@ class DataLakeAnalyticsSchema(_serialization.Model):
         "properties": {"key": "properties", "type": "DataLakeAnalyticsSchemaProperties"},
     }
 
-    def __init__(self, *, properties: Optional["_models.DataLakeAnalyticsSchemaProperties"] = None, **kwargs):
+    def __init__(
+        self, *, properties: Optional["_models.DataLakeAnalyticsSchemaProperties"] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword properties:
         :paramtype properties:
@@ -7118,7 +8095,7 @@ class DataLakeAnalytics(Compute, DataLakeAnalyticsSchema):  # pylint: disable=to
     :vartype compute_location: str
     :ivar provisioning_state: The provision state of the cluster. Valid values are Unknown,
      Updating, Provisioning, Succeeded, and Failed. Known values are: "Unknown", "Updating",
-     "Creating", "Deleting", "Succeeded", "Failed", and "Canceled".
+     "Creating", "Deleting", "Succeeded", "Failed", "Canceled", and "SoftDeleted".
     :vartype provisioning_state: str or
      ~azure.mgmt.machinelearningservices.models.ProvisioningState
     :ivar description: The description of the Machine Learning compute.
@@ -7170,8 +8147,8 @@ def __init__(
         description: Optional[str] = None,
         resource_id: Optional[str] = None,
         disable_local_auth: Optional[bool] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword properties:
         :paramtype properties:
@@ -7218,7 +8195,7 @@ class DataLakeAnalyticsSchemaProperties(_serialization.Model):
         "data_lake_store_account_name": {"key": "dataLakeStoreAccountName", "type": "str"},
     }
 
-    def __init__(self, *, data_lake_store_account_name: Optional[str] = None, **kwargs):
+    def __init__(self, *, data_lake_store_account_name: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword data_lake_store_account_name: DataLake Store Account Name.
         :paramtype data_lake_store_account_name: str
@@ -7251,7 +8228,7 @@ class DataPathAssetReference(AssetReferenceBase):
         "path": {"key": "path", "type": "str"},
     }
 
-    def __init__(self, *, datastore_id: Optional[str] = None, path: Optional[str] = None, **kwargs):
+    def __init__(self, *, datastore_id: Optional[str] = None, path: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword datastore_id: ARM resource ID of the datastore where the asset is located.
         :paramtype datastore_id: str
@@ -7264,6 +8241,53 @@ def __init__(self, *, datastore_id: Optional[str] = None, path: Optional[str] =
         self.path = path
 
 
+class DatasetExportSummary(ExportSummary):
+    """DatasetExportSummary.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar end_date_time: The time when the export was completed.
+    :vartype end_date_time: ~datetime.datetime
+    :ivar exported_row_count: The total number of labeled datapoints exported.
+    :vartype exported_row_count: int
+    :ivar format: [Required] The format of exported labels, also as the discriminator. Required.
+     Known values are: "Dataset", "Coco", and "CSV".
+    :vartype format: str or ~azure.mgmt.machinelearningservices.models.ExportFormatType
+    :ivar labeling_job_id: Name and identifier of the job containing exported labels.
+    :vartype labeling_job_id: str
+    :ivar start_date_time: The time when the export was requested.
+    :vartype start_date_time: ~datetime.datetime
+    :ivar labeled_asset_name: The unique name of the labeled data asset.
+    :vartype labeled_asset_name: str
+    """
+
+    _validation = {
+        "end_date_time": {"readonly": True},
+        "exported_row_count": {"readonly": True},
+        "format": {"required": True},
+        "labeling_job_id": {"readonly": True},
+        "start_date_time": {"readonly": True},
+        "labeled_asset_name": {"readonly": True},
+    }
+
+    _attribute_map = {
+        "end_date_time": {"key": "endDateTime", "type": "iso-8601"},
+        "exported_row_count": {"key": "exportedRowCount", "type": "int"},
+        "format": {"key": "format", "type": "str"},
+        "labeling_job_id": {"key": "labelingJobId", "type": "str"},
+        "start_date_time": {"key": "startDateTime", "type": "iso-8601"},
+        "labeled_asset_name": {"key": "labeledAssetName", "type": "str"},
+    }
+
+    def __init__(self, **kwargs: Any) -> None:
+        """ """
+        super().__init__(**kwargs)
+        self.format: str = "Dataset"
+        self.labeled_asset_name = None
+
+
 class Datastore(Resource):
     """Azure Resource Manager resource envelope.
 
@@ -7302,7 +8326,7 @@ class Datastore(Resource):
         "properties": {"key": "properties", "type": "DatastoreProperties"},
     }
 
-    def __init__(self, *, properties: "_models.DatastoreProperties", **kwargs):
+    def __init__(self, *, properties: "_models.DatastoreProperties", **kwargs: Any) -> None:
         """
         :keyword properties: [Required] Additional attributes of the entity. Required.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.DatastoreProperties
@@ -7326,7 +8350,9 @@ class DatastoreResourceArmPaginatedResult(_serialization.Model):
         "value": {"key": "value", "type": "[Datastore]"},
     }
 
-    def __init__(self, *, next_link: Optional[str] = None, value: Optional[List["_models.Datastore"]] = None, **kwargs):
+    def __init__(
+        self, *, next_link: Optional[str] = None, value: Optional[List["_models.Datastore"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of Datastore objects. If null, there are no
          additional pages.
@@ -7377,7 +8403,7 @@ class DataVersionBase(Resource):
         "properties": {"key": "properties", "type": "DataVersionBaseProperties"},
     }
 
-    def __init__(self, *, properties: "_models.DataVersionBaseProperties", **kwargs):
+    def __init__(self, *, properties: "_models.DataVersionBaseProperties", **kwargs: Any) -> None:
         """
         :keyword properties: [Required] Additional attributes of the entity. Required.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.DataVersionBaseProperties
@@ -7407,9 +8433,8 @@ class DataVersionBaseProperties(AssetBase):
     :ivar data_type: [Required] Specifies the type of data. Required. Known values are: "uri_file",
      "uri_folder", and "mltable".
     :vartype data_type: str or ~azure.mgmt.machinelearningservices.models.DataType
-    :ivar data_uri: [Required] Uri of the data. Usage/meaning depends on
-     Microsoft.MachineLearning.ManagementFrontEnd.Contracts.V20221001.Assets.DataVersionBase.DataType.
-     Required.
+    :ivar data_uri: [Required] Uri of the data. Example:
+     https://go.microsoft.com/fwlink/?linkid=2202330. Required.
     :vartype data_uri: str
     """
 
@@ -7441,8 +8466,8 @@ def __init__(
         tags: Optional[Dict[str, str]] = None,
         is_anonymous: bool = False,
         is_archived: bool = False,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -7454,9 +8479,8 @@ def __init__(
         :paramtype is_anonymous: bool
         :keyword is_archived: Is the asset archived?.
         :paramtype is_archived: bool
-        :keyword data_uri: [Required] Uri of the data. Usage/meaning depends on
-         Microsoft.MachineLearning.ManagementFrontEnd.Contracts.V20221001.Assets.DataVersionBase.DataType.
-         Required.
+        :keyword data_uri: [Required] Uri of the data. Example:
+         https://go.microsoft.com/fwlink/?linkid=2202330. Required.
         :paramtype data_uri: str
         """
         super().__init__(
@@ -7487,8 +8511,8 @@ class DataVersionBaseResourceArmPaginatedResult(_serialization.Model):
     }
 
     def __init__(
-        self, *, next_link: Optional[str] = None, value: Optional[List["_models.DataVersionBase"]] = None, **kwargs
-    ):
+        self, *, next_link: Optional[str] = None, value: Optional[List["_models.DataVersionBase"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of DataVersionBase objects. If null, there are no
          additional pages.
@@ -7526,7 +8550,7 @@ class OnlineScaleSettings(_serialization.Model):
         "scale_type": {"Default": "DefaultScaleSettings", "TargetUtilization": "TargetUtilizationScaleSettings"}
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.scale_type: Optional[str] = None
@@ -7550,7 +8574,7 @@ class DefaultScaleSettings(OnlineScaleSettings):
         "scale_type": {"key": "scaleType", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.scale_type: str = "Default"
@@ -7567,7 +8591,7 @@ class DeploymentLogs(_serialization.Model):
         "content": {"key": "content", "type": "str"},
     }
 
-    def __init__(self, *, content: Optional[str] = None, **kwargs):
+    def __init__(self, *, content: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword content: The retrieved online deployment logs.
         :paramtype content: str
@@ -7580,7 +8604,7 @@ class DeploymentLogsRequest(_serialization.Model):
     """DeploymentLogsRequest.
 
     :ivar container_type: The type of container to retrieve logs from. Known values are:
-     "StorageInitializer" and "InferenceServer".
+     "StorageInitializer", "InferenceServer", and "ModelDataCollector".
     :vartype container_type: str or ~azure.mgmt.machinelearningservices.models.ContainerType
     :ivar tail: The maximum number of lines to tail.
     :vartype tail: int
@@ -7596,11 +8620,11 @@ def __init__(
         *,
         container_type: Optional[Union[str, "_models.ContainerType"]] = None,
         tail: Optional[int] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword container_type: The type of container to retrieve logs from. Known values are:
-         "StorageInitializer" and "InferenceServer".
+         "StorageInitializer", "InferenceServer", and "ModelDataCollector".
         :paramtype container_type: str or ~azure.mgmt.machinelearningservices.models.ContainerType
         :keyword tail: The maximum number of lines to tail.
         :paramtype tail: int
@@ -7633,8 +8657,8 @@ def __init__(
         instance_count: int = 1,
         instance_type: Optional[str] = None,
         properties: Optional[Dict[str, JSON]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword instance_count: Optional number of instances or nodes used by the compute target.
         :paramtype instance_count: int
@@ -7672,8 +8696,8 @@ def __init__(
         instance_count: int = 1,
         instance_type: Optional[str] = None,
         properties: Optional[Dict[str, JSON]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword instance_count: Optional number of instances or nodes used by the compute target.
         :paramtype instance_count: int
@@ -7732,8 +8756,8 @@ def __init__(
         container_registry: Optional[Dict[str, JSON]] = None,
         application_insights: Optional[Dict[str, JSON]] = None,
         others: Optional[Dict[str, JSON]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword udr: Setting for diagnosing user defined routing.
         :paramtype udr: dict[str, JSON]
@@ -7777,7 +8801,7 @@ class DiagnoseResponseResult(_serialization.Model):
         "value": {"key": "value", "type": "DiagnoseResponseResultValue"},
     }
 
-    def __init__(self, *, value: Optional["_models.DiagnoseResponseResultValue"] = None, **kwargs):
+    def __init__(self, *, value: Optional["_models.DiagnoseResponseResultValue"] = None, **kwargs: Any) -> None:
         """
         :keyword value:
         :paramtype value: ~azure.mgmt.machinelearningservices.models.DiagnoseResponseResultValue
@@ -7839,8 +8863,8 @@ def __init__(
         container_registry_results: Optional[List["_models.DiagnoseResult"]] = None,
         application_insights_results: Optional[List["_models.DiagnoseResult"]] = None,
         other_results: Optional[List["_models.DiagnoseResult"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword user_defined_route_results:
         :paramtype user_defined_route_results:
@@ -7906,7 +8930,7 @@ class DiagnoseResult(_serialization.Model):
         "message": {"key": "message", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.code = None
@@ -7925,7 +8949,7 @@ class DiagnoseWorkspaceParameters(_serialization.Model):
         "value": {"key": "value", "type": "DiagnoseRequestProperties"},
     }
 
-    def __init__(self, *, value: Optional["_models.DiagnoseRequestProperties"] = None, **kwargs):
+    def __init__(self, *, value: Optional["_models.DiagnoseRequestProperties"] = None, **kwargs: Any) -> None:
         """
         :keyword value: Value of Parameters.
         :paramtype value: ~azure.mgmt.machinelearningservices.models.DiagnoseRequestProperties
@@ -7957,12 +8981,46 @@ class DistributionConfiguration(_serialization.Model):
 
     _subtype_map = {"distribution_type": {"Mpi": "Mpi", "PyTorch": "PyTorch", "TensorFlow": "TensorFlow"}}
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.distribution_type: Optional[str] = None
 
 
+class Docker(_serialization.Model):
+    """Docker.
+
+    :ivar additional_properties: Unmatched properties from the message are deserialized to this
+     collection.
+    :vartype additional_properties: dict[str, any]
+    :ivar privileged: Indicate whether container shall run in privileged or non-privileged mode.
+    :vartype privileged: bool
+    """
+
+    _attribute_map = {
+        "additional_properties": {"key": "", "type": "{object}"},
+        "privileged": {"key": "privileged", "type": "bool"},
+    }
+
+    def __init__(
+        self,
+        *,
+        additional_properties: Optional[Dict[str, Any]] = None,
+        privileged: Optional[bool] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword additional_properties: Unmatched properties from the message are deserialized to this
+         collection.
+        :paramtype additional_properties: dict[str, any]
+        :keyword privileged: Indicate whether container shall run in privileged or non-privileged mode.
+        :paramtype privileged: bool
+        """
+        super().__init__(**kwargs)
+        self.additional_properties = additional_properties
+        self.privileged = privileged
+
+
 class EncryptionKeyVaultProperties(_serialization.Model):
     """EncryptionKeyVaultProperties.
 
@@ -7990,8 +9048,8 @@ class EncryptionKeyVaultProperties(_serialization.Model):
     }
 
     def __init__(
-        self, *, key_vault_arm_id: str, key_identifier: str, identity_client_id: Optional[str] = None, **kwargs
-    ):
+        self, *, key_vault_arm_id: str, key_identifier: str, identity_client_id: Optional[str] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword key_vault_arm_id: The ArmId of the keyVault where the customer owned encryption key is
          present. Required.
@@ -8008,6 +9066,32 @@ def __init__(
         self.identity_client_id = identity_client_id
 
 
+class EncryptionKeyVaultUpdateProperties(_serialization.Model):
+    """EncryptionKeyVaultUpdateProperties.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar key_identifier: Key Vault uri to access the encryption key. Required.
+    :vartype key_identifier: str
+    """
+
+    _validation = {
+        "key_identifier": {"required": True},
+    }
+
+    _attribute_map = {
+        "key_identifier": {"key": "keyIdentifier", "type": "str"},
+    }
+
+    def __init__(self, *, key_identifier: str, **kwargs: Any) -> None:
+        """
+        :keyword key_identifier: Key Vault uri to access the encryption key. Required.
+        :paramtype key_identifier: str
+        """
+        super().__init__(**kwargs)
+        self.key_identifier = key_identifier
+
+
 class EncryptionProperty(_serialization.Model):
     """EncryptionProperty.
 
@@ -8040,8 +9124,8 @@ def __init__(
         status: Union[str, "_models.EncryptionStatus"],
         key_vault_properties: "_models.EncryptionKeyVaultProperties",
         identity: Optional["_models.IdentityForCmk"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword status: Indicates whether or not the encryption is enabled for the workspace.
          Required. Known values are: "Enabled" and "Disabled".
@@ -8059,6 +9143,89 @@ def __init__(
         self.key_vault_properties = key_vault_properties
 
 
+class EncryptionUpdateProperties(_serialization.Model):
+    """EncryptionUpdateProperties.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar key_vault_properties: Customer Key vault properties. Required.
+    :vartype key_vault_properties:
+     ~azure.mgmt.machinelearningservices.models.EncryptionKeyVaultUpdateProperties
+    """
+
+    _validation = {
+        "key_vault_properties": {"required": True},
+    }
+
+    _attribute_map = {
+        "key_vault_properties": {"key": "keyVaultProperties", "type": "EncryptionKeyVaultUpdateProperties"},
+    }
+
+    def __init__(self, *, key_vault_properties: "_models.EncryptionKeyVaultUpdateProperties", **kwargs: Any) -> None:
+        """
+        :keyword key_vault_properties: Customer Key vault properties. Required.
+        :paramtype key_vault_properties:
+         ~azure.mgmt.machinelearningservices.models.EncryptionKeyVaultUpdateProperties
+        """
+        super().__init__(**kwargs)
+        self.key_vault_properties = key_vault_properties
+
+
+class Endpoint(_serialization.Model):
+    """Endpoint.
+
+    :ivar protocol: Protocol over which communication will happen over this endpoint. Known values
+     are: "tcp", "udp", and "http".
+    :vartype protocol: str or ~azure.mgmt.machinelearningservices.models.Protocol
+    :ivar name: Name of the Endpoint.
+    :vartype name: str
+    :ivar target: Application port inside the container.
+    :vartype target: int
+    :ivar published: Port over which the application is exposed from container.
+    :vartype published: int
+    :ivar host_ip: Host IP over which the application is exposed from the container.
+    :vartype host_ip: str
+    """
+
+    _attribute_map = {
+        "protocol": {"key": "protocol", "type": "str"},
+        "name": {"key": "name", "type": "str"},
+        "target": {"key": "target", "type": "int"},
+        "published": {"key": "published", "type": "int"},
+        "host_ip": {"key": "hostIp", "type": "str"},
+    }
+
+    def __init__(
+        self,
+        *,
+        protocol: Union[str, "_models.Protocol"] = "tcp",
+        name: Optional[str] = None,
+        target: Optional[int] = None,
+        published: Optional[int] = None,
+        host_ip: Optional[str] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword protocol: Protocol over which communication will happen over this endpoint. Known
+         values are: "tcp", "udp", and "http".
+        :paramtype protocol: str or ~azure.mgmt.machinelearningservices.models.Protocol
+        :keyword name: Name of the Endpoint.
+        :paramtype name: str
+        :keyword target: Application port inside the container.
+        :paramtype target: int
+        :keyword published: Port over which the application is exposed from container.
+        :paramtype published: int
+        :keyword host_ip: Host IP over which the application is exposed from the container.
+        :paramtype host_ip: str
+        """
+        super().__init__(**kwargs)
+        self.protocol = protocol
+        self.name = name
+        self.target = target
+        self.published = published
+        self.host_ip = host_ip
+
+
 class EndpointAuthKeys(_serialization.Model):
     """Keys for endpoint authentication.
 
@@ -8073,7 +9240,9 @@ class EndpointAuthKeys(_serialization.Model):
         "secondary_key": {"key": "secondaryKey", "type": "str"},
     }
 
-    def __init__(self, *, primary_key: Optional[str] = None, secondary_key: Optional[str] = None, **kwargs):
+    def __init__(
+        self, *, primary_key: Optional[str] = None, secondary_key: Optional[str] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword primary_key: The primary key.
         :paramtype primary_key: str
@@ -8112,8 +9281,8 @@ def __init__(
         expiry_time_utc: int = 0,
         refresh_after_time_utc: int = 0,
         token_type: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword access_token: Access token for endpoint authentication.
         :paramtype access_token: str
@@ -8154,7 +9323,7 @@ class ScheduleActionBase(_serialization.Model):
 
     _subtype_map = {"action_type": {"CreateJob": "JobScheduleAction", "InvokeBatchEndpoint": "EndpointScheduleAction"}}
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.action_type: Optional[str] = None
@@ -8187,7 +9356,7 @@ class EndpointScheduleAction(ScheduleActionBase):
         "endpoint_invocation_definition": {"key": "endpointInvocationDefinition", "type": "object"},
     }
 
-    def __init__(self, *, endpoint_invocation_definition: JSON, **kwargs):
+    def __init__(self, *, endpoint_invocation_definition: JSON, **kwargs: Any) -> None:
         """
         :keyword endpoint_invocation_definition: [Required] Defines Schedule action definition details.
 
@@ -8240,7 +9409,7 @@ class EnvironmentContainer(Resource):
         "properties": {"key": "properties", "type": "EnvironmentContainerProperties"},
     }
 
-    def __init__(self, *, properties: "_models.EnvironmentContainerProperties", **kwargs):
+    def __init__(self, *, properties: "_models.EnvironmentContainerProperties", **kwargs: Any) -> None:
         """
         :keyword properties: [Required] Additional attributes of the entity. Required.
         :paramtype properties:
@@ -8267,11 +9436,16 @@ class EnvironmentContainerProperties(AssetContainer):
     :vartype latest_version: str
     :ivar next_version: The next auto incremental version.
     :vartype next_version: str
+    :ivar provisioning_state: Provisioning state for the environment container. Known values are:
+     "Succeeded", "Failed", "Canceled", "Creating", "Updating", and "Deleting".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.AssetProvisioningState
     """
 
     _validation = {
         "latest_version": {"readonly": True},
         "next_version": {"readonly": True},
+        "provisioning_state": {"readonly": True},
     }
 
     _attribute_map = {
@@ -8281,6 +9455,7 @@ class EnvironmentContainerProperties(AssetContainer):
         "is_archived": {"key": "isArchived", "type": "bool"},
         "latest_version": {"key": "latestVersion", "type": "str"},
         "next_version": {"key": "nextVersion", "type": "str"},
+        "provisioning_state": {"key": "provisioningState", "type": "str"},
     }
 
     def __init__(
@@ -8290,8 +9465,8 @@ def __init__(
         properties: Optional[Dict[str, str]] = None,
         tags: Optional[Dict[str, str]] = None,
         is_archived: bool = False,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -8303,6 +9478,7 @@ def __init__(
         :paramtype is_archived: bool
         """
         super().__init__(description=description, properties=properties, tags=tags, is_archived=is_archived, **kwargs)
+        self.provisioning_state = None
 
 
 class EnvironmentContainerResourceArmPaginatedResult(_serialization.Model):
@@ -8321,8 +9497,12 @@ class EnvironmentContainerResourceArmPaginatedResult(_serialization.Model):
     }
 
     def __init__(
-        self, *, next_link: Optional[str] = None, value: Optional[List["_models.EnvironmentContainer"]] = None, **kwargs
-    ):
+        self,
+        *,
+        next_link: Optional[str] = None,
+        value: Optional[List["_models.EnvironmentContainer"]] = None,
+        **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of EnvironmentContainer objects. If null, there
          are no additional pages.
@@ -8335,6 +9515,49 @@ def __init__(
         self.value = value
 
 
+class EnvironmentVariable(_serialization.Model):
+    """EnvironmentVariable.
+
+    :ivar additional_properties: Unmatched properties from the message are deserialized to this
+     collection.
+    :vartype additional_properties: dict[str, any]
+    :ivar type: Type of the Environment Variable. Possible values are: local - For local variable.
+     "local"
+    :vartype type: str or ~azure.mgmt.machinelearningservices.models.EnvironmentVariableType
+    :ivar value: Value of the Environment variable.
+    :vartype value: str
+    """
+
+    _attribute_map = {
+        "additional_properties": {"key": "", "type": "{object}"},
+        "type": {"key": "type", "type": "str"},
+        "value": {"key": "value", "type": "str"},
+    }
+
+    def __init__(
+        self,
+        *,
+        additional_properties: Optional[Dict[str, Any]] = None,
+        type: Union[str, "_models.EnvironmentVariableType"] = "local",
+        value: Optional[str] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword additional_properties: Unmatched properties from the message are deserialized to this
+         collection.
+        :paramtype additional_properties: dict[str, any]
+        :keyword type: Type of the Environment Variable. Possible values are: local - For local
+         variable. "local"
+        :paramtype type: str or ~azure.mgmt.machinelearningservices.models.EnvironmentVariableType
+        :keyword value: Value of the Environment variable.
+        :paramtype value: str
+        """
+        super().__init__(**kwargs)
+        self.additional_properties = additional_properties
+        self.type = type
+        self.value = value
+
+
 class EnvironmentVersion(Resource):
     """Azure Resource Manager resource envelope.
 
@@ -8373,7 +9596,7 @@ class EnvironmentVersion(Resource):
         "properties": {"key": "properties", "type": "EnvironmentVersionProperties"},
     }
 
-    def __init__(self, *, properties: "_models.EnvironmentVersionProperties", **kwargs):
+    def __init__(self, *, properties: "_models.EnvironmentVersionProperties", **kwargs: Any) -> None:
         """
         :keyword properties: [Required] Additional attributes of the entity. Required.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.EnvironmentVersionProperties
@@ -8436,10 +9659,15 @@ class EnvironmentVersionProperties(AssetBase):  # pylint: disable=too-many-insta
      ~azure.mgmt.machinelearningservices.models.InferenceContainerProperties
     :ivar os_type: The OS type of the environment. Known values are: "Linux" and "Windows".
     :vartype os_type: str or ~azure.mgmt.machinelearningservices.models.OperatingSystemType
+    :ivar provisioning_state: Provisioning state for the environment version. Known values are:
+     "Succeeded", "Failed", "Canceled", "Creating", "Updating", and "Deleting".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.AssetProvisioningState
     """
 
     _validation = {
         "environment_type": {"readonly": True},
+        "provisioning_state": {"readonly": True},
     }
 
     _attribute_map = {
@@ -8455,6 +9683,7 @@ class EnvironmentVersionProperties(AssetBase):  # pylint: disable=too-many-insta
         "image": {"key": "image", "type": "str"},
         "inference_config": {"key": "inferenceConfig", "type": "InferenceContainerProperties"},
         "os_type": {"key": "osType", "type": "str"},
+        "provisioning_state": {"key": "provisioningState", "type": "str"},
     }
 
     def __init__(
@@ -8471,8 +9700,8 @@ def __init__(
         image: Optional[str] = None,
         inference_config: Optional["_models.InferenceContainerProperties"] = None,
         os_type: Optional[Union[str, "_models.OperatingSystemType"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -8529,6 +9758,7 @@ def __init__(
         self.image = image
         self.inference_config = inference_config
         self.os_type = os_type
+        self.provisioning_state = None
 
 
 class EnvironmentVersionResourceArmPaginatedResult(_serialization.Model):
@@ -8547,8 +9777,12 @@ class EnvironmentVersionResourceArmPaginatedResult(_serialization.Model):
     }
 
     def __init__(
-        self, *, next_link: Optional[str] = None, value: Optional[List["_models.EnvironmentVersion"]] = None, **kwargs
-    ):
+        self,
+        *,
+        next_link: Optional[str] = None,
+        value: Optional[List["_models.EnvironmentVersion"]] = None,
+        **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of EnvironmentVersion objects. If null, there are
          no additional pages.
@@ -8582,7 +9816,7 @@ class ErrorAdditionalInfo(_serialization.Model):
         "info": {"key": "info", "type": "object"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.type = None
@@ -8622,7 +9856,7 @@ class ErrorDetail(_serialization.Model):
         "additional_info": {"key": "additionalInfo", "type": "[ErrorAdditionalInfo]"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.code = None
@@ -8633,7 +9867,8 @@ def __init__(self, **kwargs):
 
 
 class ErrorResponse(_serialization.Model):
-    """Common error response for all Azure Resource Manager APIs to return error details for failed operations. (This also follows the OData error response format.).
+    """Common error response for all Azure Resource Manager APIs to return error details for failed
+    operations. (This also follows the OData error response format.).
 
     :ivar error: The error object.
     :vartype error: ~azure.mgmt.machinelearningservices.models.ErrorDetail
@@ -8643,7 +9878,7 @@ class ErrorResponse(_serialization.Model):
         "error": {"key": "error", "type": "ErrorDetail"},
     }
 
-    def __init__(self, *, error: Optional["_models.ErrorDetail"] = None, **kwargs):
+    def __init__(self, *, error: Optional["_models.ErrorDetail"] = None, **kwargs: Any) -> None:
         """
         :keyword error: The error object.
         :paramtype error: ~azure.mgmt.machinelearningservices.models.ErrorDetail
@@ -8685,8 +9920,8 @@ def __init__(
         retail_price: float,
         os_type: Union[str, "_models.VMPriceOSType"],
         vm_tier: Union[str, "_models.VMTier"],
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword retail_price: The price charged for using the VM. Required.
         :paramtype retail_price: float
@@ -8737,8 +9972,8 @@ def __init__(
         billing_currency: Union[str, "_models.BillingCurrency"],
         unit_of_measure: Union[str, "_models.UnitOfMeasure"],
         values: List["_models.EstimatedVMPrice"],
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword billing_currency: Three lettered code specifying the currency of the VM price.
          Example: USD. Required. "USD"
@@ -8767,7 +10002,7 @@ class ExternalFQDNResponse(_serialization.Model):
         "value": {"key": "value", "type": "[FQDNEndpoints]"},
     }
 
-    def __init__(self, *, value: Optional[List["_models.FQDNEndpoints"]] = None, **kwargs):
+    def __init__(self, *, value: Optional[List["_models.FQDNEndpoints"]] = None, **kwargs: Any) -> None:
         """
         :keyword value:
         :paramtype value: list[~azure.mgmt.machinelearningservices.models.FQDNEndpoints]
@@ -8787,7 +10022,7 @@ class FeaturizationSettings(_serialization.Model):
         "dataset_language": {"key": "datasetLanguage", "type": "str"},
     }
 
-    def __init__(self, *, dataset_language: Optional[str] = None, **kwargs):
+    def __init__(self, *, dataset_language: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword dataset_language: Dataset language, useful for the text data.
         :paramtype dataset_language: str
@@ -8807,7 +10042,7 @@ class FlavorData(_serialization.Model):
         "data": {"key": "data", "type": "{str}"},
     }
 
-    def __init__(self, *, data: Optional[Dict[str, str]] = None, **kwargs):
+    def __init__(self, *, data: Optional[Dict[str, str]] = None, **kwargs: Any) -> None:
         """
         :keyword data: Model flavor-specific data.
         :paramtype data: dict[str, str]
@@ -8839,11 +10074,19 @@ class Forecasting(TableVertical, AutoMLVertical):  # pylint: disable=too-many-in
     :ivar featurization_settings: Featurization inputs needed for AutoML job.
     :vartype featurization_settings:
      ~azure.mgmt.machinelearningservices.models.TableVerticalFeaturizationSettings
+    :ivar fixed_parameters: Model/training parameters that will remain constant throughout
+     training.
+    :vartype fixed_parameters: ~azure.mgmt.machinelearningservices.models.TableFixedParameters
     :ivar limit_settings: Execution constraints for AutoMLJob.
     :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.TableVerticalLimitSettings
     :ivar n_cross_validations: Number of cross validation folds to be applied on training dataset
      when validation dataset is not provided.
     :vartype n_cross_validations: ~azure.mgmt.machinelearningservices.models.NCrossValidations
+    :ivar search_space: Search space for sampling different combinations of models and their
+     hyperparameters.
+    :vartype search_space: list[~azure.mgmt.machinelearningservices.models.TableParameterSubspace]
+    :ivar sweep_settings: Settings for model sweeping and hyperparameter tuning.
+    :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.TableSweepSettings
     :ivar test_data: Test data input.
     :vartype test_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
     :ivar test_data_size: The fraction of test dataset that needs to be set aside for validation
@@ -8885,8 +10128,11 @@ class Forecasting(TableVertical, AutoMLVertical):  # pylint: disable=too-many-in
         "training_data": {"key": "trainingData", "type": "MLTableJobInput"},
         "cv_split_column_names": {"key": "cvSplitColumnNames", "type": "[str]"},
         "featurization_settings": {"key": "featurizationSettings", "type": "TableVerticalFeaturizationSettings"},
+        "fixed_parameters": {"key": "fixedParameters", "type": "TableFixedParameters"},
         "limit_settings": {"key": "limitSettings", "type": "TableVerticalLimitSettings"},
         "n_cross_validations": {"key": "nCrossValidations", "type": "NCrossValidations"},
+        "search_space": {"key": "searchSpace", "type": "[TableParameterSubspace]"},
+        "sweep_settings": {"key": "sweepSettings", "type": "TableSweepSettings"},
         "test_data": {"key": "testData", "type": "MLTableJobInput"},
         "test_data_size": {"key": "testDataSize", "type": "float"},
         "validation_data": {"key": "validationData", "type": "MLTableJobInput"},
@@ -8905,8 +10151,11 @@ def __init__(
         target_column_name: Optional[str] = None,
         cv_split_column_names: Optional[List[str]] = None,
         featurization_settings: Optional["_models.TableVerticalFeaturizationSettings"] = None,
+        fixed_parameters: Optional["_models.TableFixedParameters"] = None,
         limit_settings: Optional["_models.TableVerticalLimitSettings"] = None,
         n_cross_validations: Optional["_models.NCrossValidations"] = None,
+        search_space: Optional[List["_models.TableParameterSubspace"]] = None,
+        sweep_settings: Optional["_models.TableSweepSettings"] = None,
         test_data: Optional["_models.MLTableJobInput"] = None,
         test_data_size: Optional[float] = None,
         validation_data: Optional["_models.MLTableJobInput"] = None,
@@ -8915,8 +10164,8 @@ def __init__(
         forecasting_settings: Optional["_models.ForecastingSettings"] = None,
         primary_metric: Optional[Union[str, "_models.ForecastingPrimaryMetrics"]] = None,
         training_settings: Optional["_models.ForecastingTrainingSettings"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
          "Warning", "Error", and "Critical".
@@ -8931,6 +10180,9 @@ def __init__(
         :keyword featurization_settings: Featurization inputs needed for AutoML job.
         :paramtype featurization_settings:
          ~azure.mgmt.machinelearningservices.models.TableVerticalFeaturizationSettings
+        :keyword fixed_parameters: Model/training parameters that will remain constant throughout
+         training.
+        :paramtype fixed_parameters: ~azure.mgmt.machinelearningservices.models.TableFixedParameters
         :keyword limit_settings: Execution constraints for AutoMLJob.
         :paramtype limit_settings:
          ~azure.mgmt.machinelearningservices.models.TableVerticalLimitSettings
@@ -8938,6 +10190,12 @@ def __init__(
          dataset
          when validation dataset is not provided.
         :paramtype n_cross_validations: ~azure.mgmt.machinelearningservices.models.NCrossValidations
+        :keyword search_space: Search space for sampling different combinations of models and their
+         hyperparameters.
+        :paramtype search_space:
+         list[~azure.mgmt.machinelearningservices.models.TableParameterSubspace]
+        :keyword sweep_settings: Settings for model sweeping and hyperparameter tuning.
+        :paramtype sweep_settings: ~azure.mgmt.machinelearningservices.models.TableSweepSettings
         :keyword test_data: Test data input.
         :paramtype test_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
         :keyword test_data_size: The fraction of test dataset that needs to be set aside for validation
@@ -8969,8 +10227,11 @@ def __init__(
         super().__init__(
             cv_split_column_names=cv_split_column_names,
             featurization_settings=featurization_settings,
+            fixed_parameters=fixed_parameters,
             limit_settings=limit_settings,
             n_cross_validations=n_cross_validations,
+            search_space=search_space,
+            sweep_settings=sweep_settings,
             test_data=test_data,
             test_data_size=test_data_size,
             validation_data=validation_data,
@@ -8990,8 +10251,11 @@ def __init__(
         self.training_settings = training_settings
         self.cv_split_column_names = cv_split_column_names
         self.featurization_settings = featurization_settings
+        self.fixed_parameters = fixed_parameters
         self.limit_settings = limit_settings
         self.n_cross_validations = n_cross_validations
+        self.search_space = search_space
+        self.sweep_settings = sweep_settings
         self.test_data = test_data
         self.test_data_size = test_data_size
         self.validation_data = validation_data
@@ -9085,8 +10349,8 @@ def __init__(
         time_column_name: Optional[str] = None,
         time_series_id_column_names: Optional[List[str]] = None,
         use_stl: Optional[Union[str, "_models.UseStl"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword country_or_region_for_holidays: Country or region for holidays for forecasting tasks.
          These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'.
@@ -9176,6 +10440,14 @@ class ForecastingTrainingSettings(TrainingSettings):
     :ivar stack_ensemble_settings: Stack ensemble settings for stack ensemble run.
     :vartype stack_ensemble_settings:
      ~azure.mgmt.machinelearningservices.models.StackEnsembleSettings
+    :ivar training_mode: TrainingMode mode - Setting to 'auto' is same as setting it to
+     'non-distributed' for now, however in the future may result in mixed mode or heuristics based
+     mode selection. Default is 'auto'.
+     If 'Distributed' then only distributed featurization is used and distributed algorithms are
+     chosen.
+     If 'NonDistributed' then only non distributed algorithms are chosen. Known values are: "Auto",
+     "Distributed", and "NonDistributed".
+    :vartype training_mode: str or ~azure.mgmt.machinelearningservices.models.TrainingMode
     :ivar allowed_training_algorithms: Allowed models for forecasting task.
     :vartype allowed_training_algorithms: list[str or
      ~azure.mgmt.machinelearningservices.models.ForecastingModels]
@@ -9192,6 +10464,7 @@ class ForecastingTrainingSettings(TrainingSettings):
         "enable_vote_ensemble": {"key": "enableVoteEnsemble", "type": "bool"},
         "ensemble_model_download_timeout": {"key": "ensembleModelDownloadTimeout", "type": "duration"},
         "stack_ensemble_settings": {"key": "stackEnsembleSettings", "type": "StackEnsembleSettings"},
+        "training_mode": {"key": "trainingMode", "type": "str"},
         "allowed_training_algorithms": {"key": "allowedTrainingAlgorithms", "type": "[str]"},
         "blocked_training_algorithms": {"key": "blockedTrainingAlgorithms", "type": "[str]"},
     }
@@ -9206,10 +10479,11 @@ def __init__(
         enable_vote_ensemble: bool = True,
         ensemble_model_download_timeout: datetime.timedelta = "PT5M",
         stack_ensemble_settings: Optional["_models.StackEnsembleSettings"] = None,
+        training_mode: Optional[Union[str, "_models.TrainingMode"]] = None,
         allowed_training_algorithms: Optional[List[Union[str, "_models.ForecastingModels"]]] = None,
         blocked_training_algorithms: Optional[List[Union[str, "_models.ForecastingModels"]]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword enable_dnn_training: Enable recommendation of DNN models.
         :paramtype enable_dnn_training: bool
@@ -9228,6 +10502,14 @@ def __init__(
         :keyword stack_ensemble_settings: Stack ensemble settings for stack ensemble run.
         :paramtype stack_ensemble_settings:
          ~azure.mgmt.machinelearningservices.models.StackEnsembleSettings
+        :keyword training_mode: TrainingMode mode - Setting to 'auto' is same as setting it to
+         'non-distributed' for now, however in the future may result in mixed mode or heuristics based
+         mode selection. Default is 'auto'.
+         If 'Distributed' then only distributed featurization is used and distributed algorithms are
+         chosen.
+         If 'NonDistributed' then only non distributed algorithms are chosen. Known values are: "Auto",
+         "Distributed", and "NonDistributed".
+        :paramtype training_mode: str or ~azure.mgmt.machinelearningservices.models.TrainingMode
         :keyword allowed_training_algorithms: Allowed models for forecasting task.
         :paramtype allowed_training_algorithms: list[str or
          ~azure.mgmt.machinelearningservices.models.ForecastingModels]
@@ -9243,6 +10525,7 @@ def __init__(
             enable_vote_ensemble=enable_vote_ensemble,
             ensemble_model_download_timeout=ensemble_model_download_timeout,
             stack_ensemble_settings=stack_ensemble_settings,
+            training_mode=training_mode,
             **kwargs
         )
         self.allowed_training_algorithms = allowed_training_algorithms
@@ -9268,8 +10551,8 @@ def __init__(
         *,
         domain_name: Optional[str] = None,
         endpoint_details: Optional[List["_models.FQDNEndpointDetail"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword domain_name:
         :paramtype domain_name: str
@@ -9293,7 +10576,7 @@ class FQDNEndpointDetail(_serialization.Model):
         "port": {"key": "port", "type": "int"},
     }
 
-    def __init__(self, *, port: Optional[int] = None, **kwargs):
+    def __init__(self, *, port: Optional[int] = None, **kwargs: Any) -> None:
         """
         :keyword port:
         :paramtype port: int
@@ -9313,7 +10596,7 @@ class FQDNEndpoints(_serialization.Model):
         "properties": {"key": "properties", "type": "FQDNEndpointsProperties"},
     }
 
-    def __init__(self, *, properties: Optional["_models.FQDNEndpointsProperties"] = None, **kwargs):
+    def __init__(self, *, properties: Optional["_models.FQDNEndpointsProperties"] = None, **kwargs: Any) -> None:
         """
         :keyword properties:
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.FQDNEndpointsProperties
@@ -9337,8 +10620,8 @@ class FQDNEndpointsProperties(_serialization.Model):
     }
 
     def __init__(
-        self, *, category: Optional[str] = None, endpoints: Optional[List["_models.FQDNEndpoint"]] = None, **kwargs
-    ):
+        self, *, category: Optional[str] = None, endpoints: Optional[List["_models.FQDNEndpoint"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword category:
         :paramtype category: str
@@ -9370,35 +10653,120 @@ class GridSamplingAlgorithm(SamplingAlgorithm):
         "sampling_algorithm_type": {"key": "samplingAlgorithmType", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.sampling_algorithm_type: str = "Grid"
 
 
-class HDInsightSchema(_serialization.Model):
-    """HDInsightSchema.
+class HdfsDatastore(DatastoreProperties):
+    """HdfsDatastore.
 
-    :ivar properties: HDInsight compute properties.
-    :vartype properties: ~azure.mgmt.machinelearningservices.models.HDInsightProperties
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar credentials: [Required] Account credentials. Required.
+    :vartype credentials: ~azure.mgmt.machinelearningservices.models.DatastoreCredentials
+    :ivar datastore_type: [Required] Storage type backing the datastore. Required. Known values
+     are: "AzureBlob", "AzureDataLakeGen1", "AzureDataLakeGen2", "AzureFile", and "Hdfs".
+    :vartype datastore_type: str or ~azure.mgmt.machinelearningservices.models.DatastoreType
+    :ivar is_default: Readonly property to indicate if datastore is the workspace default
+     datastore.
+    :vartype is_default: bool
+    :ivar hdfs_server_certificate: The TLS cert of the HDFS server. Needs to be a base64 encoded
+     string. Required if "Https" protocol is selected.
+    :vartype hdfs_server_certificate: str
+    :ivar name_node_address: [Required] IP Address or DNS HostName. Required.
+    :vartype name_node_address: str
+    :ivar protocol: Protocol used to communicate with the storage account (Https/Http).
+    :vartype protocol: str
     """
 
+    _validation = {
+        "credentials": {"required": True},
+        "datastore_type": {"required": True},
+        "is_default": {"readonly": True},
+        "name_node_address": {"required": True, "pattern": r"[a-zA-Z0-9_]"},
+    }
+
     _attribute_map = {
-        "properties": {"key": "properties", "type": "HDInsightProperties"},
+        "description": {"key": "description", "type": "str"},
+        "properties": {"key": "properties", "type": "{str}"},
+        "tags": {"key": "tags", "type": "{str}"},
+        "credentials": {"key": "credentials", "type": "DatastoreCredentials"},
+        "datastore_type": {"key": "datastoreType", "type": "str"},
+        "is_default": {"key": "isDefault", "type": "bool"},
+        "hdfs_server_certificate": {"key": "hdfsServerCertificate", "type": "str"},
+        "name_node_address": {"key": "nameNodeAddress", "type": "str"},
+        "protocol": {"key": "protocol", "type": "str"},
     }
 
-    def __init__(self, *, properties: Optional["_models.HDInsightProperties"] = None, **kwargs):
-        """
-        :keyword properties: HDInsight compute properties.
-        :paramtype properties: ~azure.mgmt.machinelearningservices.models.HDInsightProperties
+    def __init__(
+        self,
+        *,
+        credentials: "_models.DatastoreCredentials",
+        name_node_address: str,
+        description: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        tags: Optional[Dict[str, str]] = None,
+        hdfs_server_certificate: Optional[str] = None,
+        protocol: str = "http",
+        **kwargs: Any
+    ) -> None:
         """
-        super().__init__(**kwargs)
-        self.properties = properties
-
-
-class HDInsight(Compute, HDInsightSchema):  # pylint: disable=too-many-instance-attributes
-    """A HDInsight compute.
-
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword credentials: [Required] Account credentials. Required.
+        :paramtype credentials: ~azure.mgmt.machinelearningservices.models.DatastoreCredentials
+        :keyword hdfs_server_certificate: The TLS cert of the HDFS server. Needs to be a base64 encoded
+         string. Required if "Https" protocol is selected.
+        :paramtype hdfs_server_certificate: str
+        :keyword name_node_address: [Required] IP Address or DNS HostName. Required.
+        :paramtype name_node_address: str
+        :keyword protocol: Protocol used to communicate with the storage account (Https/Http).
+        :paramtype protocol: str
+        """
+        super().__init__(description=description, properties=properties, tags=tags, credentials=credentials, **kwargs)
+        self.datastore_type: str = "Hdfs"
+        self.hdfs_server_certificate = hdfs_server_certificate
+        self.name_node_address = name_node_address
+        self.protocol = protocol
+
+
+class HDInsightSchema(_serialization.Model):
+    """HDInsightSchema.
+
+    :ivar properties: HDInsight compute properties.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.HDInsightProperties
+    """
+
+    _attribute_map = {
+        "properties": {"key": "properties", "type": "HDInsightProperties"},
+    }
+
+    def __init__(self, *, properties: Optional["_models.HDInsightProperties"] = None, **kwargs: Any) -> None:
+        """
+        :keyword properties: HDInsight compute properties.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.HDInsightProperties
+        """
+        super().__init__(**kwargs)
+        self.properties = properties
+
+
+class HDInsight(Compute, HDInsightSchema):  # pylint: disable=too-many-instance-attributes
+    """A HDInsight compute.
+
     Variables are only populated by the server, and will be ignored when sending a request.
 
     All required parameters must be populated in order to send to Azure.
@@ -9413,7 +10781,7 @@ class HDInsight(Compute, HDInsightSchema):  # pylint: disable=too-many-instance-
     :vartype compute_location: str
     :ivar provisioning_state: The provision state of the cluster. Valid values are Unknown,
      Updating, Provisioning, Succeeded, and Failed. Known values are: "Unknown", "Updating",
-     "Creating", "Deleting", "Succeeded", "Failed", and "Canceled".
+     "Creating", "Deleting", "Succeeded", "Failed", "Canceled", and "SoftDeleted".
     :vartype provisioning_state: str or
      ~azure.mgmt.machinelearningservices.models.ProvisioningState
     :ivar description: The description of the Machine Learning compute.
@@ -9465,8 +10833,8 @@ def __init__(
         description: Optional[str] = None,
         resource_id: Optional[str] = None,
         disable_local_auth: Optional[bool] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword properties: HDInsight compute properties.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.HDInsightProperties
@@ -9525,8 +10893,8 @@ def __init__(
         ssh_port: Optional[int] = None,
         address: Optional[str] = None,
         administrator_account: Optional["_models.VirtualMachineSshCredentials"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword ssh_port: Port open for ssh connections on the master node of the cluster.
         :paramtype ssh_port: int
@@ -9564,7 +10932,7 @@ class IdAssetReference(AssetReferenceBase):
         "asset_id": {"key": "assetId", "type": "str"},
     }
 
-    def __init__(self, *, asset_id: str, **kwargs):
+    def __init__(self, *, asset_id: str, **kwargs: Any) -> None:
         """
         :keyword asset_id: [Required] ARM resource ID of the asset. Required.
         :paramtype asset_id: str
@@ -9586,7 +10954,7 @@ class IdentityForCmk(_serialization.Model):
         "user_assigned_identity": {"key": "userAssignedIdentity", "type": "str"},
     }
 
-    def __init__(self, *, user_assigned_identity: Optional[str] = None, **kwargs):
+    def __init__(self, *, user_assigned_identity: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword user_assigned_identity: The ArmId of the user assigned identity that will be used to
          access the customer managed key vault.
@@ -9596,23 +10964,89 @@ def __init__(self, *, user_assigned_identity: Optional[str] = None, **kwargs):
         self.user_assigned_identity = user_assigned_identity
 
 
+class IdleShutdownSetting(_serialization.Model):
+    """Stops compute instance after user defined period of inactivity.
+
+    :ivar idle_time_before_shutdown: Time is defined in ISO8601 format. Minimum is 15 min, maximum
+     is 3 days.
+    :vartype idle_time_before_shutdown: str
+    """
+
+    _attribute_map = {
+        "idle_time_before_shutdown": {"key": "idleTimeBeforeShutdown", "type": "str"},
+    }
+
+    def __init__(self, *, idle_time_before_shutdown: Optional[str] = None, **kwargs: Any) -> None:
+        """
+        :keyword idle_time_before_shutdown: Time is defined in ISO8601 format. Minimum is 15 min,
+         maximum is 3 days.
+        :paramtype idle_time_before_shutdown: str
+        """
+        super().__init__(**kwargs)
+        self.idle_time_before_shutdown = idle_time_before_shutdown
+
+
+class Image(_serialization.Model):
+    """Image.
+
+    :ivar additional_properties: Unmatched properties from the message are deserialized to this
+     collection.
+    :vartype additional_properties: dict[str, any]
+    :ivar type: Type of the image. Possible values are: docker - For docker images. azureml - For
+     AzureML images. Known values are: "docker" and "azureml".
+    :vartype type: str or ~azure.mgmt.machinelearningservices.models.ImageType
+    :ivar reference: Image reference URL.
+    :vartype reference: str
+    """
+
+    _attribute_map = {
+        "additional_properties": {"key": "", "type": "{object}"},
+        "type": {"key": "type", "type": "str"},
+        "reference": {"key": "reference", "type": "str"},
+    }
+
+    def __init__(
+        self,
+        *,
+        additional_properties: Optional[Dict[str, Any]] = None,
+        type: Union[str, "_models.ImageType"] = "docker",
+        reference: Optional[str] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword additional_properties: Unmatched properties from the message are deserialized to this
+         collection.
+        :paramtype additional_properties: dict[str, any]
+        :keyword type: Type of the image. Possible values are: docker - For docker images. azureml -
+         For AzureML images. Known values are: "docker" and "azureml".
+        :paramtype type: str or ~azure.mgmt.machinelearningservices.models.ImageType
+        :keyword reference: Image reference URL.
+        :paramtype reference: str
+        """
+        super().__init__(**kwargs)
+        self.additional_properties = additional_properties
+        self.type = type
+        self.reference = reference
+
+
 class ImageVertical(_serialization.Model):
     """Abstract class for AutoML tasks that train image (computer vision) models -
-    such as Image Classification / Image Classification Multilabel / Image Object Detection / Image Instance Segmentation.
+    such as Image Classification / Image Classification Multilabel / Image Object Detection / Image
+    Instance Segmentation.
 
-        All required parameters must be populated in order to send to Azure.
+    All required parameters must be populated in order to send to Azure.
 
-        :ivar limit_settings: [Required] Limit settings for the AutoML job. Required.
-        :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
-        :ivar sweep_settings: Model sweeping and hyperparameter sweeping related settings.
-        :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
-        :ivar validation_data: Validation data inputs.
-        :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
-        :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
-         validation purpose.
-         Values between (0.0 , 1.0)
-         Applied when validation dataset is not provided.
-        :vartype validation_data_size: float
+    :ivar limit_settings: [Required] Limit settings for the AutoML job. Required.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+    :ivar sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+    :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
+     validation purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype validation_data_size: float
     """
 
     _validation = {
@@ -9633,8 +11067,8 @@ def __init__(
         sweep_settings: Optional["_models.ImageSweepSettings"] = None,
         validation_data: Optional["_models.MLTableJobInput"] = None,
         validation_data_size: Optional[float] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword limit_settings: [Required] Limit settings for the AutoML job. Required.
         :paramtype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
@@ -9702,8 +11136,8 @@ def __init__(
         validation_data_size: Optional[float] = None,
         model_settings: Optional["_models.ImageModelSettingsClassification"] = None,
         search_space: Optional[List["_models.ImageModelDistributionSettingsClassification"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword limit_settings: [Required] Limit settings for the AutoML job. Required.
         :paramtype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
@@ -9736,47 +11170,49 @@ def __init__(
 
 
 class ImageClassification(ImageClassificationBase, AutoMLVertical):  # pylint: disable=too-many-instance-attributes
-    """Image Classification. Multi-class image classification is used when an image is classified with only a single label
-    from a set of classes - e.g. each image is classified as either an image of a 'cat' or a 'dog' or a 'duck'.
+    """Image Classification. Multi-class image classification is used when an image is classified with
+    only a single label
+    from a set of classes - e.g. each image is classified as either an image of a 'cat' or a 'dog'
+    or a 'duck'.
 
-        All required parameters must be populated in order to send to Azure.
+    All required parameters must be populated in order to send to Azure.
 
-        :ivar log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
-         "Warning", "Error", and "Critical".
-        :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
-        :ivar target_column_name: Target column name: This is prediction values column.
-         Also known as label column name in context of classification tasks.
-        :vartype target_column_name: str
-        :ivar task_type: [Required] Task type for AutoMLJob. Required. Known values are:
-         "Classification", "Regression", "Forecasting", "ImageClassification",
-         "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
-         "TextClassification", "TextClassificationMultilabel", and "TextNER".
-        :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
-        :ivar training_data: [Required] Training data input. Required.
-        :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
-        :ivar limit_settings: [Required] Limit settings for the AutoML job. Required.
-        :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
-        :ivar sweep_settings: Model sweeping and hyperparameter sweeping related settings.
-        :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
-        :ivar validation_data: Validation data inputs.
-        :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
-        :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
-         validation purpose.
-         Values between (0.0 , 1.0)
-         Applied when validation dataset is not provided.
-        :vartype validation_data_size: float
-        :ivar model_settings: Settings used for training the model.
-        :vartype model_settings:
-         ~azure.mgmt.machinelearningservices.models.ImageModelSettingsClassification
-        :ivar search_space: Search space for sampling different combinations of models and their
-         hyperparameters.
-        :vartype search_space:
-         list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsClassification]
-        :ivar primary_metric: Primary metric to optimize for this task. Known values are:
-         "AUCWeighted", "Accuracy", "NormMacroRecall", "AveragePrecisionScoreWeighted", and
-         "PrecisionScoreWeighted".
-        :vartype primary_metric: str or
-         ~azure.mgmt.machinelearningservices.models.ClassificationPrimaryMetrics
+    :ivar log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
+     "Warning", "Error", and "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: [Required] Task type for AutoMLJob. Required. Known values are:
+     "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", and "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: [Required] Training data input. Required.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar limit_settings: [Required] Limit settings for the AutoML job. Required.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+    :ivar sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+    :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
+     validation purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype validation_data_size: float
+    :ivar model_settings: Settings used for training the model.
+    :vartype model_settings:
+     ~azure.mgmt.machinelearningservices.models.ImageModelSettingsClassification
+    :ivar search_space: Search space for sampling different combinations of models and their
+     hyperparameters.
+    :vartype search_space:
+     list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsClassification]
+    :ivar primary_metric: Primary metric to optimize for this task. Known values are:
+     "AUCWeighted", "Accuracy", "NormMacroRecall", "AveragePrecisionScoreWeighted", and
+     "PrecisionScoreWeighted".
+    :vartype primary_metric: str or
+     ~azure.mgmt.machinelearningservices.models.ClassificationPrimaryMetrics
     """
 
     _validation = {
@@ -9812,8 +11248,8 @@ def __init__(
         model_settings: Optional["_models.ImageModelSettingsClassification"] = None,
         search_space: Optional[List["_models.ImageModelDistributionSettingsClassification"]] = None,
         primary_metric: Optional[Union[str, "_models.ClassificationPrimaryMetrics"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
          "Warning", "Error", and "Critical".
@@ -9875,47 +11311,48 @@ def __init__(
 class ImageClassificationMultilabel(
     ImageClassificationBase, AutoMLVertical
 ):  # pylint: disable=too-many-instance-attributes
-    """Image Classification Multilabel. Multi-label image classification is used when an image could have one or more labels
+    """Image Classification Multilabel. Multi-label image classification is used when an image could
+    have one or more labels
     from a set of labels - e.g. an image could be labeled with both 'cat' and 'dog'.
 
-        All required parameters must be populated in order to send to Azure.
+    All required parameters must be populated in order to send to Azure.
 
-        :ivar log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
-         "Warning", "Error", and "Critical".
-        :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
-        :ivar target_column_name: Target column name: This is prediction values column.
-         Also known as label column name in context of classification tasks.
-        :vartype target_column_name: str
-        :ivar task_type: [Required] Task type for AutoMLJob. Required. Known values are:
-         "Classification", "Regression", "Forecasting", "ImageClassification",
-         "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
-         "TextClassification", "TextClassificationMultilabel", and "TextNER".
-        :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
-        :ivar training_data: [Required] Training data input. Required.
-        :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
-        :ivar limit_settings: [Required] Limit settings for the AutoML job. Required.
-        :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
-        :ivar sweep_settings: Model sweeping and hyperparameter sweeping related settings.
-        :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
-        :ivar validation_data: Validation data inputs.
-        :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
-        :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
-         validation purpose.
-         Values between (0.0 , 1.0)
-         Applied when validation dataset is not provided.
-        :vartype validation_data_size: float
-        :ivar model_settings: Settings used for training the model.
-        :vartype model_settings:
-         ~azure.mgmt.machinelearningservices.models.ImageModelSettingsClassification
-        :ivar search_space: Search space for sampling different combinations of models and their
-         hyperparameters.
-        :vartype search_space:
-         list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsClassification]
-        :ivar primary_metric: Primary metric to optimize for this task. Known values are:
-         "AUCWeighted", "Accuracy", "NormMacroRecall", "AveragePrecisionScoreWeighted",
-         "PrecisionScoreWeighted", and "IOU".
-        :vartype primary_metric: str or
-         ~azure.mgmt.machinelearningservices.models.ClassificationMultilabelPrimaryMetrics
+    :ivar log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
+     "Warning", "Error", and "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: [Required] Task type for AutoMLJob. Required. Known values are:
+     "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", and "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: [Required] Training data input. Required.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar limit_settings: [Required] Limit settings for the AutoML job. Required.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+    :ivar sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+    :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
+     validation purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype validation_data_size: float
+    :ivar model_settings: Settings used for training the model.
+    :vartype model_settings:
+     ~azure.mgmt.machinelearningservices.models.ImageModelSettingsClassification
+    :ivar search_space: Search space for sampling different combinations of models and their
+     hyperparameters.
+    :vartype search_space:
+     list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsClassification]
+    :ivar primary_metric: Primary metric to optimize for this task. Known values are:
+     "AUCWeighted", "Accuracy", "NormMacroRecall", "AveragePrecisionScoreWeighted",
+     "PrecisionScoreWeighted", and "IOU".
+    :vartype primary_metric: str or
+     ~azure.mgmt.machinelearningservices.models.ClassificationMultilabelPrimaryMetrics
     """
 
     _validation = {
@@ -9951,8 +11388,8 @@ def __init__(
         model_settings: Optional["_models.ImageModelSettingsClassification"] = None,
         search_space: Optional[List["_models.ImageModelDistributionSettingsClassification"]] = None,
         primary_metric: Optional[Union[str, "_models.ClassificationMultilabelPrimaryMetrics"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
          "Warning", "Error", and "Critical".
@@ -10058,8 +11495,8 @@ def __init__(
         validation_data_size: Optional[float] = None,
         model_settings: Optional["_models.ImageModelSettingsObjectDetection"] = None,
         search_space: Optional[List["_models.ImageModelDistributionSettingsObjectDetection"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword limit_settings: [Required] Limit settings for the AutoML job. Required.
         :paramtype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
@@ -10094,45 +11531,46 @@ def __init__(
 class ImageInstanceSegmentation(
     ImageObjectDetectionBase, AutoMLVertical
 ):  # pylint: disable=too-many-instance-attributes
-    """Image Instance Segmentation. Instance segmentation is used to identify objects in an image at the pixel level,
+    """Image Instance Segmentation. Instance segmentation is used to identify objects in an image at
+    the pixel level,
     drawing a polygon around each object in the image.
 
-        All required parameters must be populated in order to send to Azure.
+    All required parameters must be populated in order to send to Azure.
 
-        :ivar log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
-         "Warning", "Error", and "Critical".
-        :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
-        :ivar target_column_name: Target column name: This is prediction values column.
-         Also known as label column name in context of classification tasks.
-        :vartype target_column_name: str
-        :ivar task_type: [Required] Task type for AutoMLJob. Required. Known values are:
-         "Classification", "Regression", "Forecasting", "ImageClassification",
-         "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
-         "TextClassification", "TextClassificationMultilabel", and "TextNER".
-        :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
-        :ivar training_data: [Required] Training data input. Required.
-        :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
-        :ivar limit_settings: [Required] Limit settings for the AutoML job. Required.
-        :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
-        :ivar sweep_settings: Model sweeping and hyperparameter sweeping related settings.
-        :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
-        :ivar validation_data: Validation data inputs.
-        :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
-        :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
-         validation purpose.
-         Values between (0.0 , 1.0)
-         Applied when validation dataset is not provided.
-        :vartype validation_data_size: float
-        :ivar model_settings: Settings used for training the model.
-        :vartype model_settings:
-         ~azure.mgmt.machinelearningservices.models.ImageModelSettingsObjectDetection
-        :ivar search_space: Search space for sampling different combinations of models and their
-         hyperparameters.
-        :vartype search_space:
-         list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsObjectDetection]
-        :ivar primary_metric: Primary metric to optimize for this task. "MeanAveragePrecision"
-        :vartype primary_metric: str or
-         ~azure.mgmt.machinelearningservices.models.InstanceSegmentationPrimaryMetrics
+    :ivar log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
+     "Warning", "Error", and "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: [Required] Task type for AutoMLJob. Required. Known values are:
+     "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", and "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: [Required] Training data input. Required.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar limit_settings: [Required] Limit settings for the AutoML job. Required.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+    :ivar sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+    :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
+     validation purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype validation_data_size: float
+    :ivar model_settings: Settings used for training the model.
+    :vartype model_settings:
+     ~azure.mgmt.machinelearningservices.models.ImageModelSettingsObjectDetection
+    :ivar search_space: Search space for sampling different combinations of models and their
+     hyperparameters.
+    :vartype search_space:
+     list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsObjectDetection]
+    :ivar primary_metric: Primary metric to optimize for this task. "MeanAveragePrecision"
+    :vartype primary_metric: str or
+     ~azure.mgmt.machinelearningservices.models.InstanceSegmentationPrimaryMetrics
     """
 
     _validation = {
@@ -10168,8 +11606,8 @@ def __init__(
         model_settings: Optional["_models.ImageModelSettingsObjectDetection"] = None,
         search_space: Optional[List["_models.ImageModelDistributionSettingsObjectDetection"]] = None,
         primary_metric: Optional[Union[str, "_models.InstanceSegmentationPrimaryMetrics"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
          "Warning", "Error", and "Critical".
@@ -10244,8 +11682,8 @@ class ImageLimitSettings(_serialization.Model):
     }
 
     def __init__(
-        self, *, max_concurrent_trials: int = 1, max_trials: int = 1, timeout: datetime.timedelta = "P7D", **kwargs
-    ):
+        self, *, max_concurrent_trials: int = 1, max_trials: int = 1, timeout: datetime.timedelta = "P7D", **kwargs: Any
+    ) -> None:
         """
         :keyword max_concurrent_trials: Maximum number of concurrent AutoML iterations.
         :paramtype max_concurrent_trials: int
@@ -10260,6 +11698,49 @@ def __init__(
         self.timeout = timeout
 
 
+class ImageMetadata(_serialization.Model):
+    """Returns metadata about the operating system image for this compute instance.
+
+    :ivar current_image_version: Specifies the current operating system image version this compute
+     instance is running on.
+    :vartype current_image_version: str
+    :ivar latest_image_version: Specifies the latest available operating system image version.
+    :vartype latest_image_version: str
+    :ivar is_latest_os_image_version: Specifies whether this compute instance is running on the
+     latest operating system image.
+    :vartype is_latest_os_image_version: bool
+    """
+
+    _attribute_map = {
+        "current_image_version": {"key": "currentImageVersion", "type": "str"},
+        "latest_image_version": {"key": "latestImageVersion", "type": "str"},
+        "is_latest_os_image_version": {"key": "isLatestOsImageVersion", "type": "bool"},
+    }
+
+    def __init__(
+        self,
+        *,
+        current_image_version: Optional[str] = None,
+        latest_image_version: Optional[str] = None,
+        is_latest_os_image_version: Optional[bool] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword current_image_version: Specifies the current operating system image version this
+         compute instance is running on.
+        :paramtype current_image_version: str
+        :keyword latest_image_version: Specifies the latest available operating system image version.
+        :paramtype latest_image_version: str
+        :keyword is_latest_os_image_version: Specifies whether this compute instance is running on the
+         latest operating system image.
+        :paramtype is_latest_os_image_version: bool
+        """
+        super().__init__(**kwargs)
+        self.current_image_version = current_image_version
+        self.latest_image_version = latest_image_version
+        self.is_latest_os_image_version = is_latest_os_image_version
+
+
 class ImageModelDistributionSettings(_serialization.Model):  # pylint: disable=too-many-instance-attributes
     """Distribution expressions to sweep over values of model settings.
 
@@ -10270,91 +11751,92 @@ class ImageModelDistributionSettings(_serialization.Model):  # pylint: disable=t
     LearningRate = "uniform(0.001, 0.01)";
     LayersToFreeze = "choice(0, 2)";
     </code></example>`
-    All distributions can be specified as distribution_name(min, max) or choice(val1, val2, ..., valn)
+    All distributions can be specified as distribution_name(min, max) or choice(val1, val2, ...,
+    valn)
     where distribution name can be: uniform, quniform, loguniform, etc
     For more details on how to compose distribution expressions please check the documentation:
     https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters
     For more information on the available settings please visit the official documentation:
     https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
 
-        :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
-        :vartype ams_gradient: str
-        :ivar augmentations: Settings for using Augmentations.
-        :vartype augmentations: str
-        :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
-         [0, 1].
-        :vartype beta1: str
-        :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
-         [0, 1].
-        :vartype beta2: str
-        :ivar distributed: Whether to use distributer training.
-        :vartype distributed: str
-        :ivar early_stopping: Enable early stopping logic during training.
-        :vartype early_stopping: str
-        :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
-         primary metric improvement
-         is tracked for early stopping. Must be a positive integer.
-        :vartype early_stopping_delay: str
-        :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
-         primary metric improvement before
-         the run is stopped. Must be a positive integer.
-        :vartype early_stopping_patience: str
-        :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
-        :vartype enable_onnx_normalization: str
-        :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
-         be a positive integer.
-        :vartype evaluation_frequency: str
-        :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
-         "GradAccumulationStep" steps without
-         updating the model weights while accumulating the gradients of those steps, and then using
-         the accumulated gradients to compute the weight updates. Must be a positive integer.
-        :vartype gradient_accumulation_step: str
-        :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
-         For instance, passing 2 as value for 'seresnext' means
-         freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
-         please
-         see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
-        :vartype layers_to_freeze: str
-        :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
-        :vartype learning_rate: str
-        :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
-         'step'.
-        :vartype learning_rate_scheduler: str
-        :ivar model_name: Name of the model to use for training.
-         For more information on the available models please visit the official documentation:
-         https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
-        :vartype model_name: str
-        :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
-        :vartype momentum: str
-        :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
-        :vartype nesterov: str
-        :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
-        :vartype number_of_epochs: str
-        :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
-        :vartype number_of_workers: str
-        :ivar optimizer: Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
-        :vartype optimizer: str
-        :ivar random_seed: Random seed to be used when using deterministic training.
-        :vartype random_seed: str
-        :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
-         the range [0, 1].
-        :vartype step_lr_gamma: str
-        :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
-         positive integer.
-        :vartype step_lr_step_size: str
-        :ivar training_batch_size: Training batch size. Must be a positive integer.
-        :vartype training_batch_size: str
-        :ivar validation_batch_size: Validation batch size. Must be a positive integer.
-        :vartype validation_batch_size: str
-        :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
-         'warmup_cosine'. Must be a float in the range [0, 1].
-        :vartype warmup_cosine_lr_cycles: str
-        :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
-         'warmup_cosine'. Must be a positive integer.
-        :vartype warmup_cosine_lr_warmup_epochs: str
-        :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
-         a float in the range[0, 1].
-        :vartype weight_decay: str
+    :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+    :vartype ams_gradient: str
+    :ivar augmentations: Settings for using Augmentations.
+    :vartype augmentations: str
+    :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta1: str
+    :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta2: str
+    :ivar distributed: Whether to use distributer training.
+    :vartype distributed: str
+    :ivar early_stopping: Enable early stopping logic during training.
+    :vartype early_stopping: str
+    :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
+     primary metric improvement
+     is tracked for early stopping. Must be a positive integer.
+    :vartype early_stopping_delay: str
+    :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
+     primary metric improvement before
+     the run is stopped. Must be a positive integer.
+    :vartype early_stopping_patience: str
+    :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
+    :vartype enable_onnx_normalization: str
+    :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
+     be a positive integer.
+    :vartype evaluation_frequency: str
+    :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
+     "GradAccumulationStep" steps without
+     updating the model weights while accumulating the gradients of those steps, and then using
+     the accumulated gradients to compute the weight updates. Must be a positive integer.
+    :vartype gradient_accumulation_step: str
+    :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
+     For instance, passing 2 as value for 'seresnext' means
+     freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+     please
+     see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype layers_to_freeze: str
+    :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+    :vartype learning_rate: str
+    :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+     'step'.
+    :vartype learning_rate_scheduler: str
+    :ivar model_name: Name of the model to use for training.
+     For more information on the available models please visit the official documentation:
+     https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype model_name: str
+    :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
+    :vartype momentum: str
+    :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
+    :vartype nesterov: str
+    :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
+    :vartype number_of_epochs: str
+    :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
+    :vartype number_of_workers: str
+    :ivar optimizer: Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
+    :vartype optimizer: str
+    :ivar random_seed: Random seed to be used when using deterministic training.
+    :vartype random_seed: str
+    :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
+     the range [0, 1].
+    :vartype step_lr_gamma: str
+    :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
+     positive integer.
+    :vartype step_lr_step_size: str
+    :ivar training_batch_size: Training batch size. Must be a positive integer.
+    :vartype training_batch_size: str
+    :ivar validation_batch_size: Validation batch size. Must be a positive integer.
+    :vartype validation_batch_size: str
+    :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+     'warmup_cosine'. Must be a float in the range [0, 1].
+    :vartype warmup_cosine_lr_cycles: str
+    :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+     'warmup_cosine'. Must be a positive integer.
+    :vartype warmup_cosine_lr_warmup_epochs: str
+    :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
+     a float in the range[0, 1].
+    :vartype weight_decay: str
     """
 
     _attribute_map = {
@@ -10419,8 +11901,8 @@ def __init__(  # pylint: disable=too-many-locals
         warmup_cosine_lr_cycles: Optional[str] = None,
         warmup_cosine_lr_warmup_epochs: Optional[str] = None,
         weight_decay: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
         :paramtype ams_gradient: str
@@ -10551,97 +12033,97 @@ class ImageModelDistributionSettingsClassification(
     For more information on the available settings please visit the official documentation:
     https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
 
-        :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
-        :vartype ams_gradient: str
-        :ivar augmentations: Settings for using Augmentations.
-        :vartype augmentations: str
-        :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
-         [0, 1].
-        :vartype beta1: str
-        :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
-         [0, 1].
-        :vartype beta2: str
-        :ivar distributed: Whether to use distributer training.
-        :vartype distributed: str
-        :ivar early_stopping: Enable early stopping logic during training.
-        :vartype early_stopping: str
-        :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
-         primary metric improvement
-         is tracked for early stopping. Must be a positive integer.
-        :vartype early_stopping_delay: str
-        :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
-         primary metric improvement before
-         the run is stopped. Must be a positive integer.
-        :vartype early_stopping_patience: str
-        :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
-        :vartype enable_onnx_normalization: str
-        :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
-         be a positive integer.
-        :vartype evaluation_frequency: str
-        :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
-         "GradAccumulationStep" steps without
-         updating the model weights while accumulating the gradients of those steps, and then using
-         the accumulated gradients to compute the weight updates. Must be a positive integer.
-        :vartype gradient_accumulation_step: str
-        :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
-         For instance, passing 2 as value for 'seresnext' means
-         freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
-         please
-         see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
-        :vartype layers_to_freeze: str
-        :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
-        :vartype learning_rate: str
-        :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
-         'step'.
-        :vartype learning_rate_scheduler: str
-        :ivar model_name: Name of the model to use for training.
-         For more information on the available models please visit the official documentation:
-         https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
-        :vartype model_name: str
-        :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
-        :vartype momentum: str
-        :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
-        :vartype nesterov: str
-        :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
-        :vartype number_of_epochs: str
-        :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
-        :vartype number_of_workers: str
-        :ivar optimizer: Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
-        :vartype optimizer: str
-        :ivar random_seed: Random seed to be used when using deterministic training.
-        :vartype random_seed: str
-        :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
-         the range [0, 1].
-        :vartype step_lr_gamma: str
-        :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
-         positive integer.
-        :vartype step_lr_step_size: str
-        :ivar training_batch_size: Training batch size. Must be a positive integer.
-        :vartype training_batch_size: str
-        :ivar validation_batch_size: Validation batch size. Must be a positive integer.
-        :vartype validation_batch_size: str
-        :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
-         'warmup_cosine'. Must be a float in the range [0, 1].
-        :vartype warmup_cosine_lr_cycles: str
-        :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
-         'warmup_cosine'. Must be a positive integer.
-        :vartype warmup_cosine_lr_warmup_epochs: str
-        :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
-         a float in the range[0, 1].
-        :vartype weight_decay: str
-        :ivar training_crop_size: Image crop size that is input to the neural network for the training
-         dataset. Must be a positive integer.
-        :vartype training_crop_size: str
-        :ivar validation_crop_size: Image crop size that is input to the neural network for the
-         validation dataset. Must be a positive integer.
-        :vartype validation_crop_size: str
-        :ivar validation_resize_size: Image size to which to resize before cropping for validation
-         dataset. Must be a positive integer.
-        :vartype validation_resize_size: str
-        :ivar weighted_loss: Weighted loss. The accepted values are 0 for no weighted loss.
-         1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be
-         0 or 1 or 2.
-        :vartype weighted_loss: str
+    :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+    :vartype ams_gradient: str
+    :ivar augmentations: Settings for using Augmentations.
+    :vartype augmentations: str
+    :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta1: str
+    :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta2: str
+    :ivar distributed: Whether to use distributer training.
+    :vartype distributed: str
+    :ivar early_stopping: Enable early stopping logic during training.
+    :vartype early_stopping: str
+    :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
+     primary metric improvement
+     is tracked for early stopping. Must be a positive integer.
+    :vartype early_stopping_delay: str
+    :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
+     primary metric improvement before
+     the run is stopped. Must be a positive integer.
+    :vartype early_stopping_patience: str
+    :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
+    :vartype enable_onnx_normalization: str
+    :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
+     be a positive integer.
+    :vartype evaluation_frequency: str
+    :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
+     "GradAccumulationStep" steps without
+     updating the model weights while accumulating the gradients of those steps, and then using
+     the accumulated gradients to compute the weight updates. Must be a positive integer.
+    :vartype gradient_accumulation_step: str
+    :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
+     For instance, passing 2 as value for 'seresnext' means
+     freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+     please
+     see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype layers_to_freeze: str
+    :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+    :vartype learning_rate: str
+    :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+     'step'.
+    :vartype learning_rate_scheduler: str
+    :ivar model_name: Name of the model to use for training.
+     For more information on the available models please visit the official documentation:
+     https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype model_name: str
+    :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
+    :vartype momentum: str
+    :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
+    :vartype nesterov: str
+    :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
+    :vartype number_of_epochs: str
+    :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
+    :vartype number_of_workers: str
+    :ivar optimizer: Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
+    :vartype optimizer: str
+    :ivar random_seed: Random seed to be used when using deterministic training.
+    :vartype random_seed: str
+    :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
+     the range [0, 1].
+    :vartype step_lr_gamma: str
+    :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
+     positive integer.
+    :vartype step_lr_step_size: str
+    :ivar training_batch_size: Training batch size. Must be a positive integer.
+    :vartype training_batch_size: str
+    :ivar validation_batch_size: Validation batch size. Must be a positive integer.
+    :vartype validation_batch_size: str
+    :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+     'warmup_cosine'. Must be a float in the range [0, 1].
+    :vartype warmup_cosine_lr_cycles: str
+    :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+     'warmup_cosine'. Must be a positive integer.
+    :vartype warmup_cosine_lr_warmup_epochs: str
+    :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
+     a float in the range[0, 1].
+    :vartype weight_decay: str
+    :ivar training_crop_size: Image crop size that is input to the neural network for the training
+     dataset. Must be a positive integer.
+    :vartype training_crop_size: str
+    :ivar validation_crop_size: Image crop size that is input to the neural network for the
+     validation dataset. Must be a positive integer.
+    :vartype validation_crop_size: str
+    :ivar validation_resize_size: Image size to which to resize before cropping for validation
+     dataset. Must be a positive integer.
+    :vartype validation_resize_size: str
+    :ivar weighted_loss: Weighted loss. The accepted values are 0 for no weighted loss.
+     1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be
+     0 or 1 or 2.
+    :vartype weighted_loss: str
     """
 
     _attribute_map = {
@@ -10714,8 +12196,8 @@ def __init__(  # pylint: disable=too-many-locals
         validation_crop_size: Optional[str] = None,
         validation_resize_size: Optional[str] = None,
         weighted_loss: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
         :paramtype ams_gradient: str
@@ -10865,136 +12347,136 @@ class ImageModelDistributionSettingsObjectDetection(
     For more information on the available settings please visit the official documentation:
     https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
 
-        :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
-        :vartype ams_gradient: str
-        :ivar augmentations: Settings for using Augmentations.
-        :vartype augmentations: str
-        :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
-         [0, 1].
-        :vartype beta1: str
-        :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
-         [0, 1].
-        :vartype beta2: str
-        :ivar distributed: Whether to use distributer training.
-        :vartype distributed: str
-        :ivar early_stopping: Enable early stopping logic during training.
-        :vartype early_stopping: str
-        :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
-         primary metric improvement
-         is tracked for early stopping. Must be a positive integer.
-        :vartype early_stopping_delay: str
-        :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
-         primary metric improvement before
-         the run is stopped. Must be a positive integer.
-        :vartype early_stopping_patience: str
-        :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
-        :vartype enable_onnx_normalization: str
-        :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
-         be a positive integer.
-        :vartype evaluation_frequency: str
-        :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
-         "GradAccumulationStep" steps without
-         updating the model weights while accumulating the gradients of those steps, and then using
-         the accumulated gradients to compute the weight updates. Must be a positive integer.
-        :vartype gradient_accumulation_step: str
-        :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
-         For instance, passing 2 as value for 'seresnext' means
-         freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
-         please
-         see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
-        :vartype layers_to_freeze: str
-        :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
-        :vartype learning_rate: str
-        :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
-         'step'.
-        :vartype learning_rate_scheduler: str
-        :ivar model_name: Name of the model to use for training.
-         For more information on the available models please visit the official documentation:
-         https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
-        :vartype model_name: str
-        :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
-        :vartype momentum: str
-        :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
-        :vartype nesterov: str
-        :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
-        :vartype number_of_epochs: str
-        :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
-        :vartype number_of_workers: str
-        :ivar optimizer: Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
-        :vartype optimizer: str
-        :ivar random_seed: Random seed to be used when using deterministic training.
-        :vartype random_seed: str
-        :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
-         the range [0, 1].
-        :vartype step_lr_gamma: str
-        :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
-         positive integer.
-        :vartype step_lr_step_size: str
-        :ivar training_batch_size: Training batch size. Must be a positive integer.
-        :vartype training_batch_size: str
-        :ivar validation_batch_size: Validation batch size. Must be a positive integer.
-        :vartype validation_batch_size: str
-        :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
-         'warmup_cosine'. Must be a float in the range [0, 1].
-        :vartype warmup_cosine_lr_cycles: str
-        :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
-         'warmup_cosine'. Must be a positive integer.
-        :vartype warmup_cosine_lr_warmup_epochs: str
-        :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
-         a float in the range[0, 1].
-        :vartype weight_decay: str
-        :ivar box_detections_per_image: Maximum number of detections per image, for all classes. Must
-         be a positive integer.
-         Note: This settings is not supported for the 'yolov5' algorithm.
-        :vartype box_detections_per_image: str
-        :ivar box_score_threshold: During inference, only return proposals with a classification score
-         greater than
-         BoxScoreThreshold. Must be a float in the range[0, 1].
-        :vartype box_score_threshold: str
-        :ivar image_size: Image size for train and validation. Must be a positive integer.
-         Note: The training run may get into CUDA OOM if the size is too big.
-         Note: This settings is only supported for the 'yolov5' algorithm.
-        :vartype image_size: str
-        :ivar max_size: Maximum size of the image to be rescaled before feeding it to the backbone.
-         Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
-         Note: This settings is not supported for the 'yolov5' algorithm.
-        :vartype max_size: str
-        :ivar min_size: Minimum size of the image to be rescaled before feeding it to the backbone.
-         Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
-         Note: This settings is not supported for the 'yolov5' algorithm.
-        :vartype min_size: str
-        :ivar model_size: Model size. Must be 'small', 'medium', 'large', or 'xlarge'.
-         Note: training run may get into CUDA OOM if the model size is too big.
-         Note: This settings is only supported for the 'yolov5' algorithm.
-        :vartype model_size: str
-        :ivar multi_scale: Enable multi-scale image by varying image size by +/- 50%.
-         Note: training run may get into CUDA OOM if no sufficient GPU memory.
-         Note: This settings is only supported for the 'yolov5' algorithm.
-        :vartype multi_scale: str
-        :ivar nms_iou_threshold: IOU threshold used during inference in NMS post processing. Must be
-         float in the range [0, 1].
-        :vartype nms_iou_threshold: str
-        :ivar tile_grid_size: The grid size to use for tiling each image. Note: TileGridSize must not
-         be
-         None to enable small object detection logic. A string containing two integers in mxn format.
-         Note: This settings is not supported for the 'yolov5' algorithm.
-        :vartype tile_grid_size: str
-        :ivar tile_overlap_ratio: Overlap ratio between adjacent tiles in each dimension. Must be float
-         in the range [0, 1).
-         Note: This settings is not supported for the 'yolov5' algorithm.
-        :vartype tile_overlap_ratio: str
-        :ivar tile_predictions_nms_threshold: The IOU threshold to use to perform NMS while merging
-         predictions from tiles and image.
-         Used in validation/ inference. Must be float in the range [0, 1].
-         Note: This settings is not supported for the 'yolov5' algorithm.
-         NMS: Non-maximum suppression.
-        :vartype tile_predictions_nms_threshold: str
-        :ivar validation_iou_threshold: IOU threshold to use when computing validation metric. Must be
-         float in the range [0, 1].
-        :vartype validation_iou_threshold: str
-        :ivar validation_metric_type: Metric computation method to use for validation metrics. Must be
-         'none', 'coco', 'voc', or 'coco_voc'.
-        :vartype validation_metric_type: str
+    :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+    :vartype ams_gradient: str
+    :ivar augmentations: Settings for using Augmentations.
+    :vartype augmentations: str
+    :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta1: str
+    :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta2: str
+    :ivar distributed: Whether to use distributer training.
+    :vartype distributed: str
+    :ivar early_stopping: Enable early stopping logic during training.
+    :vartype early_stopping: str
+    :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
+     primary metric improvement
+     is tracked for early stopping. Must be a positive integer.
+    :vartype early_stopping_delay: str
+    :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
+     primary metric improvement before
+     the run is stopped. Must be a positive integer.
+    :vartype early_stopping_patience: str
+    :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
+    :vartype enable_onnx_normalization: str
+    :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
+     be a positive integer.
+    :vartype evaluation_frequency: str
+    :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
+     "GradAccumulationStep" steps without
+     updating the model weights while accumulating the gradients of those steps, and then using
+     the accumulated gradients to compute the weight updates. Must be a positive integer.
+    :vartype gradient_accumulation_step: str
+    :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
+     For instance, passing 2 as value for 'seresnext' means
+     freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+     please
+     see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype layers_to_freeze: str
+    :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+    :vartype learning_rate: str
+    :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+     'step'.
+    :vartype learning_rate_scheduler: str
+    :ivar model_name: Name of the model to use for training.
+     For more information on the available models please visit the official documentation:
+     https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype model_name: str
+    :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
+    :vartype momentum: str
+    :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
+    :vartype nesterov: str
+    :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
+    :vartype number_of_epochs: str
+    :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
+    :vartype number_of_workers: str
+    :ivar optimizer: Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
+    :vartype optimizer: str
+    :ivar random_seed: Random seed to be used when using deterministic training.
+    :vartype random_seed: str
+    :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
+     the range [0, 1].
+    :vartype step_lr_gamma: str
+    :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
+     positive integer.
+    :vartype step_lr_step_size: str
+    :ivar training_batch_size: Training batch size. Must be a positive integer.
+    :vartype training_batch_size: str
+    :ivar validation_batch_size: Validation batch size. Must be a positive integer.
+    :vartype validation_batch_size: str
+    :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+     'warmup_cosine'. Must be a float in the range [0, 1].
+    :vartype warmup_cosine_lr_cycles: str
+    :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+     'warmup_cosine'. Must be a positive integer.
+    :vartype warmup_cosine_lr_warmup_epochs: str
+    :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
+     a float in the range[0, 1].
+    :vartype weight_decay: str
+    :ivar box_detections_per_image: Maximum number of detections per image, for all classes. Must
+     be a positive integer.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype box_detections_per_image: str
+    :ivar box_score_threshold: During inference, only return proposals with a classification score
+     greater than
+     BoxScoreThreshold. Must be a float in the range[0, 1].
+    :vartype box_score_threshold: str
+    :ivar image_size: Image size for train and validation. Must be a positive integer.
+     Note: The training run may get into CUDA OOM if the size is too big.
+     Note: This settings is only supported for the 'yolov5' algorithm.
+    :vartype image_size: str
+    :ivar max_size: Maximum size of the image to be rescaled before feeding it to the backbone.
+     Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype max_size: str
+    :ivar min_size: Minimum size of the image to be rescaled before feeding it to the backbone.
+     Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype min_size: str
+    :ivar model_size: Model size. Must be 'small', 'medium', 'large', or 'xlarge'.
+     Note: training run may get into CUDA OOM if the model size is too big.
+     Note: This settings is only supported for the 'yolov5' algorithm.
+    :vartype model_size: str
+    :ivar multi_scale: Enable multi-scale image by varying image size by +/- 50%.
+     Note: training run may get into CUDA OOM if no sufficient GPU memory.
+     Note: This settings is only supported for the 'yolov5' algorithm.
+    :vartype multi_scale: str
+    :ivar nms_iou_threshold: IOU threshold used during inference in NMS post processing. Must be
+     float in the range [0, 1].
+    :vartype nms_iou_threshold: str
+    :ivar tile_grid_size: The grid size to use for tiling each image. Note: TileGridSize must not
+     be
+     None to enable small object detection logic. A string containing two integers in mxn format.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype tile_grid_size: str
+    :ivar tile_overlap_ratio: Overlap ratio between adjacent tiles in each dimension. Must be float
+     in the range [0, 1).
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype tile_overlap_ratio: str
+    :ivar tile_predictions_nms_threshold: The IOU threshold to use to perform NMS while merging
+     predictions from tiles and image.
+     Used in validation/ inference. Must be float in the range [0, 1].
+     Note: This settings is not supported for the 'yolov5' algorithm.
+     NMS: Non-maximum suppression.
+    :vartype tile_predictions_nms_threshold: str
+    :ivar validation_iou_threshold: IOU threshold to use when computing validation metric. Must be
+     float in the range [0, 1].
+    :vartype validation_iou_threshold: str
+    :ivar validation_metric_type: Metric computation method to use for validation metrics. Must be
+     'none', 'coco', 'voc', or 'coco_voc'.
+    :vartype validation_metric_type: str
     """
 
     _attribute_map = {
@@ -11085,8 +12567,8 @@ def __init__(  # pylint: disable=too-many-locals
         tile_predictions_nms_threshold: Optional[str] = None,
         validation_iou_threshold: Optional[str] = None,
         validation_metric_type: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
         :paramtype ams_gradient: str
@@ -11272,94 +12754,94 @@ class ImageModelSettings(_serialization.Model):  # pylint: disable=too-many-inst
     For more information on the available settings please visit the official documentation:
     https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
 
-        :ivar advanced_settings: Settings for advanced scenarios.
-        :vartype advanced_settings: str
-        :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
-        :vartype ams_gradient: bool
-        :ivar augmentations: Settings for using Augmentations.
-        :vartype augmentations: str
-        :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
-         [0, 1].
-        :vartype beta1: float
-        :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
-         [0, 1].
-        :vartype beta2: float
-        :ivar checkpoint_frequency: Frequency to store model checkpoints. Must be a positive integer.
-        :vartype checkpoint_frequency: int
-        :ivar checkpoint_model: The pretrained checkpoint model for incremental training.
-        :vartype checkpoint_model: ~azure.mgmt.machinelearningservices.models.MLFlowModelJobInput
-        :ivar checkpoint_run_id: The id of a previous run that has a pretrained checkpoint for
-         incremental training.
-        :vartype checkpoint_run_id: str
-        :ivar distributed: Whether to use distributed training.
-        :vartype distributed: bool
-        :ivar early_stopping: Enable early stopping logic during training.
-        :vartype early_stopping: bool
-        :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
-         primary metric improvement
-         is tracked for early stopping. Must be a positive integer.
-        :vartype early_stopping_delay: int
-        :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
-         primary metric improvement before
-         the run is stopped. Must be a positive integer.
-        :vartype early_stopping_patience: int
-        :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
-        :vartype enable_onnx_normalization: bool
-        :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
-         be a positive integer.
-        :vartype evaluation_frequency: int
-        :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
-         "GradAccumulationStep" steps without
-         updating the model weights while accumulating the gradients of those steps, and then using
-         the accumulated gradients to compute the weight updates. Must be a positive integer.
-        :vartype gradient_accumulation_step: int
-        :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
-         For instance, passing 2 as value for 'seresnext' means
-         freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
-         please
-         see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
-        :vartype layers_to_freeze: int
-        :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
-        :vartype learning_rate: float
-        :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
-         'step'. Known values are: "None", "WarmupCosine", and "Step".
-        :vartype learning_rate_scheduler: str or
-         ~azure.mgmt.machinelearningservices.models.LearningRateScheduler
-        :ivar model_name: Name of the model to use for training.
-         For more information on the available models please visit the official documentation:
-         https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
-        :vartype model_name: str
-        :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
-        :vartype momentum: float
-        :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
-        :vartype nesterov: bool
-        :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
-        :vartype number_of_epochs: int
-        :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
-        :vartype number_of_workers: int
-        :ivar optimizer: Type of optimizer. Known values are: "None", "Sgd", "Adam", and "Adamw".
-        :vartype optimizer: str or ~azure.mgmt.machinelearningservices.models.StochasticOptimizer
-        :ivar random_seed: Random seed to be used when using deterministic training.
-        :vartype random_seed: int
-        :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
-         the range [0, 1].
-        :vartype step_lr_gamma: float
-        :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
-         positive integer.
-        :vartype step_lr_step_size: int
-        :ivar training_batch_size: Training batch size. Must be a positive integer.
-        :vartype training_batch_size: int
-        :ivar validation_batch_size: Validation batch size. Must be a positive integer.
-        :vartype validation_batch_size: int
-        :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
-         'warmup_cosine'. Must be a float in the range [0, 1].
-        :vartype warmup_cosine_lr_cycles: float
-        :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
-         'warmup_cosine'. Must be a positive integer.
-        :vartype warmup_cosine_lr_warmup_epochs: int
-        :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
-         a float in the range[0, 1].
-        :vartype weight_decay: float
+    :ivar advanced_settings: Settings for advanced scenarios.
+    :vartype advanced_settings: str
+    :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+    :vartype ams_gradient: bool
+    :ivar augmentations: Settings for using Augmentations.
+    :vartype augmentations: str
+    :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta1: float
+    :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta2: float
+    :ivar checkpoint_frequency: Frequency to store model checkpoints. Must be a positive integer.
+    :vartype checkpoint_frequency: int
+    :ivar checkpoint_model: The pretrained checkpoint model for incremental training.
+    :vartype checkpoint_model: ~azure.mgmt.machinelearningservices.models.MLFlowModelJobInput
+    :ivar checkpoint_run_id: The id of a previous run that has a pretrained checkpoint for
+     incremental training.
+    :vartype checkpoint_run_id: str
+    :ivar distributed: Whether to use distributed training.
+    :vartype distributed: bool
+    :ivar early_stopping: Enable early stopping logic during training.
+    :vartype early_stopping: bool
+    :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
+     primary metric improvement
+     is tracked for early stopping. Must be a positive integer.
+    :vartype early_stopping_delay: int
+    :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
+     primary metric improvement before
+     the run is stopped. Must be a positive integer.
+    :vartype early_stopping_patience: int
+    :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
+    :vartype enable_onnx_normalization: bool
+    :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
+     be a positive integer.
+    :vartype evaluation_frequency: int
+    :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
+     "GradAccumulationStep" steps without
+     updating the model weights while accumulating the gradients of those steps, and then using
+     the accumulated gradients to compute the weight updates. Must be a positive integer.
+    :vartype gradient_accumulation_step: int
+    :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
+     For instance, passing 2 as value for 'seresnext' means
+     freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+     please
+     see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype layers_to_freeze: int
+    :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+    :vartype learning_rate: float
+    :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+     'step'. Known values are: "None", "WarmupCosine", and "Step".
+    :vartype learning_rate_scheduler: str or
+     ~azure.mgmt.machinelearningservices.models.LearningRateScheduler
+    :ivar model_name: Name of the model to use for training.
+     For more information on the available models please visit the official documentation:
+     https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype model_name: str
+    :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
+    :vartype momentum: float
+    :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
+    :vartype nesterov: bool
+    :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
+    :vartype number_of_epochs: int
+    :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
+    :vartype number_of_workers: int
+    :ivar optimizer: Type of optimizer. Known values are: "None", "Sgd", "Adam", and "Adamw".
+    :vartype optimizer: str or ~azure.mgmt.machinelearningservices.models.StochasticOptimizer
+    :ivar random_seed: Random seed to be used when using deterministic training.
+    :vartype random_seed: int
+    :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
+     the range [0, 1].
+    :vartype step_lr_gamma: float
+    :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
+     positive integer.
+    :vartype step_lr_step_size: int
+    :ivar training_batch_size: Training batch size. Must be a positive integer.
+    :vartype training_batch_size: int
+    :ivar validation_batch_size: Validation batch size. Must be a positive integer.
+    :vartype validation_batch_size: int
+    :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+     'warmup_cosine'. Must be a float in the range [0, 1].
+    :vartype warmup_cosine_lr_cycles: float
+    :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+     'warmup_cosine'. Must be a positive integer.
+    :vartype warmup_cosine_lr_warmup_epochs: int
+    :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
+     a float in the range[0, 1].
+    :vartype weight_decay: float
     """
 
     _attribute_map = {
@@ -11432,8 +12914,8 @@ def __init__(  # pylint: disable=too-many-locals
         warmup_cosine_lr_cycles: Optional[float] = None,
         warmup_cosine_lr_warmup_epochs: Optional[int] = None,
         weight_decay: Optional[float] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword advanced_settings: Settings for advanced scenarios.
         :paramtype advanced_settings: str
@@ -11567,107 +13049,107 @@ class ImageModelSettingsClassification(ImageModelSettings):  # pylint: disable=t
     For more information on the available settings please visit the official documentation:
     https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
 
-        :ivar advanced_settings: Settings for advanced scenarios.
-        :vartype advanced_settings: str
-        :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
-        :vartype ams_gradient: bool
-        :ivar augmentations: Settings for using Augmentations.
-        :vartype augmentations: str
-        :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
-         [0, 1].
-        :vartype beta1: float
-        :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
-         [0, 1].
-        :vartype beta2: float
-        :ivar checkpoint_frequency: Frequency to store model checkpoints. Must be a positive integer.
-        :vartype checkpoint_frequency: int
-        :ivar checkpoint_model: The pretrained checkpoint model for incremental training.
-        :vartype checkpoint_model: ~azure.mgmt.machinelearningservices.models.MLFlowModelJobInput
-        :ivar checkpoint_run_id: The id of a previous run that has a pretrained checkpoint for
-         incremental training.
-        :vartype checkpoint_run_id: str
-        :ivar distributed: Whether to use distributed training.
-        :vartype distributed: bool
-        :ivar early_stopping: Enable early stopping logic during training.
-        :vartype early_stopping: bool
-        :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
-         primary metric improvement
-         is tracked for early stopping. Must be a positive integer.
-        :vartype early_stopping_delay: int
-        :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
-         primary metric improvement before
-         the run is stopped. Must be a positive integer.
-        :vartype early_stopping_patience: int
-        :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
-        :vartype enable_onnx_normalization: bool
-        :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
-         be a positive integer.
-        :vartype evaluation_frequency: int
-        :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
-         "GradAccumulationStep" steps without
-         updating the model weights while accumulating the gradients of those steps, and then using
-         the accumulated gradients to compute the weight updates. Must be a positive integer.
-        :vartype gradient_accumulation_step: int
-        :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
-         For instance, passing 2 as value for 'seresnext' means
-         freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
-         please
-         see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
-        :vartype layers_to_freeze: int
-        :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
-        :vartype learning_rate: float
-        :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
-         'step'. Known values are: "None", "WarmupCosine", and "Step".
-        :vartype learning_rate_scheduler: str or
-         ~azure.mgmt.machinelearningservices.models.LearningRateScheduler
-        :ivar model_name: Name of the model to use for training.
-         For more information on the available models please visit the official documentation:
-         https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
-        :vartype model_name: str
-        :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
-        :vartype momentum: float
-        :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
-        :vartype nesterov: bool
-        :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
-        :vartype number_of_epochs: int
-        :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
-        :vartype number_of_workers: int
-        :ivar optimizer: Type of optimizer. Known values are: "None", "Sgd", "Adam", and "Adamw".
-        :vartype optimizer: str or ~azure.mgmt.machinelearningservices.models.StochasticOptimizer
-        :ivar random_seed: Random seed to be used when using deterministic training.
-        :vartype random_seed: int
-        :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
-         the range [0, 1].
-        :vartype step_lr_gamma: float
-        :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
-         positive integer.
-        :vartype step_lr_step_size: int
-        :ivar training_batch_size: Training batch size. Must be a positive integer.
-        :vartype training_batch_size: int
-        :ivar validation_batch_size: Validation batch size. Must be a positive integer.
-        :vartype validation_batch_size: int
-        :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
-         'warmup_cosine'. Must be a float in the range [0, 1].
-        :vartype warmup_cosine_lr_cycles: float
-        :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
-         'warmup_cosine'. Must be a positive integer.
-        :vartype warmup_cosine_lr_warmup_epochs: int
-        :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
-         a float in the range[0, 1].
-        :vartype weight_decay: float
-        :ivar training_crop_size: Image crop size that is input to the neural network for the training
-         dataset. Must be a positive integer.
-        :vartype training_crop_size: int
-        :ivar validation_crop_size: Image crop size that is input to the neural network for the
-         validation dataset. Must be a positive integer.
-        :vartype validation_crop_size: int
-        :ivar validation_resize_size: Image size to which to resize before cropping for validation
-         dataset. Must be a positive integer.
-        :vartype validation_resize_size: int
-        :ivar weighted_loss: Weighted loss. The accepted values are 0 for no weighted loss.
-         1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be
-         0 or 1 or 2.
-        :vartype weighted_loss: int
+    :ivar advanced_settings: Settings for advanced scenarios.
+    :vartype advanced_settings: str
+    :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+    :vartype ams_gradient: bool
+    :ivar augmentations: Settings for using Augmentations.
+    :vartype augmentations: str
+    :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta1: float
+    :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta2: float
+    :ivar checkpoint_frequency: Frequency to store model checkpoints. Must be a positive integer.
+    :vartype checkpoint_frequency: int
+    :ivar checkpoint_model: The pretrained checkpoint model for incremental training.
+    :vartype checkpoint_model: ~azure.mgmt.machinelearningservices.models.MLFlowModelJobInput
+    :ivar checkpoint_run_id: The id of a previous run that has a pretrained checkpoint for
+     incremental training.
+    :vartype checkpoint_run_id: str
+    :ivar distributed: Whether to use distributed training.
+    :vartype distributed: bool
+    :ivar early_stopping: Enable early stopping logic during training.
+    :vartype early_stopping: bool
+    :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
+     primary metric improvement
+     is tracked for early stopping. Must be a positive integer.
+    :vartype early_stopping_delay: int
+    :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
+     primary metric improvement before
+     the run is stopped. Must be a positive integer.
+    :vartype early_stopping_patience: int
+    :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
+    :vartype enable_onnx_normalization: bool
+    :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
+     be a positive integer.
+    :vartype evaluation_frequency: int
+    :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
+     "GradAccumulationStep" steps without
+     updating the model weights while accumulating the gradients of those steps, and then using
+     the accumulated gradients to compute the weight updates. Must be a positive integer.
+    :vartype gradient_accumulation_step: int
+    :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
+     For instance, passing 2 as value for 'seresnext' means
+     freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+     please
+     see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype layers_to_freeze: int
+    :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+    :vartype learning_rate: float
+    :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+     'step'. Known values are: "None", "WarmupCosine", and "Step".
+    :vartype learning_rate_scheduler: str or
+     ~azure.mgmt.machinelearningservices.models.LearningRateScheduler
+    :ivar model_name: Name of the model to use for training.
+     For more information on the available models please visit the official documentation:
+     https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype model_name: str
+    :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
+    :vartype momentum: float
+    :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
+    :vartype nesterov: bool
+    :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
+    :vartype number_of_epochs: int
+    :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
+    :vartype number_of_workers: int
+    :ivar optimizer: Type of optimizer. Known values are: "None", "Sgd", "Adam", and "Adamw".
+    :vartype optimizer: str or ~azure.mgmt.machinelearningservices.models.StochasticOptimizer
+    :ivar random_seed: Random seed to be used when using deterministic training.
+    :vartype random_seed: int
+    :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
+     the range [0, 1].
+    :vartype step_lr_gamma: float
+    :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
+     positive integer.
+    :vartype step_lr_step_size: int
+    :ivar training_batch_size: Training batch size. Must be a positive integer.
+    :vartype training_batch_size: int
+    :ivar validation_batch_size: Validation batch size. Must be a positive integer.
+    :vartype validation_batch_size: int
+    :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+     'warmup_cosine'. Must be a float in the range [0, 1].
+    :vartype warmup_cosine_lr_cycles: float
+    :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+     'warmup_cosine'. Must be a positive integer.
+    :vartype warmup_cosine_lr_warmup_epochs: int
+    :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
+     a float in the range[0, 1].
+    :vartype weight_decay: float
+    :ivar training_crop_size: Image crop size that is input to the neural network for the training
+     dataset. Must be a positive integer.
+    :vartype training_crop_size: int
+    :ivar validation_crop_size: Image crop size that is input to the neural network for the
+     validation dataset. Must be a positive integer.
+    :vartype validation_crop_size: int
+    :ivar validation_resize_size: Image size to which to resize before cropping for validation
+     dataset. Must be a positive integer.
+    :vartype validation_resize_size: int
+    :ivar weighted_loss: Weighted loss. The accepted values are 0 for no weighted loss.
+     1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be
+     0 or 1 or 2.
+    :vartype weighted_loss: int
     """
 
     _attribute_map = {
@@ -11748,8 +13230,8 @@ def __init__(  # pylint: disable=too-many-locals
         validation_crop_size: Optional[int] = None,
         validation_resize_size: Optional[int] = None,
         weighted_loss: Optional[int] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword advanced_settings: Settings for advanced scenarios.
         :paramtype advanced_settings: str
@@ -11902,147 +13384,147 @@ class ImageModelSettingsObjectDetection(ImageModelSettings):  # pylint: disable=
     For more information on the available settings please visit the official documentation:
     https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
 
-        :ivar advanced_settings: Settings for advanced scenarios.
-        :vartype advanced_settings: str
-        :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
-        :vartype ams_gradient: bool
-        :ivar augmentations: Settings for using Augmentations.
-        :vartype augmentations: str
-        :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
-         [0, 1].
-        :vartype beta1: float
-        :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
-         [0, 1].
-        :vartype beta2: float
-        :ivar checkpoint_frequency: Frequency to store model checkpoints. Must be a positive integer.
-        :vartype checkpoint_frequency: int
-        :ivar checkpoint_model: The pretrained checkpoint model for incremental training.
-        :vartype checkpoint_model: ~azure.mgmt.machinelearningservices.models.MLFlowModelJobInput
-        :ivar checkpoint_run_id: The id of a previous run that has a pretrained checkpoint for
-         incremental training.
-        :vartype checkpoint_run_id: str
-        :ivar distributed: Whether to use distributed training.
-        :vartype distributed: bool
-        :ivar early_stopping: Enable early stopping logic during training.
-        :vartype early_stopping: bool
-        :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
-         primary metric improvement
-         is tracked for early stopping. Must be a positive integer.
-        :vartype early_stopping_delay: int
-        :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
-         primary metric improvement before
-         the run is stopped. Must be a positive integer.
-        :vartype early_stopping_patience: int
-        :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
-        :vartype enable_onnx_normalization: bool
-        :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
-         be a positive integer.
-        :vartype evaluation_frequency: int
-        :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
-         "GradAccumulationStep" steps without
-         updating the model weights while accumulating the gradients of those steps, and then using
-         the accumulated gradients to compute the weight updates. Must be a positive integer.
-        :vartype gradient_accumulation_step: int
-        :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
-         For instance, passing 2 as value for 'seresnext' means
-         freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
-         please
-         see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
-        :vartype layers_to_freeze: int
-        :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
-        :vartype learning_rate: float
-        :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
-         'step'. Known values are: "None", "WarmupCosine", and "Step".
-        :vartype learning_rate_scheduler: str or
-         ~azure.mgmt.machinelearningservices.models.LearningRateScheduler
-        :ivar model_name: Name of the model to use for training.
-         For more information on the available models please visit the official documentation:
-         https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
-        :vartype model_name: str
-        :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
-        :vartype momentum: float
-        :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
-        :vartype nesterov: bool
-        :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
-        :vartype number_of_epochs: int
-        :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
-        :vartype number_of_workers: int
-        :ivar optimizer: Type of optimizer. Known values are: "None", "Sgd", "Adam", and "Adamw".
-        :vartype optimizer: str or ~azure.mgmt.machinelearningservices.models.StochasticOptimizer
-        :ivar random_seed: Random seed to be used when using deterministic training.
-        :vartype random_seed: int
-        :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
-         the range [0, 1].
-        :vartype step_lr_gamma: float
-        :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
-         positive integer.
-        :vartype step_lr_step_size: int
-        :ivar training_batch_size: Training batch size. Must be a positive integer.
-        :vartype training_batch_size: int
-        :ivar validation_batch_size: Validation batch size. Must be a positive integer.
-        :vartype validation_batch_size: int
-        :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
-         'warmup_cosine'. Must be a float in the range [0, 1].
-        :vartype warmup_cosine_lr_cycles: float
-        :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
-         'warmup_cosine'. Must be a positive integer.
-        :vartype warmup_cosine_lr_warmup_epochs: int
-        :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
-         a float in the range[0, 1].
-        :vartype weight_decay: float
-        :ivar box_detections_per_image: Maximum number of detections per image, for all classes. Must
-         be a positive integer.
-         Note: This settings is not supported for the 'yolov5' algorithm.
-        :vartype box_detections_per_image: int
-        :ivar box_score_threshold: During inference, only return proposals with a classification score
-         greater than
-         BoxScoreThreshold. Must be a float in the range[0, 1].
-        :vartype box_score_threshold: float
-        :ivar image_size: Image size for train and validation. Must be a positive integer.
-         Note: The training run may get into CUDA OOM if the size is too big.
-         Note: This settings is only supported for the 'yolov5' algorithm.
-        :vartype image_size: int
-        :ivar max_size: Maximum size of the image to be rescaled before feeding it to the backbone.
-         Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
-         Note: This settings is not supported for the 'yolov5' algorithm.
-        :vartype max_size: int
-        :ivar min_size: Minimum size of the image to be rescaled before feeding it to the backbone.
-         Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
-         Note: This settings is not supported for the 'yolov5' algorithm.
-        :vartype min_size: int
-        :ivar model_size: Model size. Must be 'small', 'medium', 'large', or 'xlarge'.
-         Note: training run may get into CUDA OOM if the model size is too big.
-         Note: This settings is only supported for the 'yolov5' algorithm. Known values are: "None",
-         "Small", "Medium", "Large", and "ExtraLarge".
-        :vartype model_size: str or ~azure.mgmt.machinelearningservices.models.ModelSize
-        :ivar multi_scale: Enable multi-scale image by varying image size by +/- 50%.
-         Note: training run may get into CUDA OOM if no sufficient GPU memory.
-         Note: This settings is only supported for the 'yolov5' algorithm.
-        :vartype multi_scale: bool
-        :ivar nms_iou_threshold: IOU threshold used during inference in NMS post processing. Must be a
-         float in the range [0, 1].
-        :vartype nms_iou_threshold: float
-        :ivar tile_grid_size: The grid size to use for tiling each image. Note: TileGridSize must not
-         be
-         None to enable small object detection logic. A string containing two integers in mxn format.
-         Note: This settings is not supported for the 'yolov5' algorithm.
-        :vartype tile_grid_size: str
-        :ivar tile_overlap_ratio: Overlap ratio between adjacent tiles in each dimension. Must be float
-         in the range [0, 1).
-         Note: This settings is not supported for the 'yolov5' algorithm.
-        :vartype tile_overlap_ratio: float
-        :ivar tile_predictions_nms_threshold: The IOU threshold to use to perform NMS while merging
-         predictions from tiles and image.
-         Used in validation/ inference. Must be float in the range [0, 1].
-         Note: This settings is not supported for the 'yolov5' algorithm.
-        :vartype tile_predictions_nms_threshold: float
-        :ivar validation_iou_threshold: IOU threshold to use when computing validation metric. Must be
-         float in the range [0, 1].
-        :vartype validation_iou_threshold: float
-        :ivar validation_metric_type: Metric computation method to use for validation metrics. Known
-         values are: "None", "Coco", "Voc", and "CocoVoc".
-        :vartype validation_metric_type: str or
-         ~azure.mgmt.machinelearningservices.models.ValidationMetricType
+    :ivar advanced_settings: Settings for advanced scenarios.
+    :vartype advanced_settings: str
+    :ivar ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+    :vartype ams_gradient: bool
+    :ivar augmentations: Settings for using Augmentations.
+    :vartype augmentations: str
+    :ivar beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta1: float
+    :ivar beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :vartype beta2: float
+    :ivar checkpoint_frequency: Frequency to store model checkpoints. Must be a positive integer.
+    :vartype checkpoint_frequency: int
+    :ivar checkpoint_model: The pretrained checkpoint model for incremental training.
+    :vartype checkpoint_model: ~azure.mgmt.machinelearningservices.models.MLFlowModelJobInput
+    :ivar checkpoint_run_id: The id of a previous run that has a pretrained checkpoint for
+     incremental training.
+    :vartype checkpoint_run_id: str
+    :ivar distributed: Whether to use distributed training.
+    :vartype distributed: bool
+    :ivar early_stopping: Enable early stopping logic during training.
+    :vartype early_stopping: bool
+    :ivar early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
+     primary metric improvement
+     is tracked for early stopping. Must be a positive integer.
+    :vartype early_stopping_delay: int
+    :ivar early_stopping_patience: Minimum number of epochs or validation evaluations with no
+     primary metric improvement before
+     the run is stopped. Must be a positive integer.
+    :vartype early_stopping_patience: int
+    :ivar enable_onnx_normalization: Enable normalization when exporting ONNX model.
+    :vartype enable_onnx_normalization: bool
+    :ivar evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
+     be a positive integer.
+    :vartype evaluation_frequency: int
+    :ivar gradient_accumulation_step: Gradient accumulation means running a configured number of
+     "GradAccumulationStep" steps without
+     updating the model weights while accumulating the gradients of those steps, and then using
+     the accumulated gradients to compute the weight updates. Must be a positive integer.
+    :vartype gradient_accumulation_step: int
+    :ivar layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
+     For instance, passing 2 as value for 'seresnext' means
+     freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+     please
+     see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype layers_to_freeze: int
+    :ivar learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+    :vartype learning_rate: float
+    :ivar learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+     'step'. Known values are: "None", "WarmupCosine", and "Step".
+    :vartype learning_rate_scheduler: str or
+     ~azure.mgmt.machinelearningservices.models.LearningRateScheduler
+    :ivar model_name: Name of the model to use for training.
+     For more information on the available models please visit the official documentation:
+     https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
+    :vartype model_name: str
+    :ivar momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
+    :vartype momentum: float
+    :ivar nesterov: Enable nesterov when optimizer is 'sgd'.
+    :vartype nesterov: bool
+    :ivar number_of_epochs: Number of training epochs. Must be a positive integer.
+    :vartype number_of_epochs: int
+    :ivar number_of_workers: Number of data loader workers. Must be a non-negative integer.
+    :vartype number_of_workers: int
+    :ivar optimizer: Type of optimizer. Known values are: "None", "Sgd", "Adam", and "Adamw".
+    :vartype optimizer: str or ~azure.mgmt.machinelearningservices.models.StochasticOptimizer
+    :ivar random_seed: Random seed to be used when using deterministic training.
+    :vartype random_seed: int
+    :ivar step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
+     the range [0, 1].
+    :vartype step_lr_gamma: float
+    :ivar step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
+     positive integer.
+    :vartype step_lr_step_size: int
+    :ivar training_batch_size: Training batch size. Must be a positive integer.
+    :vartype training_batch_size: int
+    :ivar validation_batch_size: Validation batch size. Must be a positive integer.
+    :vartype validation_batch_size: int
+    :ivar warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+     'warmup_cosine'. Must be a float in the range [0, 1].
+    :vartype warmup_cosine_lr_cycles: float
+    :ivar warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+     'warmup_cosine'. Must be a positive integer.
+    :vartype warmup_cosine_lr_warmup_epochs: int
+    :ivar weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
+     a float in the range[0, 1].
+    :vartype weight_decay: float
+    :ivar box_detections_per_image: Maximum number of detections per image, for all classes. Must
+     be a positive integer.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype box_detections_per_image: int
+    :ivar box_score_threshold: During inference, only return proposals with a classification score
+     greater than
+     BoxScoreThreshold. Must be a float in the range[0, 1].
+    :vartype box_score_threshold: float
+    :ivar image_size: Image size for train and validation. Must be a positive integer.
+     Note: The training run may get into CUDA OOM if the size is too big.
+     Note: This settings is only supported for the 'yolov5' algorithm.
+    :vartype image_size: int
+    :ivar max_size: Maximum size of the image to be rescaled before feeding it to the backbone.
+     Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype max_size: int
+    :ivar min_size: Minimum size of the image to be rescaled before feeding it to the backbone.
+     Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype min_size: int
+    :ivar model_size: Model size. Must be 'small', 'medium', 'large', or 'xlarge'.
+     Note: training run may get into CUDA OOM if the model size is too big.
+     Note: This settings is only supported for the 'yolov5' algorithm. Known values are: "None",
+     "Small", "Medium", "Large", and "ExtraLarge".
+    :vartype model_size: str or ~azure.mgmt.machinelearningservices.models.ModelSize
+    :ivar multi_scale: Enable multi-scale image by varying image size by +/- 50%.
+     Note: training run may get into CUDA OOM if no sufficient GPU memory.
+     Note: This settings is only supported for the 'yolov5' algorithm.
+    :vartype multi_scale: bool
+    :ivar nms_iou_threshold: IOU threshold used during inference in NMS post processing. Must be a
+     float in the range [0, 1].
+    :vartype nms_iou_threshold: float
+    :ivar tile_grid_size: The grid size to use for tiling each image. Note: TileGridSize must not
+     be
+     None to enable small object detection logic. A string containing two integers in mxn format.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype tile_grid_size: str
+    :ivar tile_overlap_ratio: Overlap ratio between adjacent tiles in each dimension. Must be float
+     in the range [0, 1).
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype tile_overlap_ratio: float
+    :ivar tile_predictions_nms_threshold: The IOU threshold to use to perform NMS while merging
+     predictions from tiles and image.
+     Used in validation/ inference. Must be float in the range [0, 1].
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :vartype tile_predictions_nms_threshold: float
+    :ivar validation_iou_threshold: IOU threshold to use when computing validation metric. Must be
+     float in the range [0, 1].
+    :vartype validation_iou_threshold: float
+    :ivar validation_metric_type: Metric computation method to use for validation metrics. Known
+     values are: "None", "Coco", "Voc", and "CocoVoc".
+    :vartype validation_metric_type: str or
+     ~azure.mgmt.machinelearningservices.models.ValidationMetricType
     """
 
     _attribute_map = {
@@ -12141,8 +13623,8 @@ def __init__(  # pylint: disable=too-many-locals
         tile_predictions_nms_threshold: Optional[float] = None,
         validation_iou_threshold: Optional[float] = None,
         validation_metric_type: Optional[Union[str, "_models.ValidationMetricType"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword advanced_settings: Settings for advanced scenarios.
         :paramtype advanced_settings: str
@@ -12340,45 +13822,46 @@ def __init__(  # pylint: disable=too-many-locals
 
 
 class ImageObjectDetection(ImageObjectDetectionBase, AutoMLVertical):  # pylint: disable=too-many-instance-attributes
-    """Image Object Detection. Object detection is used to identify objects in an image and locate each object with a
+    """Image Object Detection. Object detection is used to identify objects in an image and locate
+    each object with a
     bounding box e.g. locate all dogs and cats in an image and draw a bounding box around each.
 
-        All required parameters must be populated in order to send to Azure.
+    All required parameters must be populated in order to send to Azure.
 
-        :ivar log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
-         "Warning", "Error", and "Critical".
-        :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
-        :ivar target_column_name: Target column name: This is prediction values column.
-         Also known as label column name in context of classification tasks.
-        :vartype target_column_name: str
-        :ivar task_type: [Required] Task type for AutoMLJob. Required. Known values are:
-         "Classification", "Regression", "Forecasting", "ImageClassification",
-         "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
-         "TextClassification", "TextClassificationMultilabel", and "TextNER".
-        :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
-        :ivar training_data: [Required] Training data input. Required.
-        :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
-        :ivar limit_settings: [Required] Limit settings for the AutoML job. Required.
-        :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
-        :ivar sweep_settings: Model sweeping and hyperparameter sweeping related settings.
-        :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
-        :ivar validation_data: Validation data inputs.
-        :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
-        :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
-         validation purpose.
-         Values between (0.0 , 1.0)
-         Applied when validation dataset is not provided.
-        :vartype validation_data_size: float
-        :ivar model_settings: Settings used for training the model.
-        :vartype model_settings:
-         ~azure.mgmt.machinelearningservices.models.ImageModelSettingsObjectDetection
-        :ivar search_space: Search space for sampling different combinations of models and their
-         hyperparameters.
-        :vartype search_space:
-         list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsObjectDetection]
-        :ivar primary_metric: Primary metric to optimize for this task. "MeanAveragePrecision"
-        :vartype primary_metric: str or
-         ~azure.mgmt.machinelearningservices.models.ObjectDetectionPrimaryMetrics
+    :ivar log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
+     "Warning", "Error", and "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: [Required] Task type for AutoMLJob. Required. Known values are:
+     "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", and "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: [Required] Training data input. Required.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar limit_settings: [Required] Limit settings for the AutoML job. Required.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.ImageLimitSettings
+    :ivar sweep_settings: Model sweeping and hyperparameter sweeping related settings.
+    :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.ImageSweepSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar validation_data_size: The fraction of training dataset that needs to be set aside for
+     validation purpose.
+     Values between (0.0 , 1.0)
+     Applied when validation dataset is not provided.
+    :vartype validation_data_size: float
+    :ivar model_settings: Settings used for training the model.
+    :vartype model_settings:
+     ~azure.mgmt.machinelearningservices.models.ImageModelSettingsObjectDetection
+    :ivar search_space: Search space for sampling different combinations of models and their
+     hyperparameters.
+    :vartype search_space:
+     list[~azure.mgmt.machinelearningservices.models.ImageModelDistributionSettingsObjectDetection]
+    :ivar primary_metric: Primary metric to optimize for this task. "MeanAveragePrecision"
+    :vartype primary_metric: str or
+     ~azure.mgmt.machinelearningservices.models.ObjectDetectionPrimaryMetrics
     """
 
     _validation = {
@@ -12414,8 +13897,8 @@ def __init__(
         model_settings: Optional["_models.ImageModelSettingsObjectDetection"] = None,
         search_space: Optional[List["_models.ImageModelDistributionSettingsObjectDetection"]] = None,
         primary_metric: Optional[Union[str, "_models.ObjectDetectionPrimaryMetrics"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
          "Warning", "Error", and "Critical".
@@ -12499,8 +13982,8 @@ def __init__(
         *,
         sampling_algorithm: Union[str, "_models.SamplingAlgorithmType"],
         early_termination: Optional["_models.EarlyTerminationPolicy"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword early_termination: Type of early termination policy.
         :paramtype early_termination: ~azure.mgmt.machinelearningservices.models.EarlyTerminationPolicy
@@ -12538,8 +14021,8 @@ def __init__(
         liveness_route: Optional["_models.Route"] = None,
         readiness_route: Optional["_models.Route"] = None,
         scoring_route: Optional["_models.Route"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword liveness_route: The route to check the liveness of the inference server container.
         :paramtype liveness_route: ~azure.mgmt.machinelearningservices.models.Route
@@ -12574,8 +14057,8 @@ def __init__(
         *,
         node_selector: Optional[Dict[str, str]] = None,
         resources: Optional["_models.InstanceTypeSchemaResources"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword node_selector: Node Selector.
         :paramtype node_selector: dict[str, str]
@@ -12601,7 +14084,9 @@ class InstanceTypeSchemaResources(_serialization.Model):
         "limits": {"key": "limits", "type": "{str}"},
     }
 
-    def __init__(self, *, requests: Optional[Dict[str, str]] = None, limits: Optional[Dict[str, str]] = None, **kwargs):
+    def __init__(
+        self, *, requests: Optional[Dict[str, str]] = None, limits: Optional[Dict[str, str]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword requests: Resource requests for this instance type.
         :paramtype requests: dict[str, str]
@@ -12651,7 +14136,7 @@ class JobBase(Resource):
         "properties": {"key": "properties", "type": "JobBaseProperties"},
     }
 
-    def __init__(self, *, properties: "_models.JobBaseProperties", **kwargs):
+    def __init__(self, *, properties: "_models.JobBaseProperties", **kwargs: Any) -> None:
         """
         :keyword properties: [Required] Additional attributes of the entity. Required.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.JobBaseProperties
@@ -12675,7 +14160,9 @@ class JobBaseResourceArmPaginatedResult(_serialization.Model):
         "value": {"key": "value", "type": "[JobBase]"},
     }
 
-    def __init__(self, *, next_link: Optional[str] = None, value: Optional[List["_models.JobBase"]] = None, **kwargs):
+    def __init__(
+        self, *, next_link: Optional[str] = None, value: Optional[List["_models.JobBase"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of JobBase objects. If null, there are no
          additional pages.
@@ -12727,8 +14214,8 @@ def __init__(
         properties: Optional[Dict[str, JSON]] = None,
         docker_args: Optional[str] = None,
         shm_size: str = "2g",
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword instance_count: Optional number of instances or nodes used by the compute target.
         :paramtype instance_count: int
@@ -12772,7 +14259,7 @@ class JobScheduleAction(ScheduleActionBase):
         "job_definition": {"key": "jobDefinition", "type": "JobBaseProperties"},
     }
 
-    def __init__(self, *, job_definition: "_models.JobBaseProperties", **kwargs):
+    def __init__(self, *, job_definition: "_models.JobBaseProperties", **kwargs: Any) -> None:
         """
         :keyword job_definition: [Required] Defines Schedule action definition details. Required.
         :paramtype job_definition: ~azure.mgmt.machinelearningservices.models.JobBaseProperties
@@ -12793,7 +14280,10 @@ class JobService(_serialization.Model):
     :vartype error_message: str
     :ivar job_service_type: Endpoint type.
     :vartype job_service_type: str
-    :ivar port: Port for endpoint.
+    :ivar nodes: Nodes that user would like to start the service on.
+     If Nodes is not set or set to null, the service will only be started on leader node.
+    :vartype nodes: ~azure.mgmt.machinelearningservices.models.Nodes
+    :ivar port: Port for endpoint set by user.
     :vartype port: int
     :ivar properties: Additional properties to set on the endpoint.
     :vartype properties: dict[str, str]
@@ -12810,6 +14300,7 @@ class JobService(_serialization.Model):
         "endpoint": {"key": "endpoint", "type": "str"},
         "error_message": {"key": "errorMessage", "type": "str"},
         "job_service_type": {"key": "jobServiceType", "type": "str"},
+        "nodes": {"key": "nodes", "type": "Nodes"},
         "port": {"key": "port", "type": "int"},
         "properties": {"key": "properties", "type": "{str}"},
         "status": {"key": "status", "type": "str"},
@@ -12820,16 +14311,20 @@ def __init__(
         *,
         endpoint: Optional[str] = None,
         job_service_type: Optional[str] = None,
+        nodes: Optional["_models.Nodes"] = None,
         port: Optional[int] = None,
         properties: Optional[Dict[str, str]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword endpoint: Url for endpoint.
         :paramtype endpoint: str
         :keyword job_service_type: Endpoint type.
         :paramtype job_service_type: str
-        :keyword port: Port for endpoint.
+        :keyword nodes: Nodes that user would like to start the service on.
+         If Nodes is not set or set to null, the service will only be started on leader node.
+        :paramtype nodes: ~azure.mgmt.machinelearningservices.models.Nodes
+        :keyword port: Port for endpoint set by user.
         :paramtype port: int
         :keyword properties: Additional properties to set on the endpoint.
         :paramtype properties: dict[str, str]
@@ -12838,131 +14333,378 @@ def __init__(
         self.endpoint = endpoint
         self.error_message = None
         self.job_service_type = job_service_type
+        self.nodes = nodes
         self.port = port
         self.properties = properties
         self.status = None
 
 
-class KubernetesSchema(_serialization.Model):
-    """Kubernetes Compute Schema.
+class KerberosCredentials(_serialization.Model):
+    """KerberosCredentials.
 
-    :ivar properties: Properties of Kubernetes.
-    :vartype properties: ~azure.mgmt.machinelearningservices.models.KubernetesProperties
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar kerberos_kdc_address: [Required] IP Address or DNS HostName. Required.
+    :vartype kerberos_kdc_address: str
+    :ivar kerberos_principal: [Required] Kerberos Username. Required.
+    :vartype kerberos_principal: str
+    :ivar kerberos_realm: [Required] Domain over which a Kerberos authentication server has the
+     authority to authenticate a user, host or service. Required.
+    :vartype kerberos_realm: str
     """
 
+    _validation = {
+        "kerberos_kdc_address": {"required": True, "pattern": r"[a-zA-Z0-9_]"},
+        "kerberos_principal": {"required": True, "pattern": r"[a-zA-Z0-9_]"},
+        "kerberos_realm": {"required": True, "pattern": r"[a-zA-Z0-9_]"},
+    }
+
     _attribute_map = {
-        "properties": {"key": "properties", "type": "KubernetesProperties"},
+        "kerberos_kdc_address": {"key": "kerberosKdcAddress", "type": "str"},
+        "kerberos_principal": {"key": "kerberosPrincipal", "type": "str"},
+        "kerberos_realm": {"key": "kerberosRealm", "type": "str"},
     }
 
-    def __init__(self, *, properties: Optional["_models.KubernetesProperties"] = None, **kwargs):
+    def __init__(
+        self, *, kerberos_kdc_address: str, kerberos_principal: str, kerberos_realm: str, **kwargs: Any
+    ) -> None:
         """
-        :keyword properties: Properties of Kubernetes.
-        :paramtype properties: ~azure.mgmt.machinelearningservices.models.KubernetesProperties
+        :keyword kerberos_kdc_address: [Required] IP Address or DNS HostName. Required.
+        :paramtype kerberos_kdc_address: str
+        :keyword kerberos_principal: [Required] Kerberos Username. Required.
+        :paramtype kerberos_principal: str
+        :keyword kerberos_realm: [Required] Domain over which a Kerberos authentication server has the
+         authority to authenticate a user, host or service. Required.
+        :paramtype kerberos_realm: str
         """
         super().__init__(**kwargs)
-        self.properties = properties
+        self.kerberos_kdc_address = kerberos_kdc_address
+        self.kerberos_principal = kerberos_principal
+        self.kerberos_realm = kerberos_realm
 
 
-class Kubernetes(Compute, KubernetesSchema):  # pylint: disable=too-many-instance-attributes
-    """A Machine Learning compute based on Kubernetes Compute.
-
-    Variables are only populated by the server, and will be ignored when sending a request.
+class KerberosKeytabCredentials(KerberosCredentials, DatastoreCredentials):
+    """KerberosKeytabCredentials.
 
     All required parameters must be populated in order to send to Azure.
 
-    :ivar properties: Properties of Kubernetes.
-    :vartype properties: ~azure.mgmt.machinelearningservices.models.KubernetesProperties
-    :ivar compute_type: The type of compute. Required. Known values are: "AKS", "Kubernetes",
-     "AmlCompute", "ComputeInstance", "DataFactory", "VirtualMachine", "HDInsight", "Databricks",
-     "DataLakeAnalytics", and "SynapseSpark".
-    :vartype compute_type: str or ~azure.mgmt.machinelearningservices.models.ComputeType
-    :ivar compute_location: Location for the underlying compute.
-    :vartype compute_location: str
-    :ivar provisioning_state: The provision state of the cluster. Valid values are Unknown,
-     Updating, Provisioning, Succeeded, and Failed. Known values are: "Unknown", "Updating",
-     "Creating", "Deleting", "Succeeded", "Failed", and "Canceled".
-    :vartype provisioning_state: str or
-     ~azure.mgmt.machinelearningservices.models.ProvisioningState
-    :ivar description: The description of the Machine Learning compute.
-    :vartype description: str
-    :ivar created_on: The time at which the compute was created.
-    :vartype created_on: ~datetime.datetime
-    :ivar modified_on: The time at which the compute was last modified.
-    :vartype modified_on: ~datetime.datetime
-    :ivar resource_id: ARM resource id of the underlying compute.
-    :vartype resource_id: str
-    :ivar provisioning_errors: Errors during provisioning.
-    :vartype provisioning_errors: list[~azure.mgmt.machinelearningservices.models.ErrorResponse]
-    :ivar is_attached_compute: Indicating whether the compute was provisioned by user and brought
-     from outside if true, or machine learning service provisioned it if false.
-    :vartype is_attached_compute: bool
-    :ivar disable_local_auth: Opt-out of local authentication and ensure customers can use only MSI
-     and AAD exclusively for authentication.
-    :vartype disable_local_auth: bool
+    :ivar credentials_type: [Required] Credential type used to authentication with storage.
+     Required. Known values are: "AccountKey", "Certificate", "None", "Sas", "ServicePrincipal",
+     "KerberosKeytab", and "KerberosPassword".
+    :vartype credentials_type: str or ~azure.mgmt.machinelearningservices.models.CredentialsType
+    :ivar kerberos_kdc_address: [Required] IP Address or DNS HostName. Required.
+    :vartype kerberos_kdc_address: str
+    :ivar kerberos_principal: [Required] Kerberos Username. Required.
+    :vartype kerberos_principal: str
+    :ivar kerberos_realm: [Required] Domain over which a Kerberos authentication server has the
+     authority to authenticate a user, host or service. Required.
+    :vartype kerberos_realm: str
+    :ivar secrets: [Required] Keytab secrets. Required.
+    :vartype secrets: ~azure.mgmt.machinelearningservices.models.KerberosKeytabSecrets
     """
 
     _validation = {
-        "compute_type": {"required": True},
-        "provisioning_state": {"readonly": True},
-        "created_on": {"readonly": True},
-        "modified_on": {"readonly": True},
-        "provisioning_errors": {"readonly": True},
-        "is_attached_compute": {"readonly": True},
+        "credentials_type": {"required": True},
+        "kerberos_kdc_address": {"required": True, "pattern": r"[a-zA-Z0-9_]"},
+        "kerberos_principal": {"required": True, "pattern": r"[a-zA-Z0-9_]"},
+        "kerberos_realm": {"required": True, "pattern": r"[a-zA-Z0-9_]"},
+        "secrets": {"required": True},
     }
 
     _attribute_map = {
-        "properties": {"key": "properties", "type": "KubernetesProperties"},
-        "compute_type": {"key": "computeType", "type": "str"},
-        "compute_location": {"key": "computeLocation", "type": "str"},
-        "provisioning_state": {"key": "provisioningState", "type": "str"},
-        "description": {"key": "description", "type": "str"},
-        "created_on": {"key": "createdOn", "type": "iso-8601"},
-        "modified_on": {"key": "modifiedOn", "type": "iso-8601"},
-        "resource_id": {"key": "resourceId", "type": "str"},
-        "provisioning_errors": {"key": "provisioningErrors", "type": "[ErrorResponse]"},
-        "is_attached_compute": {"key": "isAttachedCompute", "type": "bool"},
-        "disable_local_auth": {"key": "disableLocalAuth", "type": "bool"},
+        "credentials_type": {"key": "credentialsType", "type": "str"},
+        "kerberos_kdc_address": {"key": "kerberosKdcAddress", "type": "str"},
+        "kerberos_principal": {"key": "kerberosPrincipal", "type": "str"},
+        "kerberos_realm": {"key": "kerberosRealm", "type": "str"},
+        "secrets": {"key": "secrets", "type": "KerberosKeytabSecrets"},
     }
 
     def __init__(
         self,
         *,
-        properties: Optional["_models.KubernetesProperties"] = None,
-        compute_location: Optional[str] = None,
-        description: Optional[str] = None,
-        resource_id: Optional[str] = None,
-        disable_local_auth: Optional[bool] = None,
-        **kwargs
-    ):
-        """
-        :keyword properties: Properties of Kubernetes.
-        :paramtype properties: ~azure.mgmt.machinelearningservices.models.KubernetesProperties
-        :keyword compute_location: Location for the underlying compute.
-        :paramtype compute_location: str
-        :keyword description: The description of the Machine Learning compute.
-        :paramtype description: str
-        :keyword resource_id: ARM resource id of the underlying compute.
-        :paramtype resource_id: str
-        :keyword disable_local_auth: Opt-out of local authentication and ensure customers can use only
-         MSI and AAD exclusively for authentication.
-        :paramtype disable_local_auth: bool
+        kerberos_kdc_address: str,
+        kerberos_principal: str,
+        kerberos_realm: str,
+        secrets: "_models.KerberosKeytabSecrets",
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword kerberos_kdc_address: [Required] IP Address or DNS HostName. Required.
+        :paramtype kerberos_kdc_address: str
+        :keyword kerberos_principal: [Required] Kerberos Username. Required.
+        :paramtype kerberos_principal: str
+        :keyword kerberos_realm: [Required] Domain over which a Kerberos authentication server has the
+         authority to authenticate a user, host or service. Required.
+        :paramtype kerberos_realm: str
+        :keyword secrets: [Required] Keytab secrets. Required.
+        :paramtype secrets: ~azure.mgmt.machinelearningservices.models.KerberosKeytabSecrets
         """
         super().__init__(
-            compute_location=compute_location,
-            description=description,
-            resource_id=resource_id,
-            disable_local_auth=disable_local_auth,
-            properties=properties,
+            kerberos_kdc_address=kerberos_kdc_address,
+            kerberos_principal=kerberos_principal,
+            kerberos_realm=kerberos_realm,
             **kwargs
         )
-        self.properties = properties
-        self.compute_type: str = "Kubernetes"
-        self.compute_location = compute_location
-        self.provisioning_state = None
-        self.description = description
-        self.created_on = None
-        self.modified_on = None
-        self.resource_id = resource_id
+        self.credentials_type: str = "KerberosKeytab"
+        self.secrets = secrets
+        self.kerberos_kdc_address = kerberos_kdc_address
+        self.kerberos_principal = kerberos_principal
+        self.kerberos_realm = kerberos_realm
+
+
+class KerberosKeytabSecrets(DatastoreSecrets):
+    """KerberosKeytabSecrets.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar secrets_type: [Required] Credential type used to authentication with storage. Required.
+     Known values are: "AccountKey", "Certificate", "Sas", "ServicePrincipal", "KerberosPassword",
+     and "KerberosKeytab".
+    :vartype secrets_type: str or ~azure.mgmt.machinelearningservices.models.SecretsType
+    :ivar kerberos_keytab: Kerberos keytab secret.
+    :vartype kerberos_keytab: str
+    """
+
+    _validation = {
+        "secrets_type": {"required": True},
+    }
+
+    _attribute_map = {
+        "secrets_type": {"key": "secretsType", "type": "str"},
+        "kerberos_keytab": {"key": "kerberosKeytab", "type": "str"},
+    }
+
+    def __init__(self, *, kerberos_keytab: Optional[str] = None, **kwargs: Any) -> None:
+        """
+        :keyword kerberos_keytab: Kerberos keytab secret.
+        :paramtype kerberos_keytab: str
+        """
+        super().__init__(**kwargs)
+        self.secrets_type: str = "KerberosKeytab"
+        self.kerberos_keytab = kerberos_keytab
+
+
+class KerberosPasswordCredentials(KerberosCredentials, DatastoreCredentials):
+    """KerberosPasswordCredentials.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar credentials_type: [Required] Credential type used to authentication with storage.
+     Required. Known values are: "AccountKey", "Certificate", "None", "Sas", "ServicePrincipal",
+     "KerberosKeytab", and "KerberosPassword".
+    :vartype credentials_type: str or ~azure.mgmt.machinelearningservices.models.CredentialsType
+    :ivar kerberos_kdc_address: [Required] IP Address or DNS HostName. Required.
+    :vartype kerberos_kdc_address: str
+    :ivar kerberos_principal: [Required] Kerberos Username. Required.
+    :vartype kerberos_principal: str
+    :ivar kerberos_realm: [Required] Domain over which a Kerberos authentication server has the
+     authority to authenticate a user, host or service. Required.
+    :vartype kerberos_realm: str
+    :ivar secrets: [Required] Kerberos password secrets. Required.
+    :vartype secrets: ~azure.mgmt.machinelearningservices.models.KerberosPasswordSecrets
+    """
+
+    _validation = {
+        "credentials_type": {"required": True},
+        "kerberos_kdc_address": {"required": True, "pattern": r"[a-zA-Z0-9_]"},
+        "kerberos_principal": {"required": True, "pattern": r"[a-zA-Z0-9_]"},
+        "kerberos_realm": {"required": True, "pattern": r"[a-zA-Z0-9_]"},
+        "secrets": {"required": True},
+    }
+
+    _attribute_map = {
+        "credentials_type": {"key": "credentialsType", "type": "str"},
+        "kerberos_kdc_address": {"key": "kerberosKdcAddress", "type": "str"},
+        "kerberos_principal": {"key": "kerberosPrincipal", "type": "str"},
+        "kerberos_realm": {"key": "kerberosRealm", "type": "str"},
+        "secrets": {"key": "secrets", "type": "KerberosPasswordSecrets"},
+    }
+
+    def __init__(
+        self,
+        *,
+        kerberos_kdc_address: str,
+        kerberos_principal: str,
+        kerberos_realm: str,
+        secrets: "_models.KerberosPasswordSecrets",
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword kerberos_kdc_address: [Required] IP Address or DNS HostName. Required.
+        :paramtype kerberos_kdc_address: str
+        :keyword kerberos_principal: [Required] Kerberos Username. Required.
+        :paramtype kerberos_principal: str
+        :keyword kerberos_realm: [Required] Domain over which a Kerberos authentication server has the
+         authority to authenticate a user, host or service. Required.
+        :paramtype kerberos_realm: str
+        :keyword secrets: [Required] Kerberos password secrets. Required.
+        :paramtype secrets: ~azure.mgmt.machinelearningservices.models.KerberosPasswordSecrets
+        """
+        super().__init__(
+            kerberos_kdc_address=kerberos_kdc_address,
+            kerberos_principal=kerberos_principal,
+            kerberos_realm=kerberos_realm,
+            **kwargs
+        )
+        self.credentials_type: str = "KerberosPassword"
+        self.secrets = secrets
+        self.kerberos_kdc_address = kerberos_kdc_address
+        self.kerberos_principal = kerberos_principal
+        self.kerberos_realm = kerberos_realm
+
+
+class KerberosPasswordSecrets(DatastoreSecrets):
+    """KerberosPasswordSecrets.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar secrets_type: [Required] Credential type used to authentication with storage. Required.
+     Known values are: "AccountKey", "Certificate", "Sas", "ServicePrincipal", "KerberosPassword",
+     and "KerberosKeytab".
+    :vartype secrets_type: str or ~azure.mgmt.machinelearningservices.models.SecretsType
+    :ivar kerberos_password: Kerberos password secret.
+    :vartype kerberos_password: str
+    """
+
+    _validation = {
+        "secrets_type": {"required": True},
+    }
+
+    _attribute_map = {
+        "secrets_type": {"key": "secretsType", "type": "str"},
+        "kerberos_password": {"key": "kerberosPassword", "type": "str"},
+    }
+
+    def __init__(self, *, kerberos_password: Optional[str] = None, **kwargs: Any) -> None:
+        """
+        :keyword kerberos_password: Kerberos password secret.
+        :paramtype kerberos_password: str
+        """
+        super().__init__(**kwargs)
+        self.secrets_type: str = "KerberosPassword"
+        self.kerberos_password = kerberos_password
+
+
+class KubernetesSchema(_serialization.Model):
+    """Kubernetes Compute Schema.
+
+    :ivar properties: Properties of Kubernetes.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.KubernetesProperties
+    """
+
+    _attribute_map = {
+        "properties": {"key": "properties", "type": "KubernetesProperties"},
+    }
+
+    def __init__(self, *, properties: Optional["_models.KubernetesProperties"] = None, **kwargs: Any) -> None:
+        """
+        :keyword properties: Properties of Kubernetes.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.KubernetesProperties
+        """
+        super().__init__(**kwargs)
+        self.properties = properties
+
+
+class Kubernetes(Compute, KubernetesSchema):  # pylint: disable=too-many-instance-attributes
+    """A Machine Learning compute based on Kubernetes Compute.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar properties: Properties of Kubernetes.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.KubernetesProperties
+    :ivar compute_type: The type of compute. Required. Known values are: "AKS", "Kubernetes",
+     "AmlCompute", "ComputeInstance", "DataFactory", "VirtualMachine", "HDInsight", "Databricks",
+     "DataLakeAnalytics", and "SynapseSpark".
+    :vartype compute_type: str or ~azure.mgmt.machinelearningservices.models.ComputeType
+    :ivar compute_location: Location for the underlying compute.
+    :vartype compute_location: str
+    :ivar provisioning_state: The provision state of the cluster. Valid values are Unknown,
+     Updating, Provisioning, Succeeded, and Failed. Known values are: "Unknown", "Updating",
+     "Creating", "Deleting", "Succeeded", "Failed", "Canceled", and "SoftDeleted".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.ProvisioningState
+    :ivar description: The description of the Machine Learning compute.
+    :vartype description: str
+    :ivar created_on: The time at which the compute was created.
+    :vartype created_on: ~datetime.datetime
+    :ivar modified_on: The time at which the compute was last modified.
+    :vartype modified_on: ~datetime.datetime
+    :ivar resource_id: ARM resource id of the underlying compute.
+    :vartype resource_id: str
+    :ivar provisioning_errors: Errors during provisioning.
+    :vartype provisioning_errors: list[~azure.mgmt.machinelearningservices.models.ErrorResponse]
+    :ivar is_attached_compute: Indicating whether the compute was provisioned by user and brought
+     from outside if true, or machine learning service provisioned it if false.
+    :vartype is_attached_compute: bool
+    :ivar disable_local_auth: Opt-out of local authentication and ensure customers can use only MSI
+     and AAD exclusively for authentication.
+    :vartype disable_local_auth: bool
+    """
+
+    _validation = {
+        "compute_type": {"required": True},
+        "provisioning_state": {"readonly": True},
+        "created_on": {"readonly": True},
+        "modified_on": {"readonly": True},
+        "provisioning_errors": {"readonly": True},
+        "is_attached_compute": {"readonly": True},
+    }
+
+    _attribute_map = {
+        "properties": {"key": "properties", "type": "KubernetesProperties"},
+        "compute_type": {"key": "computeType", "type": "str"},
+        "compute_location": {"key": "computeLocation", "type": "str"},
+        "provisioning_state": {"key": "provisioningState", "type": "str"},
+        "description": {"key": "description", "type": "str"},
+        "created_on": {"key": "createdOn", "type": "iso-8601"},
+        "modified_on": {"key": "modifiedOn", "type": "iso-8601"},
+        "resource_id": {"key": "resourceId", "type": "str"},
+        "provisioning_errors": {"key": "provisioningErrors", "type": "[ErrorResponse]"},
+        "is_attached_compute": {"key": "isAttachedCompute", "type": "bool"},
+        "disable_local_auth": {"key": "disableLocalAuth", "type": "bool"},
+    }
+
+    def __init__(
+        self,
+        *,
+        properties: Optional["_models.KubernetesProperties"] = None,
+        compute_location: Optional[str] = None,
+        description: Optional[str] = None,
+        resource_id: Optional[str] = None,
+        disable_local_auth: Optional[bool] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword properties: Properties of Kubernetes.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.KubernetesProperties
+        :keyword compute_location: Location for the underlying compute.
+        :paramtype compute_location: str
+        :keyword description: The description of the Machine Learning compute.
+        :paramtype description: str
+        :keyword resource_id: ARM resource id of the underlying compute.
+        :paramtype resource_id: str
+        :keyword disable_local_auth: Opt-out of local authentication and ensure customers can use only
+         MSI and AAD exclusively for authentication.
+        :paramtype disable_local_auth: bool
+        """
+        super().__init__(
+            compute_location=compute_location,
+            description=description,
+            resource_id=resource_id,
+            disable_local_auth=disable_local_auth,
+            properties=properties,
+            **kwargs
+        )
+        self.properties = properties
+        self.compute_type: str = "Kubernetes"
+        self.compute_location = compute_location
+        self.provisioning_state = None
+        self.description = description
+        self.created_on = None
+        self.modified_on = None
+        self.resource_id = resource_id
         self.provisioning_errors = None
         self.is_attached_compute = None
         self.disable_local_auth = disable_local_auth
@@ -12982,8 +14724,8 @@ class OnlineDeploymentProperties(EndpointDeploymentPropertiesBase):  # pylint: d
     :vartype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
     :ivar description: Description of the endpoint deployment.
     :vartype description: str
-    :ivar environment_id: ARM resource ID or AssetId of the environment specification for the
-     endpoint deployment.
+    :ivar environment_id: ARM resource ID of the environment specification for the endpoint
+     deployment.
     :vartype environment_id: str
     :ivar environment_variables: Environment variables configuration for the deployment.
     :vartype environment_variables: dict[str, str]
@@ -13069,15 +14811,15 @@ def __init__(
         readiness_probe: Optional["_models.ProbeSettings"] = None,
         request_settings: Optional["_models.OnlineRequestSettings"] = None,
         scale_settings: Optional["_models.OnlineScaleSettings"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword code_configuration: Code configuration for the endpoint deployment.
         :paramtype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
         :keyword description: Description of the endpoint deployment.
         :paramtype description: str
-        :keyword environment_id: ARM resource ID or AssetId of the environment specification for the
-         endpoint deployment.
+        :keyword environment_id: ARM resource ID of the environment specification for the endpoint
+         deployment.
         :paramtype environment_id: str
         :keyword environment_variables: Environment variables configuration for the deployment.
         :paramtype environment_variables: dict[str, str]
@@ -13141,8 +14883,8 @@ class KubernetesOnlineDeployment(OnlineDeploymentProperties):  # pylint: disable
     :vartype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
     :ivar description: Description of the endpoint deployment.
     :vartype description: str
-    :ivar environment_id: ARM resource ID or AssetId of the environment specification for the
-     endpoint deployment.
+    :ivar environment_id: ARM resource ID of the environment specification for the endpoint
+     deployment.
     :vartype environment_id: str
     :ivar environment_variables: Environment variables configuration for the deployment.
     :vartype environment_variables: dict[str, str]
@@ -13233,15 +14975,15 @@ def __init__(
         request_settings: Optional["_models.OnlineRequestSettings"] = None,
         scale_settings: Optional["_models.OnlineScaleSettings"] = None,
         container_resource_requirements: Optional["_models.ContainerResourceRequirements"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword code_configuration: Code configuration for the endpoint deployment.
         :paramtype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
         :keyword description: Description of the endpoint deployment.
         :paramtype description: str
-        :keyword environment_id: ARM resource ID or AssetId of the environment specification for the
-         endpoint deployment.
+        :keyword environment_id: ARM resource ID of the environment specification for the endpoint
+         deployment.
         :paramtype environment_id: str
         :keyword environment_variables: Environment variables configuration for the deployment.
         :paramtype environment_variables: dict[str, str]
@@ -13342,8 +15084,8 @@ def __init__(
         namespace: str = "default",
         default_instance_type: Optional[str] = None,
         instance_types: Optional[Dict[str, "_models.InstanceTypeSchema"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword relay_connection_string: Relay connection string.
         :paramtype relay_connection_string: str
@@ -13374,359 +15116,778 @@ def __init__(
         self.instance_types = instance_types
 
 
-class ListAmlUserFeatureResult(_serialization.Model):
-    """The List Aml user feature operation response.
-
-    Variables are only populated by the server, and will be ignored when sending a request.
+class LabelCategory(_serialization.Model):
+    """Label category definition.
 
-    :ivar value: The list of AML user facing features.
-    :vartype value: list[~azure.mgmt.machinelearningservices.models.AmlUserFeature]
-    :ivar next_link: The URI to fetch the next page of AML user features information. Call
-     ListNext() with this to fetch the next page of AML user features information.
-    :vartype next_link: str
+    :ivar classes: Dictionary of label classes in this category.
+    :vartype classes: dict[str, ~azure.mgmt.machinelearningservices.models.LabelClass]
+    :ivar display_name: Display name of the label category.
+    :vartype display_name: str
+    :ivar multi_select: Indicates whether it is allowed to select multiple classes in this
+     category. Known values are: "Enabled" and "Disabled".
+    :vartype multi_select: str or ~azure.mgmt.machinelearningservices.models.MultiSelect
     """
 
-    _validation = {
-        "value": {"readonly": True},
-        "next_link": {"readonly": True},
-    }
-
     _attribute_map = {
-        "value": {"key": "value", "type": "[AmlUserFeature]"},
-        "next_link": {"key": "nextLink", "type": "str"},
+        "classes": {"key": "classes", "type": "{LabelClass}"},
+        "display_name": {"key": "displayName", "type": "str"},
+        "multi_select": {"key": "multiSelect", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
-        """ """
+    def __init__(
+        self,
+        *,
+        classes: Optional[Dict[str, "_models.LabelClass"]] = None,
+        display_name: Optional[str] = None,
+        multi_select: Optional[Union[str, "_models.MultiSelect"]] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword classes: Dictionary of label classes in this category.
+        :paramtype classes: dict[str, ~azure.mgmt.machinelearningservices.models.LabelClass]
+        :keyword display_name: Display name of the label category.
+        :paramtype display_name: str
+        :keyword multi_select: Indicates whether it is allowed to select multiple classes in this
+         category. Known values are: "Enabled" and "Disabled".
+        :paramtype multi_select: str or ~azure.mgmt.machinelearningservices.models.MultiSelect
+        """
         super().__init__(**kwargs)
-        self.value = None
-        self.next_link = None
-
+        self.classes = classes
+        self.display_name = display_name
+        self.multi_select = multi_select
 
-class ListNotebookKeysResult(_serialization.Model):
-    """ListNotebookKeysResult.
 
-    Variables are only populated by the server, and will be ignored when sending a request.
+class LabelClass(_serialization.Model):
+    """Label class definition.
 
-    :ivar primary_access_key:
-    :vartype primary_access_key: str
-    :ivar secondary_access_key:
-    :vartype secondary_access_key: str
+    :ivar display_name: Display name of the label class.
+    :vartype display_name: str
+    :ivar subclasses: Dictionary of subclasses of the label class.
+    :vartype subclasses: dict[str, ~azure.mgmt.machinelearningservices.models.LabelClass]
     """
 
-    _validation = {
-        "primary_access_key": {"readonly": True},
-        "secondary_access_key": {"readonly": True},
-    }
-
     _attribute_map = {
-        "primary_access_key": {"key": "primaryAccessKey", "type": "str"},
-        "secondary_access_key": {"key": "secondaryAccessKey", "type": "str"},
+        "display_name": {"key": "displayName", "type": "str"},
+        "subclasses": {"key": "subclasses", "type": "{LabelClass}"},
     }
 
-    def __init__(self, **kwargs):
-        """ """
+    def __init__(
+        self,
+        *,
+        display_name: Optional[str] = None,
+        subclasses: Optional[Dict[str, "_models.LabelClass"]] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword display_name: Display name of the label class.
+        :paramtype display_name: str
+        :keyword subclasses: Dictionary of subclasses of the label class.
+        :paramtype subclasses: dict[str, ~azure.mgmt.machinelearningservices.models.LabelClass]
+        """
         super().__init__(**kwargs)
-        self.primary_access_key = None
-        self.secondary_access_key = None
-
+        self.display_name = display_name
+        self.subclasses = subclasses
 
-class ListStorageAccountKeysResult(_serialization.Model):
-    """ListStorageAccountKeysResult.
 
-    Variables are only populated by the server, and will be ignored when sending a request.
+class LabelingDataConfiguration(_serialization.Model):
+    """Labeling data configuration definition.
 
-    :ivar user_storage_key:
-    :vartype user_storage_key: str
+    :ivar data_id: Resource Id of the data asset to perform labeling.
+    :vartype data_id: str
+    :ivar incremental_data_refresh: Indicates whether to enable incremental data refresh. Known
+     values are: "Enabled" and "Disabled".
+    :vartype incremental_data_refresh: str or
+     ~azure.mgmt.machinelearningservices.models.IncrementalDataRefresh
     """
 
-    _validation = {
-        "user_storage_key": {"readonly": True},
-    }
-
     _attribute_map = {
-        "user_storage_key": {"key": "userStorageKey", "type": "str"},
+        "data_id": {"key": "dataId", "type": "str"},
+        "incremental_data_refresh": {"key": "incrementalDataRefresh", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
-        """ """
-        super().__init__(**kwargs)
-        self.user_storage_key = None
-
-
-class ListUsagesResult(_serialization.Model):
-    """The List Usages operation response.
-
-    Variables are only populated by the server, and will be ignored when sending a request.
-
-    :ivar value: The list of AML resource usages.
-    :vartype value: list[~azure.mgmt.machinelearningservices.models.Usage]
-    :ivar next_link: The URI to fetch the next page of AML resource usage information. Call
-     ListNext() with this to fetch the next page of AML resource usage information.
-    :vartype next_link: str
-    """
-
-    _validation = {
-        "value": {"readonly": True},
-        "next_link": {"readonly": True},
-    }
-
-    _attribute_map = {
-        "value": {"key": "value", "type": "[Usage]"},
-        "next_link": {"key": "nextLink", "type": "str"},
-    }
-
-    def __init__(self, **kwargs):
-        """ """
+    def __init__(
+        self,
+        *,
+        data_id: Optional[str] = None,
+        incremental_data_refresh: Optional[Union[str, "_models.IncrementalDataRefresh"]] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword data_id: Resource Id of the data asset to perform labeling.
+        :paramtype data_id: str
+        :keyword incremental_data_refresh: Indicates whether to enable incremental data refresh. Known
+         values are: "Enabled" and "Disabled".
+        :paramtype incremental_data_refresh: str or
+         ~azure.mgmt.machinelearningservices.models.IncrementalDataRefresh
+        """
         super().__init__(**kwargs)
-        self.value = None
-        self.next_link = None
+        self.data_id = data_id
+        self.incremental_data_refresh = incremental_data_refresh
 
 
-class ListWorkspaceKeysResult(_serialization.Model):
-    """ListWorkspaceKeysResult.
+class LabelingJob(Resource):
+    """Azure Resource Manager resource envelope.
 
     Variables are only populated by the server, and will be ignored when sending a request.
 
-    :ivar user_storage_key:
-    :vartype user_storage_key: str
-    :ivar user_storage_resource_id:
-    :vartype user_storage_resource_id: str
-    :ivar app_insights_instrumentation_key:
-    :vartype app_insights_instrumentation_key: str
-    :ivar container_registry_credentials:
-    :vartype container_registry_credentials:
-     ~azure.mgmt.machinelearningservices.models.RegistryListCredentialsResult
-    :ivar notebook_access_keys:
-    :vartype notebook_access_keys:
-     ~azure.mgmt.machinelearningservices.models.ListNotebookKeysResult
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar properties: [Required] Additional attributes of the entity. Required.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.LabelingJobProperties
     """
 
     _validation = {
-        "user_storage_key": {"readonly": True},
-        "user_storage_resource_id": {"readonly": True},
-        "app_insights_instrumentation_key": {"readonly": True},
-        "container_registry_credentials": {"readonly": True},
-        "notebook_access_keys": {"readonly": True},
+        "id": {"readonly": True},
+        "name": {"readonly": True},
+        "type": {"readonly": True},
+        "system_data": {"readonly": True},
+        "properties": {"required": True},
     }
 
     _attribute_map = {
-        "user_storage_key": {"key": "userStorageKey", "type": "str"},
-        "user_storage_resource_id": {"key": "userStorageResourceId", "type": "str"},
-        "app_insights_instrumentation_key": {"key": "appInsightsInstrumentationKey", "type": "str"},
-        "container_registry_credentials": {
-            "key": "containerRegistryCredentials",
-            "type": "RegistryListCredentialsResult",
-        },
-        "notebook_access_keys": {"key": "notebookAccessKeys", "type": "ListNotebookKeysResult"},
+        "id": {"key": "id", "type": "str"},
+        "name": {"key": "name", "type": "str"},
+        "type": {"key": "type", "type": "str"},
+        "system_data": {"key": "systemData", "type": "SystemData"},
+        "properties": {"key": "properties", "type": "LabelingJobProperties"},
     }
 
-    def __init__(self, **kwargs):
-        """ """
+    def __init__(self, *, properties: "_models.LabelingJobProperties", **kwargs: Any) -> None:
+        """
+        :keyword properties: [Required] Additional attributes of the entity. Required.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.LabelingJobProperties
+        """
         super().__init__(**kwargs)
-        self.user_storage_key = None
-        self.user_storage_resource_id = None
-        self.app_insights_instrumentation_key = None
-        self.container_registry_credentials = None
-        self.notebook_access_keys = None
+        self.properties = properties
 
 
-class ListWorkspaceQuotas(_serialization.Model):
-    """The List WorkspaceQuotasByVMFamily operation response.
+class LabelingJobMediaProperties(_serialization.Model):
+    """Properties of a labeling job.
 
-    Variables are only populated by the server, and will be ignored when sending a request.
+    You probably want to use the sub-classes and not this class directly. Known sub-classes are:
+    LabelingJobImageProperties, LabelingJobTextProperties
 
-    :ivar value: The list of Workspace Quotas by VM Family.
-    :vartype value: list[~azure.mgmt.machinelearningservices.models.ResourceQuota]
-    :ivar next_link: The URI to fetch the next page of workspace quota information by VM Family.
-     Call ListNext() with this to fetch the next page of Workspace Quota information.
-    :vartype next_link: str
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar media_type: [Required] Media type of the job. Required. Known values are: "Image" and
+     "Text".
+    :vartype media_type: str or ~azure.mgmt.machinelearningservices.models.MediaType
     """
 
     _validation = {
-        "value": {"readonly": True},
-        "next_link": {"readonly": True},
+        "media_type": {"required": True},
     }
 
     _attribute_map = {
-        "value": {"key": "value", "type": "[ResourceQuota]"},
-        "next_link": {"key": "nextLink", "type": "str"},
+        "media_type": {"key": "mediaType", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    _subtype_map = {"media_type": {"Image": "LabelingJobImageProperties", "Text": "LabelingJobTextProperties"}}
+
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
-        self.value = None
-        self.next_link = None
+        self.media_type: Optional[str] = None
 
 
-class LiteralJobInput(JobInput):
-    """Literal input type.
+class LabelingJobImageProperties(LabelingJobMediaProperties):
+    """Properties of a labeling job for image data.
 
     All required parameters must be populated in order to send to Azure.
 
-    :ivar description: Description for the input.
-    :vartype description: str
-    :ivar job_input_type: [Required] Specifies the type of job. Required. Known values are:
-     "literal", "uri_file", "uri_folder", "mltable", "custom_model", "mlflow_model", and
-     "triton_model".
-    :vartype job_input_type: str or ~azure.mgmt.machinelearningservices.models.JobInputType
-    :ivar value: [Required] Literal value for the input. Required.
-    :vartype value: str
+    :ivar media_type: [Required] Media type of the job. Required. Known values are: "Image" and
+     "Text".
+    :vartype media_type: str or ~azure.mgmt.machinelearningservices.models.MediaType
+    :ivar annotation_type: Annotation type of image labeling job. Known values are:
+     "Classification", "BoundingBox", and "InstanceSegmentation".
+    :vartype annotation_type: str or ~azure.mgmt.machinelearningservices.models.ImageAnnotationType
     """
 
     _validation = {
-        "job_input_type": {"required": True},
-        "value": {"required": True, "pattern": r"[a-zA-Z0-9_]"},
+        "media_type": {"required": True},
     }
 
     _attribute_map = {
-        "description": {"key": "description", "type": "str"},
-        "job_input_type": {"key": "jobInputType", "type": "str"},
-        "value": {"key": "value", "type": "str"},
+        "media_type": {"key": "mediaType", "type": "str"},
+        "annotation_type": {"key": "annotationType", "type": "str"},
     }
 
-    def __init__(self, *, value: str, description: Optional[str] = None, **kwargs):
+    def __init__(
+        self, *, annotation_type: Optional[Union[str, "_models.ImageAnnotationType"]] = None, **kwargs: Any
+    ) -> None:
         """
-        :keyword description: Description for the input.
-        :paramtype description: str
-        :keyword value: [Required] Literal value for the input. Required.
-        :paramtype value: str
+        :keyword annotation_type: Annotation type of image labeling job. Known values are:
+         "Classification", "BoundingBox", and "InstanceSegmentation".
+        :paramtype annotation_type: str or
+         ~azure.mgmt.machinelearningservices.models.ImageAnnotationType
         """
-        super().__init__(description=description, **kwargs)
-        self.job_input_type: str = "literal"
-        self.value = value
-
+        super().__init__(**kwargs)
+        self.media_type: str = "Image"
+        self.annotation_type = annotation_type
 
-class ManagedIdentity(IdentityConfiguration):
-    """Managed identity configuration.
 
-    All required parameters must be populated in order to send to Azure.
+class LabelingJobInstructions(_serialization.Model):
+    """Instructions for labeling job.
 
-    :ivar identity_type: [Required] Specifies the type of identity framework. Required. Known
-     values are: "Managed", "AMLToken", and "UserIdentity".
-    :vartype identity_type: str or
-     ~azure.mgmt.machinelearningservices.models.IdentityConfigurationType
-    :ivar client_id: Specifies a user-assigned identity by client ID. For system-assigned, do not
-     set this field.
-    :vartype client_id: str
-    :ivar object_id: Specifies a user-assigned identity by object ID. For system-assigned, do not
-     set this field.
-    :vartype object_id: str
-    :ivar resource_id: Specifies a user-assigned identity by ARM resource ID. For system-assigned,
-     do not set this field.
-    :vartype resource_id: str
+    :ivar uri: The link to a page with detailed labeling instructions for labelers.
+    :vartype uri: str
     """
 
-    _validation = {
-        "identity_type": {"required": True},
-    }
-
     _attribute_map = {
-        "identity_type": {"key": "identityType", "type": "str"},
-        "client_id": {"key": "clientId", "type": "str"},
-        "object_id": {"key": "objectId", "type": "str"},
-        "resource_id": {"key": "resourceId", "type": "str"},
+        "uri": {"key": "uri", "type": "str"},
     }
 
-    def __init__(
-        self,
-        *,
-        client_id: Optional[str] = None,
-        object_id: Optional[str] = None,
-        resource_id: Optional[str] = None,
-        **kwargs
-    ):
+    def __init__(self, *, uri: Optional[str] = None, **kwargs: Any) -> None:
         """
-        :keyword client_id: Specifies a user-assigned identity by client ID. For system-assigned, do
-         not set this field.
-        :paramtype client_id: str
-        :keyword object_id: Specifies a user-assigned identity by object ID. For system-assigned, do
-         not set this field.
-        :paramtype object_id: str
-        :keyword resource_id: Specifies a user-assigned identity by ARM resource ID. For
-         system-assigned, do not set this field.
-        :paramtype resource_id: str
+        :keyword uri: The link to a page with detailed labeling instructions for labelers.
+        :paramtype uri: str
         """
         super().__init__(**kwargs)
-        self.identity_type: str = "Managed"
-        self.client_id = client_id
-        self.object_id = object_id
-        self.resource_id = resource_id
+        self.uri = uri
 
 
-class WorkspaceConnectionPropertiesV2(_serialization.Model):
-    """WorkspaceConnectionPropertiesV2.
+class LabelingJobProperties(JobBaseProperties):  # pylint: disable=too-many-instance-attributes
+    """Labeling job definition.
 
-    You probably want to use the sub-classes and not this class directly. Known sub-classes are:
-    ManagedIdentityAuthTypeWorkspaceConnectionProperties,
-    NoneAuthTypeWorkspaceConnectionProperties, PATAuthTypeWorkspaceConnectionProperties,
-    SASAuthTypeWorkspaceConnectionProperties, UsernamePasswordAuthTypeWorkspaceConnectionProperties
+    Variables are only populated by the server, and will be ignored when sending a request.
 
     All required parameters must be populated in order to send to Azure.
 
-    :ivar auth_type: Authentication type of the connection target. Required. Known values are:
-     "PAT", "ManagedIdentity", "UsernamePassword", "None", and "SAS".
-    :vartype auth_type: str or ~azure.mgmt.machinelearningservices.models.ConnectionAuthType
-    :ivar category: Category of the connection. Known values are: "PythonFeed",
-     "ContainerRegistry", and "Git".
-    :vartype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
-    :ivar target:
-    :vartype target: str
-    :ivar value: Value details of the workspace connection.
-    :vartype value: str
-    :ivar value_format: format for the workspace connection value. "JSON"
-    :vartype value_format: str or ~azure.mgmt.machinelearningservices.models.ValueFormat
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar component_id: ARM resource ID of the component resource.
+    :vartype component_id: str
+    :ivar compute_id: ARM resource ID of the compute resource.
+    :vartype compute_id: str
+    :ivar display_name: Display name of job.
+    :vartype display_name: str
+    :ivar experiment_name: The name of the experiment the job belongs to. If not set, the job is
+     placed in the "Default" experiment.
+    :vartype experiment_name: str
+    :ivar identity: Identity configuration. If set, this should be one of AmlToken,
+     ManagedIdentity, UserIdentity or null.
+     Defaults to AmlToken if null.
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.IdentityConfiguration
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar job_type: [Required] Specifies the type of job. Required. Known values are: "AutoML",
+     "Command", "Labeling", "Sweep", "Pipeline", and "Spark".
+    :vartype job_type: str or ~azure.mgmt.machinelearningservices.models.JobType
+    :ivar services: List of JobEndpoints.
+     For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
+    :vartype services: dict[str, ~azure.mgmt.machinelearningservices.models.JobService]
+    :ivar status: Status of the job. Known values are: "NotStarted", "Starting", "Provisioning",
+     "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed", "Failed",
+     "Canceled", "NotResponding", "Paused", "Unknown", and "Scheduled".
+    :vartype status: str or ~azure.mgmt.machinelearningservices.models.JobStatus
+    :ivar created_date_time: Created time of the job in UTC timezone.
+    :vartype created_date_time: ~datetime.datetime
+    :ivar data_configuration: Configuration of data used in the job.
+    :vartype data_configuration:
+     ~azure.mgmt.machinelearningservices.models.LabelingDataConfiguration
+    :ivar job_instructions: Labeling instructions of the job.
+    :vartype job_instructions: ~azure.mgmt.machinelearningservices.models.LabelingJobInstructions
+    :ivar label_categories: Label categories of the job.
+    :vartype label_categories: dict[str, ~azure.mgmt.machinelearningservices.models.LabelCategory]
+    :ivar labeling_job_media_properties: Media type specific properties in the job.
+    :vartype labeling_job_media_properties:
+     ~azure.mgmt.machinelearningservices.models.LabelingJobMediaProperties
+    :ivar ml_assist_configuration: Configuration of MLAssist feature in the job.
+    :vartype ml_assist_configuration:
+     ~azure.mgmt.machinelearningservices.models.MLAssistConfiguration
+    :ivar progress_metrics: Progress metrics of the job.
+    :vartype progress_metrics: ~azure.mgmt.machinelearningservices.models.ProgressMetrics
+    :ivar project_id: Internal id of the job(Previously called project).
+    :vartype project_id: str
+    :ivar provisioning_state: Specifies the labeling job provisioning state. Known values are:
+     "Succeeded", "Failed", "Canceled", and "InProgress".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.JobProvisioningState
+    :ivar status_messages: Status messages of the job.
+    :vartype status_messages: list[~azure.mgmt.machinelearningservices.models.StatusMessage]
     """
 
     _validation = {
-        "auth_type": {"required": True},
+        "job_type": {"required": True},
+        "status": {"readonly": True},
+        "created_date_time": {"readonly": True},
+        "progress_metrics": {"readonly": True},
+        "project_id": {"readonly": True},
+        "provisioning_state": {"readonly": True},
+        "status_messages": {"readonly": True},
     }
 
     _attribute_map = {
-        "auth_type": {"key": "authType", "type": "str"},
-        "category": {"key": "category", "type": "str"},
-        "target": {"key": "target", "type": "str"},
-        "value": {"key": "value", "type": "str"},
-        "value_format": {"key": "valueFormat", "type": "str"},
+        "description": {"key": "description", "type": "str"},
+        "properties": {"key": "properties", "type": "{str}"},
+        "tags": {"key": "tags", "type": "{str}"},
+        "component_id": {"key": "componentId", "type": "str"},
+        "compute_id": {"key": "computeId", "type": "str"},
+        "display_name": {"key": "displayName", "type": "str"},
+        "experiment_name": {"key": "experimentName", "type": "str"},
+        "identity": {"key": "identity", "type": "IdentityConfiguration"},
+        "is_archived": {"key": "isArchived", "type": "bool"},
+        "job_type": {"key": "jobType", "type": "str"},
+        "services": {"key": "services", "type": "{JobService}"},
+        "status": {"key": "status", "type": "str"},
+        "created_date_time": {"key": "createdDateTime", "type": "iso-8601"},
+        "data_configuration": {"key": "dataConfiguration", "type": "LabelingDataConfiguration"},
+        "job_instructions": {"key": "jobInstructions", "type": "LabelingJobInstructions"},
+        "label_categories": {"key": "labelCategories", "type": "{LabelCategory}"},
+        "labeling_job_media_properties": {"key": "labelingJobMediaProperties", "type": "LabelingJobMediaProperties"},
+        "ml_assist_configuration": {"key": "mlAssistConfiguration", "type": "MLAssistConfiguration"},
+        "progress_metrics": {"key": "progressMetrics", "type": "ProgressMetrics"},
+        "project_id": {"key": "projectId", "type": "str"},
+        "provisioning_state": {"key": "provisioningState", "type": "str"},
+        "status_messages": {"key": "statusMessages", "type": "[StatusMessage]"},
     }
 
-    _subtype_map = {
-        "auth_type": {
-            "ManagedIdentity": "ManagedIdentityAuthTypeWorkspaceConnectionProperties",
-            "None": "NoneAuthTypeWorkspaceConnectionProperties",
-            "PAT": "PATAuthTypeWorkspaceConnectionProperties",
-            "SAS": "SASAuthTypeWorkspaceConnectionProperties",
-            "UsernamePassword": "UsernamePasswordAuthTypeWorkspaceConnectionProperties",
-        }
+    def __init__(
+        self,
+        *,
+        description: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        tags: Optional[Dict[str, str]] = None,
+        component_id: Optional[str] = None,
+        compute_id: Optional[str] = None,
+        display_name: Optional[str] = None,
+        experiment_name: str = "Default",
+        identity: Optional["_models.IdentityConfiguration"] = None,
+        is_archived: bool = False,
+        services: Optional[Dict[str, "_models.JobService"]] = None,
+        data_configuration: Optional["_models.LabelingDataConfiguration"] = None,
+        job_instructions: Optional["_models.LabelingJobInstructions"] = None,
+        label_categories: Optional[Dict[str, "_models.LabelCategory"]] = None,
+        labeling_job_media_properties: Optional["_models.LabelingJobMediaProperties"] = None,
+        ml_assist_configuration: Optional["_models.MLAssistConfiguration"] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword component_id: ARM resource ID of the component resource.
+        :paramtype component_id: str
+        :keyword compute_id: ARM resource ID of the compute resource.
+        :paramtype compute_id: str
+        :keyword display_name: Display name of job.
+        :paramtype display_name: str
+        :keyword experiment_name: The name of the experiment the job belongs to. If not set, the job is
+         placed in the "Default" experiment.
+        :paramtype experiment_name: str
+        :keyword identity: Identity configuration. If set, this should be one of AmlToken,
+         ManagedIdentity, UserIdentity or null.
+         Defaults to AmlToken if null.
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.IdentityConfiguration
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        :keyword services: List of JobEndpoints.
+         For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
+        :paramtype services: dict[str, ~azure.mgmt.machinelearningservices.models.JobService]
+        :keyword data_configuration: Configuration of data used in the job.
+        :paramtype data_configuration:
+         ~azure.mgmt.machinelearningservices.models.LabelingDataConfiguration
+        :keyword job_instructions: Labeling instructions of the job.
+        :paramtype job_instructions: ~azure.mgmt.machinelearningservices.models.LabelingJobInstructions
+        :keyword label_categories: Label categories of the job.
+        :paramtype label_categories: dict[str,
+         ~azure.mgmt.machinelearningservices.models.LabelCategory]
+        :keyword labeling_job_media_properties: Media type specific properties in the job.
+        :paramtype labeling_job_media_properties:
+         ~azure.mgmt.machinelearningservices.models.LabelingJobMediaProperties
+        :keyword ml_assist_configuration: Configuration of MLAssist feature in the job.
+        :paramtype ml_assist_configuration:
+         ~azure.mgmt.machinelearningservices.models.MLAssistConfiguration
+        """
+        super().__init__(
+            description=description,
+            properties=properties,
+            tags=tags,
+            component_id=component_id,
+            compute_id=compute_id,
+            display_name=display_name,
+            experiment_name=experiment_name,
+            identity=identity,
+            is_archived=is_archived,
+            services=services,
+            **kwargs
+        )
+        self.job_type: str = "Labeling"
+        self.created_date_time = None
+        self.data_configuration = data_configuration
+        self.job_instructions = job_instructions
+        self.label_categories = label_categories
+        self.labeling_job_media_properties = labeling_job_media_properties
+        self.ml_assist_configuration = ml_assist_configuration
+        self.progress_metrics = None
+        self.project_id = None
+        self.provisioning_state = None
+        self.status_messages = None
+
+
+class LabelingJobResourceArmPaginatedResult(_serialization.Model):
+    """A paginated list of LabelingJob entities.
+
+    :ivar next_link: The link to the next page of LabelingJob objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type LabelingJob.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.LabelingJob]
+    """
+
+    _attribute_map = {
+        "next_link": {"key": "nextLink", "type": "str"},
+        "value": {"key": "value", "type": "[LabelingJob]"},
+    }
+
+    def __init__(
+        self, *, next_link: Optional[str] = None, value: Optional[List["_models.LabelingJob"]] = None, **kwargs: Any
+    ) -> None:
+        """
+        :keyword next_link: The link to the next page of LabelingJob objects. If null, there are no
+         additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type LabelingJob.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.LabelingJob]
+        """
+        super().__init__(**kwargs)
+        self.next_link = next_link
+        self.value = value
+
+
+class LabelingJobTextProperties(LabelingJobMediaProperties):
+    """Properties of a labeling job for text data.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar media_type: [Required] Media type of the job. Required. Known values are: "Image" and
+     "Text".
+    :vartype media_type: str or ~azure.mgmt.machinelearningservices.models.MediaType
+    :ivar annotation_type: Annotation type of text labeling job. Known values are: "Classification"
+     and "NamedEntityRecognition".
+    :vartype annotation_type: str or ~azure.mgmt.machinelearningservices.models.TextAnnotationType
+    """
+
+    _validation = {
+        "media_type": {"required": True},
+    }
+
+    _attribute_map = {
+        "media_type": {"key": "mediaType", "type": "str"},
+        "annotation_type": {"key": "annotationType", "type": "str"},
+    }
+
+    def __init__(
+        self, *, annotation_type: Optional[Union[str, "_models.TextAnnotationType"]] = None, **kwargs: Any
+    ) -> None:
+        """
+        :keyword annotation_type: Annotation type of text labeling job. Known values are:
+         "Classification" and "NamedEntityRecognition".
+        :paramtype annotation_type: str or
+         ~azure.mgmt.machinelearningservices.models.TextAnnotationType
+        """
+        super().__init__(**kwargs)
+        self.media_type: str = "Text"
+        self.annotation_type = annotation_type
+
+
+class ListAmlUserFeatureResult(_serialization.Model):
+    """The List Aml user feature operation response.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar value: The list of AML user facing features.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.AmlUserFeature]
+    :ivar next_link: The URI to fetch the next page of AML user features information. Call
+     ListNext() with this to fetch the next page of AML user features information.
+    :vartype next_link: str
+    """
+
+    _validation = {
+        "value": {"readonly": True},
+        "next_link": {"readonly": True},
+    }
+
+    _attribute_map = {
+        "value": {"key": "value", "type": "[AmlUserFeature]"},
+        "next_link": {"key": "nextLink", "type": "str"},
+    }
+
+    def __init__(self, **kwargs: Any) -> None:
+        """ """
+        super().__init__(**kwargs)
+        self.value = None
+        self.next_link = None
+
+
+class ListNotebookKeysResult(_serialization.Model):
+    """ListNotebookKeysResult.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar primary_access_key:
+    :vartype primary_access_key: str
+    :ivar secondary_access_key:
+    :vartype secondary_access_key: str
+    """
+
+    _validation = {
+        "primary_access_key": {"readonly": True},
+        "secondary_access_key": {"readonly": True},
+    }
+
+    _attribute_map = {
+        "primary_access_key": {"key": "primaryAccessKey", "type": "str"},
+        "secondary_access_key": {"key": "secondaryAccessKey", "type": "str"},
+    }
+
+    def __init__(self, **kwargs: Any) -> None:
+        """ """
+        super().__init__(**kwargs)
+        self.primary_access_key = None
+        self.secondary_access_key = None
+
+
+class ListStorageAccountKeysResult(_serialization.Model):
+    """ListStorageAccountKeysResult.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar user_storage_key:
+    :vartype user_storage_key: str
+    """
+
+    _validation = {
+        "user_storage_key": {"readonly": True},
+    }
+
+    _attribute_map = {
+        "user_storage_key": {"key": "userStorageKey", "type": "str"},
+    }
+
+    def __init__(self, **kwargs: Any) -> None:
+        """ """
+        super().__init__(**kwargs)
+        self.user_storage_key = None
+
+
+class ListUsagesResult(_serialization.Model):
+    """The List Usages operation response.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar value: The list of AML resource usages.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.Usage]
+    :ivar next_link: The URI to fetch the next page of AML resource usage information. Call
+     ListNext() with this to fetch the next page of AML resource usage information.
+    :vartype next_link: str
+    """
+
+    _validation = {
+        "value": {"readonly": True},
+        "next_link": {"readonly": True},
+    }
+
+    _attribute_map = {
+        "value": {"key": "value", "type": "[Usage]"},
+        "next_link": {"key": "nextLink", "type": "str"},
+    }
+
+    def __init__(self, **kwargs: Any) -> None:
+        """ """
+        super().__init__(**kwargs)
+        self.value = None
+        self.next_link = None
+
+
+class ListWorkspaceKeysResult(_serialization.Model):
+    """ListWorkspaceKeysResult.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar user_storage_key:
+    :vartype user_storage_key: str
+    :ivar user_storage_resource_id:
+    :vartype user_storage_resource_id: str
+    :ivar app_insights_instrumentation_key:
+    :vartype app_insights_instrumentation_key: str
+    :ivar container_registry_credentials:
+    :vartype container_registry_credentials:
+     ~azure.mgmt.machinelearningservices.models.RegistryListCredentialsResult
+    :ivar notebook_access_keys:
+    :vartype notebook_access_keys:
+     ~azure.mgmt.machinelearningservices.models.ListNotebookKeysResult
+    """
+
+    _validation = {
+        "user_storage_key": {"readonly": True},
+        "user_storage_resource_id": {"readonly": True},
+        "app_insights_instrumentation_key": {"readonly": True},
+        "container_registry_credentials": {"readonly": True},
+        "notebook_access_keys": {"readonly": True},
+    }
+
+    _attribute_map = {
+        "user_storage_key": {"key": "userStorageKey", "type": "str"},
+        "user_storage_resource_id": {"key": "userStorageResourceId", "type": "str"},
+        "app_insights_instrumentation_key": {"key": "appInsightsInstrumentationKey", "type": "str"},
+        "container_registry_credentials": {
+            "key": "containerRegistryCredentials",
+            "type": "RegistryListCredentialsResult",
+        },
+        "notebook_access_keys": {"key": "notebookAccessKeys", "type": "ListNotebookKeysResult"},
+    }
+
+    def __init__(self, **kwargs: Any) -> None:
+        """ """
+        super().__init__(**kwargs)
+        self.user_storage_key = None
+        self.user_storage_resource_id = None
+        self.app_insights_instrumentation_key = None
+        self.container_registry_credentials = None
+        self.notebook_access_keys = None
+
+
+class ListWorkspaceQuotas(_serialization.Model):
+    """The List WorkspaceQuotasByVMFamily operation response.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar value: The list of Workspace Quotas by VM Family.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.ResourceQuota]
+    :ivar next_link: The URI to fetch the next page of workspace quota information by VM Family.
+     Call ListNext() with this to fetch the next page of Workspace Quota information.
+    :vartype next_link: str
+    """
+
+    _validation = {
+        "value": {"readonly": True},
+        "next_link": {"readonly": True},
+    }
+
+    _attribute_map = {
+        "value": {"key": "value", "type": "[ResourceQuota]"},
+        "next_link": {"key": "nextLink", "type": "str"},
+    }
+
+    def __init__(self, **kwargs: Any) -> None:
+        """ """
+        super().__init__(**kwargs)
+        self.value = None
+        self.next_link = None
+
+
+class LiteralJobInput(JobInput):
+    """Literal input type.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: Description for the input.
+    :vartype description: str
+    :ivar job_input_type: [Required] Specifies the type of job. Required. Known values are:
+     "literal", "uri_file", "uri_folder", "mltable", "custom_model", "mlflow_model", and
+     "triton_model".
+    :vartype job_input_type: str or ~azure.mgmt.machinelearningservices.models.JobInputType
+    :ivar value: [Required] Literal value for the input. Required.
+    :vartype value: str
+    """
+
+    _validation = {
+        "job_input_type": {"required": True},
+        "value": {"required": True, "pattern": r"[a-zA-Z0-9_]"},
+    }
+
+    _attribute_map = {
+        "description": {"key": "description", "type": "str"},
+        "job_input_type": {"key": "jobInputType", "type": "str"},
+        "value": {"key": "value", "type": "str"},
+    }
+
+    def __init__(self, *, value: str, description: Optional[str] = None, **kwargs: Any) -> None:
+        """
+        :keyword description: Description for the input.
+        :paramtype description: str
+        :keyword value: [Required] Literal value for the input. Required.
+        :paramtype value: str
+        """
+        super().__init__(description=description, **kwargs)
+        self.job_input_type: str = "literal"
+        self.value = value
+
+
+class ManagedIdentity(IdentityConfiguration):
+    """Managed identity configuration.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar identity_type: [Required] Specifies the type of identity framework. Required. Known
+     values are: "Managed", "AMLToken", and "UserIdentity".
+    :vartype identity_type: str or
+     ~azure.mgmt.machinelearningservices.models.IdentityConfigurationType
+    :ivar client_id: Specifies a user-assigned identity by client ID. For system-assigned, do not
+     set this field.
+    :vartype client_id: str
+    :ivar object_id: Specifies a user-assigned identity by object ID. For system-assigned, do not
+     set this field.
+    :vartype object_id: str
+    :ivar resource_id: Specifies a user-assigned identity by ARM resource ID. For system-assigned,
+     do not set this field.
+    :vartype resource_id: str
+    """
+
+    _validation = {
+        "identity_type": {"required": True},
+    }
+
+    _attribute_map = {
+        "identity_type": {"key": "identityType", "type": "str"},
+        "client_id": {"key": "clientId", "type": "str"},
+        "object_id": {"key": "objectId", "type": "str"},
+        "resource_id": {"key": "resourceId", "type": "str"},
     }
 
     def __init__(
         self,
         *,
-        category: Optional[Union[str, "_models.ConnectionCategory"]] = None,
-        target: Optional[str] = None,
-        value: Optional[str] = None,
-        value_format: Optional[Union[str, "_models.ValueFormat"]] = None,
-        **kwargs
-    ):
+        client_id: Optional[str] = None,
+        object_id: Optional[str] = None,
+        resource_id: Optional[str] = None,
+        **kwargs: Any
+    ) -> None:
         """
-        :keyword category: Category of the connection. Known values are: "PythonFeed",
-         "ContainerRegistry", and "Git".
-        :paramtype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
-        :keyword target:
-        :paramtype target: str
-        :keyword value: Value details of the workspace connection.
-        :paramtype value: str
-        :keyword value_format: format for the workspace connection value. "JSON"
-        :paramtype value_format: str or ~azure.mgmt.machinelearningservices.models.ValueFormat
+        :keyword client_id: Specifies a user-assigned identity by client ID. For system-assigned, do
+         not set this field.
+        :paramtype client_id: str
+        :keyword object_id: Specifies a user-assigned identity by object ID. For system-assigned, do
+         not set this field.
+        :paramtype object_id: str
+        :keyword resource_id: Specifies a user-assigned identity by ARM resource ID. For
+         system-assigned, do not set this field.
+        :paramtype resource_id: str
         """
         super().__init__(**kwargs)
-        self.auth_type: Optional[str] = None
-        self.category = category
-        self.target = target
-        self.value = value
-        self.value_format = value_format
+        self.identity_type: str = "Managed"
+        self.client_id = client_id
+        self.object_id = object_id
+        self.resource_id = resource_id
 
 
 class ManagedIdentityAuthTypeWorkspaceConnectionProperties(WorkspaceConnectionPropertiesV2):
@@ -13735,10 +15896,12 @@ class ManagedIdentityAuthTypeWorkspaceConnectionProperties(WorkspaceConnectionPr
     All required parameters must be populated in order to send to Azure.
 
     :ivar auth_type: Authentication type of the connection target. Required. Known values are:
-     "PAT", "ManagedIdentity", "UsernamePassword", "None", and "SAS".
+     "PAT", "ManagedIdentity", "UsernamePassword", "None", "SAS", "ServicePrincipal", and
+     "AccessKey".
     :vartype auth_type: str or ~azure.mgmt.machinelearningservices.models.ConnectionAuthType
     :ivar category: Category of the connection. Known values are: "PythonFeed",
-     "ContainerRegistry", and "Git".
+     "ContainerRegistry", "Git", "FeatureStore", "S3", "Snowflake", "AzureSqlDb", and
+     "AzureSynapseAnalytics".
     :vartype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
     :ivar target:
     :vartype target: str
@@ -13772,11 +15935,12 @@ def __init__(
         value: Optional[str] = None,
         value_format: Optional[Union[str, "_models.ValueFormat"]] = None,
         credentials: Optional["_models.WorkspaceConnectionManagedIdentity"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword category: Category of the connection. Known values are: "PythonFeed",
-         "ContainerRegistry", and "Git".
+         "ContainerRegistry", "Git", "FeatureStore", "S3", "Snowflake", "AzureSqlDb", and
+         "AzureSynapseAnalytics".
         :paramtype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
         :keyword target:
         :paramtype target: str
@@ -13804,8 +15968,8 @@ class ManagedOnlineDeployment(OnlineDeploymentProperties):  # pylint: disable=to
     :vartype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
     :ivar description: Description of the endpoint deployment.
     :vartype description: str
-    :ivar environment_id: ARM resource ID or AssetId of the environment specification for the
-     endpoint deployment.
+    :ivar environment_id: ARM resource ID of the environment specification for the endpoint
+     deployment.
     :vartype environment_id: str
     :ivar environment_variables: Environment variables configuration for the deployment.
     :vartype environment_variables: dict[str, str]
@@ -13887,15 +16051,15 @@ def __init__(
         readiness_probe: Optional["_models.ProbeSettings"] = None,
         request_settings: Optional["_models.OnlineRequestSettings"] = None,
         scale_settings: Optional["_models.OnlineScaleSettings"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword code_configuration: Code configuration for the endpoint deployment.
         :paramtype code_configuration: ~azure.mgmt.machinelearningservices.models.CodeConfiguration
         :keyword description: Description of the endpoint deployment.
         :paramtype description: str
-        :keyword environment_id: ARM resource ID or AssetId of the environment specification for the
-         endpoint deployment.
+        :keyword environment_id: ARM resource ID of the environment specification for the endpoint
+         deployment.
         :paramtype environment_id: str
         :keyword environment_variables: Environment variables configuration for the deployment.
         :paramtype environment_variables: dict[str, str]
@@ -13990,8 +16154,8 @@ def __init__(
         *,
         type: Union[str, "_models.ManagedServiceIdentityType"],
         user_assigned_identities: Optional[Dict[str, "_models.UserAssignedIdentity"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword type: Type of managed service identity (where both SystemAssigned and UserAssigned
          types are allowed). Required. Known values are: "None", "SystemAssigned", "UserAssigned", and
@@ -14012,7 +16176,8 @@ def __init__(
 
 
 class MedianStoppingPolicy(EarlyTerminationPolicy):
-    """Defines an early termination policy based on running averages of the primary metric of all runs.
+    """Defines an early termination policy based on running averages of the primary metric of all
+    runs.
 
     All required parameters must be populated in order to send to Azure.
 
@@ -14036,7 +16201,7 @@ class MedianStoppingPolicy(EarlyTerminationPolicy):
         "policy_type": {"key": "policyType", "type": "str"},
     }
 
-    def __init__(self, *, delay_evaluation: int = 0, evaluation_interval: int = 0, **kwargs):
+    def __init__(self, *, delay_evaluation: int = 0, evaluation_interval: int = 0, **kwargs: Any) -> None:
         """
         :keyword delay_evaluation: Number of intervals by which to delay the first evaluation.
         :paramtype delay_evaluation: int
@@ -14047,6 +16212,102 @@ def __init__(self, *, delay_evaluation: int = 0, evaluation_interval: int = 0, *
         self.policy_type: str = "MedianStopping"
 
 
+class MLAssistConfiguration(_serialization.Model):
+    """Labeling MLAssist configuration definition.
+
+    You probably want to use the sub-classes and not this class directly. Known sub-classes are:
+    MLAssistConfigurationDisabled, MLAssistConfigurationEnabled
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar ml_assist: [Required] Indicates whether MLAssist feature is enabled. Required. Known
+     values are: "Enabled" and "Disabled".
+    :vartype ml_assist: str or ~azure.mgmt.machinelearningservices.models.MLAssistConfigurationType
+    """
+
+    _validation = {
+        "ml_assist": {"required": True},
+    }
+
+    _attribute_map = {
+        "ml_assist": {"key": "mlAssist", "type": "str"},
+    }
+
+    _subtype_map = {
+        "ml_assist": {"Disabled": "MLAssistConfigurationDisabled", "Enabled": "MLAssistConfigurationEnabled"}
+    }
+
+    def __init__(self, **kwargs: Any) -> None:
+        """ """
+        super().__init__(**kwargs)
+        self.ml_assist: Optional[str] = None
+
+
+class MLAssistConfigurationDisabled(MLAssistConfiguration):
+    """Labeling MLAssist configuration definition when MLAssist is disabled.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar ml_assist: [Required] Indicates whether MLAssist feature is enabled. Required. Known
+     values are: "Enabled" and "Disabled".
+    :vartype ml_assist: str or ~azure.mgmt.machinelearningservices.models.MLAssistConfigurationType
+    """
+
+    _validation = {
+        "ml_assist": {"required": True},
+    }
+
+    _attribute_map = {
+        "ml_assist": {"key": "mlAssist", "type": "str"},
+    }
+
+    def __init__(self, **kwargs: Any) -> None:
+        """ """
+        super().__init__(**kwargs)
+        self.ml_assist: str = "Disabled"
+
+
+class MLAssistConfigurationEnabled(MLAssistConfiguration):
+    """Labeling MLAssist configuration definition when MLAssist is enabled.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar ml_assist: [Required] Indicates whether MLAssist feature is enabled. Required. Known
+     values are: "Enabled" and "Disabled".
+    :vartype ml_assist: str or ~azure.mgmt.machinelearningservices.models.MLAssistConfigurationType
+    :ivar inferencing_compute_binding: [Required] AML compute binding used in inferencing.
+     Required.
+    :vartype inferencing_compute_binding: str
+    :ivar training_compute_binding: [Required] AML compute binding used in training. Required.
+    :vartype training_compute_binding: str
+    """
+
+    _validation = {
+        "ml_assist": {"required": True},
+        "inferencing_compute_binding": {"required": True, "pattern": r"[a-zA-Z0-9_]"},
+        "training_compute_binding": {"required": True, "pattern": r"[a-zA-Z0-9_]"},
+    }
+
+    _attribute_map = {
+        "ml_assist": {"key": "mlAssist", "type": "str"},
+        "inferencing_compute_binding": {"key": "inferencingComputeBinding", "type": "str"},
+        "training_compute_binding": {"key": "trainingComputeBinding", "type": "str"},
+    }
+
+    def __init__(self, *, inferencing_compute_binding: str, training_compute_binding: str, **kwargs: Any) -> None:
+        """
+        :keyword inferencing_compute_binding: [Required] AML compute binding used in inferencing.
+         Required.
+        :paramtype inferencing_compute_binding: str
+        :keyword training_compute_binding: [Required] AML compute binding used in training. Required.
+        :paramtype training_compute_binding: str
+        """
+        super().__init__(**kwargs)
+        self.ml_assist: str = "Enabled"
+        self.inferencing_compute_binding = inferencing_compute_binding
+        self.training_compute_binding = training_compute_binding
+
+
 class MLFlowModelJobInput(AssetJobInput, JobInput):
     """MLFlowModelJobInput.
 
@@ -14083,8 +16344,8 @@ def __init__(
         uri: str,
         description: Optional[str] = None,
         mode: Optional[Union[str, "_models.InputDeliveryMode"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: Description for the input.
         :paramtype description: str
@@ -14111,7 +16372,12 @@ class MLFlowModelJobOutput(AssetJobOutput, JobOutput):
     :ivar job_output_type: [Required] Specifies the type of job. Required. Known values are:
      "uri_file", "uri_folder", "mltable", "custom_model", "mlflow_model", and "triton_model".
     :vartype job_output_type: str or ~azure.mgmt.machinelearningservices.models.JobOutputType
-    :ivar mode: Output Asset Delivery Mode. Known values are: "ReadWriteMount" and "Upload".
+    :ivar asset_name: Output Asset Name.
+    :vartype asset_name: str
+    :ivar asset_version: Output Asset Version.
+    :vartype asset_version: str
+    :ivar mode: Output Asset Delivery Mode. Known values are: "ReadWriteMount", "Upload", and
+     "Direct".
     :vartype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
     :ivar uri: Output Asset URI.
     :vartype uri: str
@@ -14124,6 +16390,8 @@ class MLFlowModelJobOutput(AssetJobOutput, JobOutput):
     _attribute_map = {
         "description": {"key": "description", "type": "str"},
         "job_output_type": {"key": "jobOutputType", "type": "str"},
+        "asset_name": {"key": "assetName", "type": "str"},
+        "asset_version": {"key": "assetVersion", "type": "str"},
         "mode": {"key": "mode", "type": "str"},
         "uri": {"key": "uri", "type": "str"},
     }
@@ -14132,21 +16400,32 @@ def __init__(
         self,
         *,
         description: Optional[str] = None,
+        asset_name: Optional[str] = None,
+        asset_version: Optional[str] = None,
         mode: Optional[Union[str, "_models.OutputDeliveryMode"]] = None,
         uri: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: Description for the output.
         :paramtype description: str
-        :keyword mode: Output Asset Delivery Mode. Known values are: "ReadWriteMount" and "Upload".
+        :keyword asset_name: Output Asset Name.
+        :paramtype asset_name: str
+        :keyword asset_version: Output Asset Version.
+        :paramtype asset_version: str
+        :keyword mode: Output Asset Delivery Mode. Known values are: "ReadWriteMount", "Upload", and
+         "Direct".
         :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
         :keyword uri: Output Asset URI.
         :paramtype uri: str
         """
-        super().__init__(mode=mode, uri=uri, description=description, **kwargs)
+        super().__init__(
+            asset_name=asset_name, asset_version=asset_version, mode=mode, uri=uri, description=description, **kwargs
+        )
         self.description = description
         self.job_output_type: str = "mlflow_model"
+        self.asset_name = asset_name
+        self.asset_version = asset_version
         self.mode = mode
         self.uri = uri
 
@@ -14169,9 +16448,8 @@ class MLTableData(DataVersionBaseProperties):
     :ivar data_type: [Required] Specifies the type of data. Required. Known values are: "uri_file",
      "uri_folder", and "mltable".
     :vartype data_type: str or ~azure.mgmt.machinelearningservices.models.DataType
-    :ivar data_uri: [Required] Uri of the data. Usage/meaning depends on
-     Microsoft.MachineLearning.ManagementFrontEnd.Contracts.V20221001.Assets.DataVersionBase.DataType.
-     Required.
+    :ivar data_uri: [Required] Uri of the data. Example:
+     https://go.microsoft.com/fwlink/?linkid=2202330. Required.
     :vartype data_uri: str
     :ivar referenced_uris: Uris referenced in the MLTable definition (required for lineage).
     :vartype referenced_uris: list[str]
@@ -14203,8 +16481,8 @@ def __init__(
         is_anonymous: bool = False,
         is_archived: bool = False,
         referenced_uris: Optional[List[str]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -14216,9 +16494,8 @@ def __init__(
         :paramtype is_anonymous: bool
         :keyword is_archived: Is the asset archived?.
         :paramtype is_archived: bool
-        :keyword data_uri: [Required] Uri of the data. Usage/meaning depends on
-         Microsoft.MachineLearning.ManagementFrontEnd.Contracts.V20221001.Assets.DataVersionBase.DataType.
-         Required.
+        :keyword data_uri: [Required] Uri of the data. Example:
+         https://go.microsoft.com/fwlink/?linkid=2202330. Required.
         :paramtype data_uri: str
         :keyword referenced_uris: Uris referenced in the MLTable definition (required for lineage).
         :paramtype referenced_uris: list[str]
@@ -14272,8 +16549,8 @@ def __init__(
         uri: str,
         description: Optional[str] = None,
         mode: Optional[Union[str, "_models.InputDeliveryMode"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: Description for the input.
         :paramtype description: str
@@ -14300,7 +16577,12 @@ class MLTableJobOutput(AssetJobOutput, JobOutput):
     :ivar job_output_type: [Required] Specifies the type of job. Required. Known values are:
      "uri_file", "uri_folder", "mltable", "custom_model", "mlflow_model", and "triton_model".
     :vartype job_output_type: str or ~azure.mgmt.machinelearningservices.models.JobOutputType
-    :ivar mode: Output Asset Delivery Mode. Known values are: "ReadWriteMount" and "Upload".
+    :ivar asset_name: Output Asset Name.
+    :vartype asset_name: str
+    :ivar asset_version: Output Asset Version.
+    :vartype asset_version: str
+    :ivar mode: Output Asset Delivery Mode. Known values are: "ReadWriteMount", "Upload", and
+     "Direct".
     :vartype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
     :ivar uri: Output Asset URI.
     :vartype uri: str
@@ -14313,6 +16595,8 @@ class MLTableJobOutput(AssetJobOutput, JobOutput):
     _attribute_map = {
         "description": {"key": "description", "type": "str"},
         "job_output_type": {"key": "jobOutputType", "type": "str"},
+        "asset_name": {"key": "assetName", "type": "str"},
+        "asset_version": {"key": "assetVersion", "type": "str"},
         "mode": {"key": "mode", "type": "str"},
         "uri": {"key": "uri", "type": "str"},
     }
@@ -14321,21 +16605,32 @@ def __init__(
         self,
         *,
         description: Optional[str] = None,
+        asset_name: Optional[str] = None,
+        asset_version: Optional[str] = None,
         mode: Optional[Union[str, "_models.OutputDeliveryMode"]] = None,
         uri: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: Description for the output.
         :paramtype description: str
-        :keyword mode: Output Asset Delivery Mode. Known values are: "ReadWriteMount" and "Upload".
+        :keyword asset_name: Output Asset Name.
+        :paramtype asset_name: str
+        :keyword asset_version: Output Asset Version.
+        :paramtype asset_version: str
+        :keyword mode: Output Asset Delivery Mode. Known values are: "ReadWriteMount", "Upload", and
+         "Direct".
         :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
         :keyword uri: Output Asset URI.
         :paramtype uri: str
         """
-        super().__init__(mode=mode, uri=uri, description=description, **kwargs)
+        super().__init__(
+            asset_name=asset_name, asset_version=asset_version, mode=mode, uri=uri, description=description, **kwargs
+        )
         self.description = description
         self.job_output_type: str = "mltable"
+        self.asset_name = asset_name
+        self.asset_version = asset_version
         self.mode = mode
         self.uri = uri
 
@@ -14378,7 +16673,7 @@ class ModelContainer(Resource):
         "properties": {"key": "properties", "type": "ModelContainerProperties"},
     }
 
-    def __init__(self, *, properties: "_models.ModelContainerProperties", **kwargs):
+    def __init__(self, *, properties: "_models.ModelContainerProperties", **kwargs: Any) -> None:
         """
         :keyword properties: [Required] Additional attributes of the entity. Required.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.ModelContainerProperties
@@ -14404,11 +16699,16 @@ class ModelContainerProperties(AssetContainer):
     :vartype latest_version: str
     :ivar next_version: The next auto incremental version.
     :vartype next_version: str
+    :ivar provisioning_state: Provisioning state for the model container. Known values are:
+     "Succeeded", "Failed", "Canceled", "Creating", "Updating", and "Deleting".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.AssetProvisioningState
     """
 
     _validation = {
         "latest_version": {"readonly": True},
         "next_version": {"readonly": True},
+        "provisioning_state": {"readonly": True},
     }
 
     _attribute_map = {
@@ -14418,6 +16718,7 @@ class ModelContainerProperties(AssetContainer):
         "is_archived": {"key": "isArchived", "type": "bool"},
         "latest_version": {"key": "latestVersion", "type": "str"},
         "next_version": {"key": "nextVersion", "type": "str"},
+        "provisioning_state": {"key": "provisioningState", "type": "str"},
     }
 
     def __init__(
@@ -14427,8 +16728,8 @@ def __init__(
         properties: Optional[Dict[str, str]] = None,
         tags: Optional[Dict[str, str]] = None,
         is_archived: bool = False,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -14440,6 +16741,7 @@ def __init__(
         :paramtype is_archived: bool
         """
         super().__init__(description=description, properties=properties, tags=tags, is_archived=is_archived, **kwargs)
+        self.provisioning_state = None
 
 
 class ModelContainerResourceArmPaginatedResult(_serialization.Model):
@@ -14458,8 +16760,8 @@ class ModelContainerResourceArmPaginatedResult(_serialization.Model):
     }
 
     def __init__(
-        self, *, next_link: Optional[str] = None, value: Optional[List["_models.ModelContainer"]] = None, **kwargs
-    ):
+        self, *, next_link: Optional[str] = None, value: Optional[List["_models.ModelContainer"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of ModelContainer objects. If null, there are no
          additional pages.
@@ -14510,7 +16812,7 @@ class ModelVersion(Resource):
         "properties": {"key": "properties", "type": "ModelVersionProperties"},
     }
 
-    def __init__(self, *, properties: "_models.ModelVersionProperties", **kwargs):
+    def __init__(self, *, properties: "_models.ModelVersionProperties", **kwargs: Any) -> None:
         """
         :keyword properties: [Required] Additional attributes of the entity. Required.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.ModelVersionProperties
@@ -14522,6 +16824,8 @@ def __init__(self, *, properties: "_models.ModelVersionProperties", **kwargs):
 class ModelVersionProperties(AssetBase):
     """Model asset version details.
 
+    Variables are only populated by the server, and will be ignored when sending a request.
+
     :ivar description: The asset description text.
     :vartype description: str
     :ivar properties: The asset property dictionary.
@@ -14540,8 +16844,16 @@ class ModelVersionProperties(AssetBase):
     :vartype model_type: str
     :ivar model_uri: The URI path to the model contents.
     :vartype model_uri: str
+    :ivar provisioning_state: Provisioning state for the model version. Known values are:
+     "Succeeded", "Failed", "Canceled", "Creating", "Updating", and "Deleting".
+    :vartype provisioning_state: str or
+     ~azure.mgmt.machinelearningservices.models.AssetProvisioningState
     """
 
+    _validation = {
+        "provisioning_state": {"readonly": True},
+    }
+
     _attribute_map = {
         "description": {"key": "description", "type": "str"},
         "properties": {"key": "properties", "type": "{str}"},
@@ -14552,6 +16864,7 @@ class ModelVersionProperties(AssetBase):
         "job_name": {"key": "jobName", "type": "str"},
         "model_type": {"key": "modelType", "type": "str"},
         "model_uri": {"key": "modelUri", "type": "str"},
+        "provisioning_state": {"key": "provisioningState", "type": "str"},
     }
 
     def __init__(
@@ -14566,8 +16879,8 @@ def __init__(
         job_name: Optional[str] = None,
         model_type: Optional[str] = None,
         model_uri: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -14600,6 +16913,7 @@ def __init__(
         self.job_name = job_name
         self.model_type = model_type
         self.model_uri = model_uri
+        self.provisioning_state = None
 
 
 class ModelVersionResourceArmPaginatedResult(_serialization.Model):
@@ -14618,8 +16932,8 @@ class ModelVersionResourceArmPaginatedResult(_serialization.Model):
     }
 
     def __init__(
-        self, *, next_link: Optional[str] = None, value: Optional[List["_models.ModelVersion"]] = None, **kwargs
-    ):
+        self, *, next_link: Optional[str] = None, value: Optional[List["_models.ModelVersion"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of ModelVersion objects. If null, there are no
          additional pages.
@@ -14653,32 +16967,259 @@ class Mpi(DistributionConfiguration):
         "process_count_per_instance": {"key": "processCountPerInstance", "type": "int"},
     }
 
-    def __init__(self, *, process_count_per_instance: Optional[int] = None, **kwargs):
+    def __init__(self, *, process_count_per_instance: Optional[int] = None, **kwargs: Any) -> None:
+        """
+        :keyword process_count_per_instance: Number of processes per MPI node.
+        :paramtype process_count_per_instance: int
+        """
+        super().__init__(**kwargs)
+        self.distribution_type: str = "Mpi"
+        self.process_count_per_instance = process_count_per_instance
+
+
+class NlpFixedParameters(_serialization.Model):
+    """Fixed training parameters that won't be swept over during AutoML NLP training.
+
+    :ivar gradient_accumulation_steps: Number of steps to accumulate gradients over before running
+     a backward pass.
+    :vartype gradient_accumulation_steps: int
+    :ivar learning_rate: The learning rate for the training procedure.
+    :vartype learning_rate: float
+    :ivar learning_rate_scheduler: The type of learning rate schedule to use during the training
+     procedure. Known values are: "None", "Linear", "Cosine", "CosineWithRestarts", "Polynomial",
+     "Constant", and "ConstantWithWarmup".
+    :vartype learning_rate_scheduler: str or
+     ~azure.mgmt.machinelearningservices.models.NlpLearningRateScheduler
+    :ivar model_name: The name of the model to train.
+    :vartype model_name: str
+    :ivar number_of_epochs: Number of training epochs.
+    :vartype number_of_epochs: int
+    :ivar training_batch_size: The batch size for the training procedure.
+    :vartype training_batch_size: int
+    :ivar validation_batch_size: The batch size to be used during evaluation.
+    :vartype validation_batch_size: int
+    :ivar warmup_ratio: The warmup ratio, used alongside LrSchedulerType.
+    :vartype warmup_ratio: float
+    :ivar weight_decay: The weight decay for the training procedure.
+    :vartype weight_decay: float
+    """
+
+    _attribute_map = {
+        "gradient_accumulation_steps": {"key": "gradientAccumulationSteps", "type": "int"},
+        "learning_rate": {"key": "learningRate", "type": "float"},
+        "learning_rate_scheduler": {"key": "learningRateScheduler", "type": "str"},
+        "model_name": {"key": "modelName", "type": "str"},
+        "number_of_epochs": {"key": "numberOfEpochs", "type": "int"},
+        "training_batch_size": {"key": "trainingBatchSize", "type": "int"},
+        "validation_batch_size": {"key": "validationBatchSize", "type": "int"},
+        "warmup_ratio": {"key": "warmupRatio", "type": "float"},
+        "weight_decay": {"key": "weightDecay", "type": "float"},
+    }
+
+    def __init__(
+        self,
+        *,
+        gradient_accumulation_steps: Optional[int] = None,
+        learning_rate: Optional[float] = None,
+        learning_rate_scheduler: Optional[Union[str, "_models.NlpLearningRateScheduler"]] = None,
+        model_name: Optional[str] = None,
+        number_of_epochs: Optional[int] = None,
+        training_batch_size: Optional[int] = None,
+        validation_batch_size: Optional[int] = None,
+        warmup_ratio: Optional[float] = None,
+        weight_decay: Optional[float] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword gradient_accumulation_steps: Number of steps to accumulate gradients over before
+         running a backward pass.
+        :paramtype gradient_accumulation_steps: int
+        :keyword learning_rate: The learning rate for the training procedure.
+        :paramtype learning_rate: float
+        :keyword learning_rate_scheduler: The type of learning rate schedule to use during the training
+         procedure. Known values are: "None", "Linear", "Cosine", "CosineWithRestarts", "Polynomial",
+         "Constant", and "ConstantWithWarmup".
+        :paramtype learning_rate_scheduler: str or
+         ~azure.mgmt.machinelearningservices.models.NlpLearningRateScheduler
+        :keyword model_name: The name of the model to train.
+        :paramtype model_name: str
+        :keyword number_of_epochs: Number of training epochs.
+        :paramtype number_of_epochs: int
+        :keyword training_batch_size: The batch size for the training procedure.
+        :paramtype training_batch_size: int
+        :keyword validation_batch_size: The batch size to be used during evaluation.
+        :paramtype validation_batch_size: int
+        :keyword warmup_ratio: The warmup ratio, used alongside LrSchedulerType.
+        :paramtype warmup_ratio: float
+        :keyword weight_decay: The weight decay for the training procedure.
+        :paramtype weight_decay: float
+        """
+        super().__init__(**kwargs)
+        self.gradient_accumulation_steps = gradient_accumulation_steps
+        self.learning_rate = learning_rate
+        self.learning_rate_scheduler = learning_rate_scheduler
+        self.model_name = model_name
+        self.number_of_epochs = number_of_epochs
+        self.training_batch_size = training_batch_size
+        self.validation_batch_size = validation_batch_size
+        self.warmup_ratio = warmup_ratio
+        self.weight_decay = weight_decay
+
+
+class NlpParameterSubspace(_serialization.Model):
+    """Stringified search spaces for each parameter. See below examples.
+
+    :ivar gradient_accumulation_steps: Number of steps to accumulate gradients over before running
+     a backward pass.
+    :vartype gradient_accumulation_steps: str
+    :ivar learning_rate: The learning rate for the training procedure.
+    :vartype learning_rate: str
+    :ivar learning_rate_scheduler: The type of learning rate schedule to use during the training
+     procedure.
+    :vartype learning_rate_scheduler: str
+    :ivar model_name: The name of the model to train.
+    :vartype model_name: str
+    :ivar number_of_epochs: Number of training epochs.
+    :vartype number_of_epochs: str
+    :ivar training_batch_size: The batch size for the training procedure.
+    :vartype training_batch_size: str
+    :ivar validation_batch_size: The batch size to be used during evaluation.
+    :vartype validation_batch_size: str
+    :ivar warmup_ratio: The warmup ratio, used alongside LrSchedulerType.
+    :vartype warmup_ratio: str
+    :ivar weight_decay: The weight decay for the training procedure.
+    :vartype weight_decay: str
+    """
+
+    _attribute_map = {
+        "gradient_accumulation_steps": {"key": "gradientAccumulationSteps", "type": "str"},
+        "learning_rate": {"key": "learningRate", "type": "str"},
+        "learning_rate_scheduler": {"key": "learningRateScheduler", "type": "str"},
+        "model_name": {"key": "modelName", "type": "str"},
+        "number_of_epochs": {"key": "numberOfEpochs", "type": "str"},
+        "training_batch_size": {"key": "trainingBatchSize", "type": "str"},
+        "validation_batch_size": {"key": "validationBatchSize", "type": "str"},
+        "warmup_ratio": {"key": "warmupRatio", "type": "str"},
+        "weight_decay": {"key": "weightDecay", "type": "str"},
+    }
+
+    def __init__(
+        self,
+        *,
+        gradient_accumulation_steps: Optional[str] = None,
+        learning_rate: Optional[str] = None,
+        learning_rate_scheduler: Optional[str] = None,
+        model_name: Optional[str] = None,
+        number_of_epochs: Optional[str] = None,
+        training_batch_size: Optional[str] = None,
+        validation_batch_size: Optional[str] = None,
+        warmup_ratio: Optional[str] = None,
+        weight_decay: Optional[str] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword gradient_accumulation_steps: Number of steps to accumulate gradients over before
+         running a backward pass.
+        :paramtype gradient_accumulation_steps: str
+        :keyword learning_rate: The learning rate for the training procedure.
+        :paramtype learning_rate: str
+        :keyword learning_rate_scheduler: The type of learning rate schedule to use during the training
+         procedure.
+        :paramtype learning_rate_scheduler: str
+        :keyword model_name: The name of the model to train.
+        :paramtype model_name: str
+        :keyword number_of_epochs: Number of training epochs.
+        :paramtype number_of_epochs: str
+        :keyword training_batch_size: The batch size for the training procedure.
+        :paramtype training_batch_size: str
+        :keyword validation_batch_size: The batch size to be used during evaluation.
+        :paramtype validation_batch_size: str
+        :keyword warmup_ratio: The warmup ratio, used alongside LrSchedulerType.
+        :paramtype warmup_ratio: str
+        :keyword weight_decay: The weight decay for the training procedure.
+        :paramtype weight_decay: str
+        """
+        super().__init__(**kwargs)
+        self.gradient_accumulation_steps = gradient_accumulation_steps
+        self.learning_rate = learning_rate
+        self.learning_rate_scheduler = learning_rate_scheduler
+        self.model_name = model_name
+        self.number_of_epochs = number_of_epochs
+        self.training_batch_size = training_batch_size
+        self.validation_batch_size = validation_batch_size
+        self.warmup_ratio = warmup_ratio
+        self.weight_decay = weight_decay
+
+
+class NlpSweepSettings(_serialization.Model):
+    """Model sweeping and hyperparameter tuning related settings.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar early_termination: Type of early termination policy for the sweeping job.
+    :vartype early_termination: ~azure.mgmt.machinelearningservices.models.EarlyTerminationPolicy
+    :ivar sampling_algorithm: [Required] Type of sampling algorithm. Required. Known values are:
+     "Grid", "Random", and "Bayesian".
+    :vartype sampling_algorithm: str or
+     ~azure.mgmt.machinelearningservices.models.SamplingAlgorithmType
+    """
+
+    _validation = {
+        "sampling_algorithm": {"required": True},
+    }
+
+    _attribute_map = {
+        "early_termination": {"key": "earlyTermination", "type": "EarlyTerminationPolicy"},
+        "sampling_algorithm": {"key": "samplingAlgorithm", "type": "str"},
+    }
+
+    def __init__(
+        self,
+        *,
+        sampling_algorithm: Union[str, "_models.SamplingAlgorithmType"],
+        early_termination: Optional["_models.EarlyTerminationPolicy"] = None,
+        **kwargs: Any
+    ) -> None:
         """
-        :keyword process_count_per_instance: Number of processes per MPI node.
-        :paramtype process_count_per_instance: int
+        :keyword early_termination: Type of early termination policy for the sweeping job.
+        :paramtype early_termination: ~azure.mgmt.machinelearningservices.models.EarlyTerminationPolicy
+        :keyword sampling_algorithm: [Required] Type of sampling algorithm. Required. Known values are:
+         "Grid", "Random", and "Bayesian".
+        :paramtype sampling_algorithm: str or
+         ~azure.mgmt.machinelearningservices.models.SamplingAlgorithmType
         """
         super().__init__(**kwargs)
-        self.distribution_type: str = "Mpi"
-        self.process_count_per_instance = process_count_per_instance
+        self.early_termination = early_termination
+        self.sampling_algorithm = sampling_algorithm
 
 
 class NlpVertical(_serialization.Model):
     """Abstract class for NLP related AutoML tasks.
     NLP - Natural Language Processing.
 
-        :ivar featurization_settings: Featurization inputs needed for AutoML job.
-        :vartype featurization_settings:
-         ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
-        :ivar limit_settings: Execution constraints for AutoMLJob.
-        :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
-        :ivar validation_data: Validation data inputs.
-        :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar featurization_settings: Featurization inputs needed for AutoML job.
+    :vartype featurization_settings:
+     ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
+    :ivar fixed_parameters: Model/training parameters that will remain constant throughout
+     training.
+    :vartype fixed_parameters: ~azure.mgmt.machinelearningservices.models.NlpFixedParameters
+    :ivar limit_settings: Execution constraints for AutoMLJob.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
+    :ivar search_space: Search space for sampling different combinations of models and their
+     hyperparameters.
+    :vartype search_space: list[~azure.mgmt.machinelearningservices.models.NlpParameterSubspace]
+    :ivar sweep_settings: Settings for model sweeping and hyperparameter tuning.
+    :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.NlpSweepSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
     """
 
     _attribute_map = {
         "featurization_settings": {"key": "featurizationSettings", "type": "NlpVerticalFeaturizationSettings"},
+        "fixed_parameters": {"key": "fixedParameters", "type": "NlpFixedParameters"},
         "limit_settings": {"key": "limitSettings", "type": "NlpVerticalLimitSettings"},
+        "search_space": {"key": "searchSpace", "type": "[NlpParameterSubspace]"},
+        "sweep_settings": {"key": "sweepSettings", "type": "NlpSweepSettings"},
         "validation_data": {"key": "validationData", "type": "MLTableJobInput"},
     }
 
@@ -14686,22 +17227,36 @@ def __init__(
         self,
         *,
         featurization_settings: Optional["_models.NlpVerticalFeaturizationSettings"] = None,
+        fixed_parameters: Optional["_models.NlpFixedParameters"] = None,
         limit_settings: Optional["_models.NlpVerticalLimitSettings"] = None,
+        search_space: Optional[List["_models.NlpParameterSubspace"]] = None,
+        sweep_settings: Optional["_models.NlpSweepSettings"] = None,
         validation_data: Optional["_models.MLTableJobInput"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword featurization_settings: Featurization inputs needed for AutoML job.
         :paramtype featurization_settings:
          ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
+        :keyword fixed_parameters: Model/training parameters that will remain constant throughout
+         training.
+        :paramtype fixed_parameters: ~azure.mgmt.machinelearningservices.models.NlpFixedParameters
         :keyword limit_settings: Execution constraints for AutoMLJob.
         :paramtype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
+        :keyword search_space: Search space for sampling different combinations of models and their
+         hyperparameters.
+        :paramtype search_space: list[~azure.mgmt.machinelearningservices.models.NlpParameterSubspace]
+        :keyword sweep_settings: Settings for model sweeping and hyperparameter tuning.
+        :paramtype sweep_settings: ~azure.mgmt.machinelearningservices.models.NlpSweepSettings
         :keyword validation_data: Validation data inputs.
         :paramtype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
         """
         super().__init__(**kwargs)
         self.featurization_settings = featurization_settings
+        self.fixed_parameters = fixed_parameters
         self.limit_settings = limit_settings
+        self.search_space = search_space
+        self.sweep_settings = sweep_settings
         self.validation_data = validation_data
 
 
@@ -14716,7 +17271,7 @@ class NlpVerticalFeaturizationSettings(FeaturizationSettings):
         "dataset_language": {"key": "datasetLanguage", "type": "str"},
     }
 
-    def __init__(self, *, dataset_language: Optional[str] = None, **kwargs):
+    def __init__(self, *, dataset_language: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword dataset_language: Dataset language, useful for the text data.
         :paramtype dataset_language: str
@@ -14729,38 +17284,52 @@ class NlpVerticalLimitSettings(_serialization.Model):
 
     :ivar max_concurrent_trials: Maximum Concurrent AutoML iterations.
     :vartype max_concurrent_trials: int
+    :ivar max_nodes: Maximum nodes to use for the experiment.
+    :vartype max_nodes: int
     :ivar max_trials: Number of AutoML iterations.
     :vartype max_trials: int
     :ivar timeout: AutoML job timeout.
     :vartype timeout: ~datetime.timedelta
+    :ivar trial_timeout: Timeout for individual HD trials.
+    :vartype trial_timeout: ~datetime.timedelta
     """
 
     _attribute_map = {
         "max_concurrent_trials": {"key": "maxConcurrentTrials", "type": "int"},
+        "max_nodes": {"key": "maxNodes", "type": "int"},
         "max_trials": {"key": "maxTrials", "type": "int"},
         "timeout": {"key": "timeout", "type": "duration"},
+        "trial_timeout": {"key": "trialTimeout", "type": "duration"},
     }
 
     def __init__(
         self,
         *,
         max_concurrent_trials: int = 1,
+        max_nodes: int = 1,
         max_trials: int = 1,
-        timeout: Optional[datetime.timedelta] = None,
-        **kwargs
-    ):
+        timeout: datetime.timedelta = "P7D",
+        trial_timeout: Optional[datetime.timedelta] = None,
+        **kwargs: Any
+    ) -> None:
         """
         :keyword max_concurrent_trials: Maximum Concurrent AutoML iterations.
         :paramtype max_concurrent_trials: int
+        :keyword max_nodes: Maximum nodes to use for the experiment.
+        :paramtype max_nodes: int
         :keyword max_trials: Number of AutoML iterations.
         :paramtype max_trials: int
         :keyword timeout: AutoML job timeout.
         :paramtype timeout: ~datetime.timedelta
+        :keyword trial_timeout: Timeout for individual HD trials.
+        :paramtype trial_timeout: ~datetime.timedelta
         """
         super().__init__(**kwargs)
         self.max_concurrent_trials = max_concurrent_trials
+        self.max_nodes = max_nodes
         self.max_trials = max_trials
         self.timeout = timeout
+        self.trial_timeout = trial_timeout
 
 
 class NodeStateCounts(_serialization.Model):
@@ -14800,7 +17369,7 @@ class NodeStateCounts(_serialization.Model):
         "preempted_node_count": {"key": "preemptedNodeCount", "type": "int"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.idle_node_count = None
@@ -14817,10 +17386,12 @@ class NoneAuthTypeWorkspaceConnectionProperties(WorkspaceConnectionPropertiesV2)
     All required parameters must be populated in order to send to Azure.
 
     :ivar auth_type: Authentication type of the connection target. Required. Known values are:
-     "PAT", "ManagedIdentity", "UsernamePassword", "None", and "SAS".
+     "PAT", "ManagedIdentity", "UsernamePassword", "None", "SAS", "ServicePrincipal", and
+     "AccessKey".
     :vartype auth_type: str or ~azure.mgmt.machinelearningservices.models.ConnectionAuthType
     :ivar category: Category of the connection. Known values are: "PythonFeed",
-     "ContainerRegistry", and "Git".
+     "ContainerRegistry", "Git", "FeatureStore", "S3", "Snowflake", "AzureSqlDb", and
+     "AzureSynapseAnalytics".
     :vartype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
     :ivar target:
     :vartype target: str
@@ -14849,11 +17420,12 @@ def __init__(
         target: Optional[str] = None,
         value: Optional[str] = None,
         value_format: Optional[Union[str, "_models.ValueFormat"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword category: Category of the connection. Known values are: "PythonFeed",
-         "ContainerRegistry", and "Git".
+         "ContainerRegistry", "Git", "FeatureStore", "S3", "Snowflake", "AzureSqlDb", and
+         "AzureSynapseAnalytics".
         :paramtype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
         :keyword target:
         :paramtype target: str
@@ -14872,7 +17444,8 @@ class NoneDatastoreCredentials(DatastoreCredentials):
     All required parameters must be populated in order to send to Azure.
 
     :ivar credentials_type: [Required] Credential type used to authentication with storage.
-     Required. Known values are: "AccountKey", "Certificate", "None", "Sas", and "ServicePrincipal".
+     Required. Known values are: "AccountKey", "Certificate", "None", "Sas", "ServicePrincipal",
+     "KerberosKeytab", and "KerberosPassword".
     :vartype credentials_type: str or ~azure.mgmt.machinelearningservices.models.CredentialsType
     """
 
@@ -14884,7 +17457,7 @@ class NoneDatastoreCredentials(DatastoreCredentials):
         "credentials_type": {"key": "credentialsType", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.credentials_type: str = "None"
@@ -14935,7 +17508,7 @@ class NotebookAccessTokenResult(_serialization.Model):
         "scope": {"key": "scope", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.notebook_resource_id = None
@@ -14962,7 +17535,9 @@ class NotebookPreparationError(_serialization.Model):
         "status_code": {"key": "statusCode", "type": "int"},
     }
 
-    def __init__(self, *, error_message: Optional[str] = None, status_code: Optional[int] = None, **kwargs):
+    def __init__(
+        self, *, error_message: Optional[str] = None, status_code: Optional[int] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword error_message:
         :paramtype error_message: str
@@ -14998,8 +17573,8 @@ def __init__(
         fqdn: Optional[str] = None,
         resource_id: Optional[str] = None,
         notebook_preparation_error: Optional["_models.NotebookPreparationError"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword fqdn:
         :paramtype fqdn: str
@@ -15037,7 +17612,7 @@ class Objective(_serialization.Model):
         "primary_metric": {"key": "primaryMetric", "type": "str"},
     }
 
-    def __init__(self, *, goal: Union[str, "_models.Goal"], primary_metric: str, **kwargs):
+    def __init__(self, *, goal: Union[str, "_models.Goal"], primary_metric: str, **kwargs: Any) -> None:
         """
         :keyword goal: [Required] Defines supported metric goals for hyperparameter tuning. Required.
          Known values are: "Minimize" and "Maximize".
@@ -15114,8 +17689,8 @@ def __init__(
         identity: Optional["_models.ManagedServiceIdentity"] = None,
         kind: Optional[str] = None,
         sku: Optional["_models.Sku"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword tags: Resource tags.
         :paramtype tags: dict[str, str]
@@ -15154,8 +17729,12 @@ class OnlineDeploymentTrackedResourceArmPaginatedResult(_serialization.Model):
     }
 
     def __init__(
-        self, *, next_link: Optional[str] = None, value: Optional[List["_models.OnlineDeployment"]] = None, **kwargs
-    ):
+        self,
+        *,
+        next_link: Optional[str] = None,
+        value: Optional[List["_models.OnlineDeployment"]] = None,
+        **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of OnlineDeployment objects. If null, there are
          no additional pages.
@@ -15232,8 +17811,8 @@ def __init__(
         identity: Optional["_models.ManagedServiceIdentity"] = None,
         kind: Optional[str] = None,
         sku: Optional["_models.Sku"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword tags: Resource tags.
         :paramtype tags: dict[str, str]
@@ -15256,7 +17835,7 @@ def __init__(
         self.sku = sku
 
 
-class OnlineEndpointProperties(EndpointPropertiesBase):
+class OnlineEndpointProperties(EndpointPropertiesBase):  # pylint: disable=too-many-instance-attributes
     """Online endpoint configuration.
 
     Variables are only populated by the server, and will be ignored when sending a request.
@@ -15282,6 +17861,9 @@ class OnlineEndpointProperties(EndpointPropertiesBase):
     :ivar compute: ARM resource ID of the compute if it exists.
      optional.
     :vartype compute: str
+    :ivar mirror_traffic: Percentage of traffic to be mirrored to each deployment without using
+     returned scoring. Traffic values need to sum to utmost 50.
+    :vartype mirror_traffic: dict[str, int]
     :ivar provisioning_state: Provisioning state for the endpoint. Known values are: "Creating",
      "Deleting", "Succeeded", "Failed", "Updating", and "Canceled".
     :vartype provisioning_state: str or
@@ -15310,6 +17892,7 @@ class OnlineEndpointProperties(EndpointPropertiesBase):
         "scoring_uri": {"key": "scoringUri", "type": "str"},
         "swagger_uri": {"key": "swaggerUri", "type": "str"},
         "compute": {"key": "compute", "type": "str"},
+        "mirror_traffic": {"key": "mirrorTraffic", "type": "{int}"},
         "provisioning_state": {"key": "provisioningState", "type": "str"},
         "public_network_access": {"key": "publicNetworkAccess", "type": "str"},
         "traffic": {"key": "traffic", "type": "{int}"},
@@ -15323,10 +17906,11 @@ def __init__(
         keys: Optional["_models.EndpointAuthKeys"] = None,
         properties: Optional[Dict[str, str]] = None,
         compute: Optional[str] = None,
+        mirror_traffic: Optional[Dict[str, int]] = None,
         public_network_access: Optional[Union[str, "_models.PublicNetworkAccessType"]] = None,
         traffic: Optional[Dict[str, int]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword auth_mode: [Required] Use 'Key' for key based authentication and 'AMLToken' for Azure
          Machine Learning token-based authentication. 'Key' doesn't expire but 'AMLToken' does.
@@ -15343,6 +17927,9 @@ def __init__(
         :keyword compute: ARM resource ID of the compute if it exists.
          optional.
         :paramtype compute: str
+        :keyword mirror_traffic: Percentage of traffic to be mirrored to each deployment without using
+         returned scoring. Traffic values need to sum to utmost 50.
+        :paramtype mirror_traffic: dict[str, int]
         :keyword public_network_access: Set to "Enabled" for endpoints that should allow public access
          when Private Link is enabled. Known values are: "Enabled" and "Disabled".
         :paramtype public_network_access: str or
@@ -15353,6 +17940,7 @@ def __init__(
         """
         super().__init__(auth_mode=auth_mode, description=description, keys=keys, properties=properties, **kwargs)
         self.compute = compute
+        self.mirror_traffic = mirror_traffic
         self.provisioning_state = None
         self.public_network_access = public_network_access
         self.traffic = traffic
@@ -15374,8 +17962,8 @@ class OnlineEndpointTrackedResourceArmPaginatedResult(_serialization.Model):
     }
 
     def __init__(
-        self, *, next_link: Optional[str] = None, value: Optional[List["_models.OnlineEndpoint"]] = None, **kwargs
-    ):
+        self, *, next_link: Optional[str] = None, value: Optional[List["_models.OnlineEndpoint"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of OnlineEndpoint objects. If null, there are no
          additional pages.
@@ -15415,8 +18003,8 @@ def __init__(
         max_concurrent_requests_per_instance: int = 1,
         max_queue_wait: datetime.timedelta = "PT0.5S",
         request_timeout: datetime.timedelta = "PT5S",
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword max_concurrent_requests_per_instance: The number of maximum concurrent requests per
          node allowed per deployment. Defaults to 1.
@@ -15459,7 +18047,7 @@ class OutputPathAssetReference(AssetReferenceBase):
         "path": {"key": "path", "type": "str"},
     }
 
-    def __init__(self, *, job_id: Optional[str] = None, path: Optional[str] = None, **kwargs):
+    def __init__(self, *, job_id: Optional[str] = None, path: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword job_id: ARM resource ID of the job.
         :paramtype job_id: str
@@ -15487,8 +18075,8 @@ class PaginatedComputeResourcesList(_serialization.Model):
     }
 
     def __init__(
-        self, *, value: Optional[List["_models.ComputeResource"]] = None, next_link: Optional[str] = None, **kwargs
-    ):
+        self, *, value: Optional[List["_models.ComputeResource"]] = None, next_link: Optional[str] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword value: An array of Machine Learning compute objects wrapped in ARM resource envelope.
         :paramtype value: list[~azure.mgmt.machinelearningservices.models.ComputeResource]
@@ -15511,7 +18099,7 @@ class PartialBatchDeployment(_serialization.Model):
         "description": {"key": "description", "type": "str"},
     }
 
-    def __init__(self, *, description: Optional[str] = None, **kwargs):
+    def __init__(self, *, description: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword description: Description of the endpoint deployment.
         :paramtype description: str
@@ -15539,8 +18127,8 @@ def __init__(
         *,
         properties: Optional["_models.PartialBatchDeployment"] = None,
         tags: Optional[Dict[str, str]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword properties: Additional attributes of the entity.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.PartialBatchDeployment
@@ -15575,8 +18163,8 @@ def __init__(
         *,
         type: Optional[Union[str, "_models.ManagedServiceIdentityType"]] = None,
         user_assigned_identities: Optional[Dict[str, JSON]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword type: Managed service identity (system assigned and/or user assigned identities).
          Known values are: "None", "SystemAssigned", "UserAssigned", and "SystemAssigned,UserAssigned".
@@ -15603,7 +18191,7 @@ class PartialMinimalTrackedResource(_serialization.Model):
         "tags": {"key": "tags", "type": "{str}"},
     }
 
-    def __init__(self, *, tags: Optional[Dict[str, str]] = None, **kwargs):
+    def __init__(self, *, tags: Optional[Dict[str, str]] = None, **kwargs: Any) -> None:
         """
         :keyword tags: Resource tags.
         :paramtype tags: dict[str, str]
@@ -15631,8 +18219,8 @@ def __init__(
         *,
         tags: Optional[Dict[str, str]] = None,
         identity: Optional["_models.PartialManagedServiceIdentity"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword tags: Resource tags.
         :paramtype tags: dict[str, str]
@@ -15657,7 +18245,9 @@ class PartialMinimalTrackedResourceWithSku(PartialMinimalTrackedResource):
         "sku": {"key": "sku", "type": "PartialSku"},
     }
 
-    def __init__(self, *, tags: Optional[Dict[str, str]] = None, sku: Optional["_models.PartialSku"] = None, **kwargs):
+    def __init__(
+        self, *, tags: Optional[Dict[str, str]] = None, sku: Optional["_models.PartialSku"] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword tags: Resource tags.
         :paramtype tags: dict[str, str]
@@ -15668,6 +18258,61 @@ def __init__(self, *, tags: Optional[Dict[str, str]] = None, sku: Optional["_mod
         self.sku = sku
 
 
+class PartialRegistryPartialTrackedResource(_serialization.Model):
+    """Strictly used in update requests.
+
+    :ivar identity: Managed service identity (system assigned and/or user assigned identities).
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.PartialManagedServiceIdentity
+    :ivar kind: Metadata used by portal/tooling/etc to render different UX experiences for
+     resources of the same type.
+    :vartype kind: str
+    :ivar properties: Additional attributes of the entity.
+    :vartype properties: JSON
+    :ivar sku: Sku details required for ARM contract for Autoscaling.
+    :vartype sku: ~azure.mgmt.machinelearningservices.models.PartialSku
+    :ivar tags: Resource tags.
+    :vartype tags: dict[str, str]
+    """
+
+    _attribute_map = {
+        "identity": {"key": "identity", "type": "PartialManagedServiceIdentity"},
+        "kind": {"key": "kind", "type": "str"},
+        "properties": {"key": "properties", "type": "object"},
+        "sku": {"key": "sku", "type": "PartialSku"},
+        "tags": {"key": "tags", "type": "{str}"},
+    }
+
+    def __init__(
+        self,
+        *,
+        identity: Optional["_models.PartialManagedServiceIdentity"] = None,
+        kind: Optional[str] = None,
+        properties: Optional[JSON] = None,
+        sku: Optional["_models.PartialSku"] = None,
+        tags: Optional[Dict[str, str]] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword identity: Managed service identity (system assigned and/or user assigned identities).
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.PartialManagedServiceIdentity
+        :keyword kind: Metadata used by portal/tooling/etc to render different UX experiences for
+         resources of the same type.
+        :paramtype kind: str
+        :keyword properties: Additional attributes of the entity.
+        :paramtype properties: JSON
+        :keyword sku: Sku details required for ARM contract for Autoscaling.
+        :paramtype sku: ~azure.mgmt.machinelearningservices.models.PartialSku
+        :keyword tags: Resource tags.
+        :paramtype tags: dict[str, str]
+        """
+        super().__init__(**kwargs)
+        self.identity = identity
+        self.kind = kind
+        self.properties = properties
+        self.sku = sku
+        self.tags = tags
+
+
 class PartialSku(_serialization.Model):
     """Common SKU definition.
 
@@ -15704,8 +18349,8 @@ def __init__(
         name: Optional[str] = None,
         size: Optional[str] = None,
         tier: Optional[Union[str, "_models.SkuTier"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword capacity: If the SKU supports scale out/in then the capacity integer should be
          included. If scale out/in is not possible for the resource this may be omitted.
@@ -15752,7 +18397,7 @@ class Password(_serialization.Model):
         "value": {"key": "value", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.name = None
@@ -15765,10 +18410,12 @@ class PATAuthTypeWorkspaceConnectionProperties(WorkspaceConnectionPropertiesV2):
     All required parameters must be populated in order to send to Azure.
 
     :ivar auth_type: Authentication type of the connection target. Required. Known values are:
-     "PAT", "ManagedIdentity", "UsernamePassword", "None", and "SAS".
+     "PAT", "ManagedIdentity", "UsernamePassword", "None", "SAS", "ServicePrincipal", and
+     "AccessKey".
     :vartype auth_type: str or ~azure.mgmt.machinelearningservices.models.ConnectionAuthType
     :ivar category: Category of the connection. Known values are: "PythonFeed",
-     "ContainerRegistry", and "Git".
+     "ContainerRegistry", "Git", "FeatureStore", "S3", "Snowflake", "AzureSqlDb", and
+     "AzureSynapseAnalytics".
     :vartype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
     :ivar target:
     :vartype target: str
@@ -15802,11 +18449,12 @@ def __init__(
         value: Optional[str] = None,
         value_format: Optional[Union[str, "_models.ValueFormat"]] = None,
         credentials: Optional["_models.WorkspaceConnectionPersonalAccessToken"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword category: Category of the connection. Known values are: "PythonFeed",
-         "ContainerRegistry", and "Git".
+         "ContainerRegistry", "Git", "FeatureStore", "S3", "Snowflake", "AzureSqlDb", and
+         "AzureSynapseAnalytics".
         :paramtype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
         :keyword target:
         :paramtype target: str
@@ -15834,7 +18482,7 @@ class PersonalComputeInstanceSettings(_serialization.Model):
         "assigned_user": {"key": "assignedUser", "type": "AssignedUser"},
     }
 
-    def __init__(self, *, assigned_user: Optional["_models.AssignedUser"] = None, **kwargs):
+    def __init__(self, *, assigned_user: Optional["_models.AssignedUser"] = None, **kwargs: Any) -> None:
         """
         :keyword assigned_user: A user explicitly assigned to a personal compute instance.
         :paramtype assigned_user: ~azure.mgmt.machinelearningservices.models.AssignedUser
@@ -15872,14 +18520,14 @@ class PipelineJob(JobBaseProperties):  # pylint: disable=too-many-instance-attri
     :ivar is_archived: Is the asset archived?.
     :vartype is_archived: bool
     :ivar job_type: [Required] Specifies the type of job. Required. Known values are: "AutoML",
-     "Command", "Sweep", and "Pipeline".
+     "Command", "Labeling", "Sweep", "Pipeline", and "Spark".
     :vartype job_type: str or ~azure.mgmt.machinelearningservices.models.JobType
     :ivar services: List of JobEndpoints.
      For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
     :vartype services: dict[str, ~azure.mgmt.machinelearningservices.models.JobService]
     :ivar status: Status of the job. Known values are: "NotStarted", "Starting", "Provisioning",
      "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed", "Failed",
-     "Canceled", "NotResponding", "Paused", and "Unknown".
+     "Canceled", "NotResponding", "Paused", "Unknown", and "Scheduled".
     :vartype status: str or ~azure.mgmt.machinelearningservices.models.JobStatus
     :ivar inputs: Inputs for the pipeline job.
     :vartype inputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobInput]
@@ -15936,8 +18584,8 @@ def __init__(
         outputs: Optional[Dict[str, "_models.JobOutput"]] = None,
         settings: Optional[JSON] = None,
         source_job_id: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -16016,7 +18664,7 @@ class PrivateEndpoint(_serialization.Model):
         "subnet_arm_id": {"key": "subnetArmId", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.id = None
@@ -16093,8 +18741,8 @@ def __init__(
         sku: Optional["_models.Sku"] = None,
         private_endpoint: Optional["_models.PrivateEndpoint"] = None,
         private_link_service_connection_state: Optional["_models.PrivateLinkServiceConnectionState"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword identity: The identity of the resource.
         :paramtype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
@@ -16132,7 +18780,7 @@ class PrivateEndpointConnectionListResult(_serialization.Model):
         "value": {"key": "value", "type": "[PrivateEndpointConnection]"},
     }
 
-    def __init__(self, *, value: Optional[List["_models.PrivateEndpointConnection"]] = None, **kwargs):
+    def __init__(self, *, value: Optional[List["_models.PrivateEndpointConnection"]] = None, **kwargs: Any) -> None:
         """
         :keyword value: Array of private endpoint connections.
         :paramtype value: list[~azure.mgmt.machinelearningservices.models.PrivateEndpointConnection]
@@ -16204,8 +18852,8 @@ def __init__(
         tags: Optional[Dict[str, str]] = None,
         sku: Optional["_models.Sku"] = None,
         required_zone_names: Optional[List[str]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword identity: The identity of the resource.
         :paramtype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
@@ -16239,7 +18887,7 @@ class PrivateLinkResourceListResult(_serialization.Model):
         "value": {"key": "value", "type": "[PrivateLinkResource]"},
     }
 
-    def __init__(self, *, value: Optional[List["_models.PrivateLinkResource"]] = None, **kwargs):
+    def __init__(self, *, value: Optional[List["_models.PrivateLinkResource"]] = None, **kwargs: Any) -> None:
         """
         :keyword value: Array of private link resources.
         :paramtype value: list[~azure.mgmt.machinelearningservices.models.PrivateLinkResource]
@@ -16249,7 +18897,8 @@ def __init__(self, *, value: Optional[List["_models.PrivateLinkResource"]] = Non
 
 
 class PrivateLinkServiceConnectionState(_serialization.Model):
-    """A collection of information about the state of the connection between service consumer and provider.
+    """A collection of information about the state of the connection between service consumer and
+    provider.
 
     :ivar status: Indicates whether the connection has been Approved/Rejected/Removed by the owner
      of the service. Known values are: "Pending", "Approved", "Rejected", "Disconnected", and
@@ -16275,8 +18924,8 @@ def __init__(
         status: Optional[Union[str, "_models.PrivateEndpointServiceConnectionStatus"]] = None,
         description: Optional[str] = None,
         actions_required: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword status: Indicates whether the connection has been Approved/Rejected/Removed by the
          owner of the service. Known values are: "Pending", "Approved", "Rejected", "Disconnected", and
@@ -16326,8 +18975,8 @@ def __init__(
         period: datetime.timedelta = "PT10S",
         success_threshold: int = 1,
         timeout: datetime.timedelta = "PT2S",
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword failure_threshold: The number of failures to allow before returning an unhealthy
          status.
@@ -16349,6 +18998,45 @@ def __init__(
         self.timeout = timeout
 
 
+class ProgressMetrics(_serialization.Model):
+    """Progress metrics definition.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar completed_datapoint_count: The completed datapoint count.
+    :vartype completed_datapoint_count: int
+    :ivar incremental_data_last_refresh_date_time: The time of last successful incremental data
+     refresh in UTC.
+    :vartype incremental_data_last_refresh_date_time: ~datetime.datetime
+    :ivar skipped_datapoint_count: The skipped datapoint count.
+    :vartype skipped_datapoint_count: int
+    :ivar total_datapoint_count: The total datapoint count.
+    :vartype total_datapoint_count: int
+    """
+
+    _validation = {
+        "completed_datapoint_count": {"readonly": True},
+        "incremental_data_last_refresh_date_time": {"readonly": True},
+        "skipped_datapoint_count": {"readonly": True},
+        "total_datapoint_count": {"readonly": True},
+    }
+
+    _attribute_map = {
+        "completed_datapoint_count": {"key": "completedDatapointCount", "type": "int"},
+        "incremental_data_last_refresh_date_time": {"key": "incrementalDataLastRefreshDateTime", "type": "iso-8601"},
+        "skipped_datapoint_count": {"key": "skippedDatapointCount", "type": "int"},
+        "total_datapoint_count": {"key": "totalDatapointCount", "type": "int"},
+    }
+
+    def __init__(self, **kwargs: Any) -> None:
+        """ """
+        super().__init__(**kwargs)
+        self.completed_datapoint_count = None
+        self.incremental_data_last_refresh_date_time = None
+        self.skipped_datapoint_count = None
+        self.total_datapoint_count = None
+
+
 class PyTorch(DistributionConfiguration):
     """PyTorch distribution configuration.
 
@@ -16370,7 +19058,7 @@ class PyTorch(DistributionConfiguration):
         "process_count_per_instance": {"key": "processCountPerInstance", "type": "int"},
     }
 
-    def __init__(self, *, process_count_per_instance: Optional[int] = None, **kwargs):
+    def __init__(self, *, process_count_per_instance: Optional[int] = None, **kwargs: Any) -> None:
         """
         :keyword process_count_per_instance: Number of processes per node.
         :paramtype process_count_per_instance: int
@@ -16407,8 +19095,8 @@ def __init__(
         type: Optional[str] = None,
         limit: Optional[int] = None,
         unit: Optional[Union[str, "_models.QuotaUnit"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword id: Specifies the resource ID.
         :paramtype id: str
@@ -16441,8 +19129,12 @@ class QuotaUpdateParameters(_serialization.Model):
     }
 
     def __init__(
-        self, *, value: Optional[List["_models.QuotaBaseProperties"]] = None, location: Optional[str] = None, **kwargs
-    ):
+        self,
+        *,
+        value: Optional[List["_models.QuotaBaseProperties"]] = None,
+        location: Optional[str] = None,
+        **kwargs: Any
+    ) -> None:
         """
         :keyword value: The list for update quota.
         :paramtype value: list[~azure.mgmt.machinelearningservices.models.QuotaBaseProperties]
@@ -16464,6 +19156,9 @@ class RandomSamplingAlgorithm(SamplingAlgorithm):
      "Bayesian".
     :vartype sampling_algorithm_type: str or
      ~azure.mgmt.machinelearningservices.models.SamplingAlgorithmType
+    :ivar logbase: An optional positive number or e in string format to be used as base for log
+     based random sampling.
+    :vartype logbase: str
     :ivar rule: The specific type of random algorithm. Known values are: "Random" and "Sobol".
     :vartype rule: str or ~azure.mgmt.machinelearningservices.models.RandomSamplingAlgorithmRule
     :ivar seed: An optional integer to use as the seed for random number generation.
@@ -16476,6 +19171,7 @@ class RandomSamplingAlgorithm(SamplingAlgorithm):
 
     _attribute_map = {
         "sampling_algorithm_type": {"key": "samplingAlgorithmType", "type": "str"},
+        "logbase": {"key": "logbase", "type": "str"},
         "rule": {"key": "rule", "type": "str"},
         "seed": {"key": "seed", "type": "int"},
     }
@@ -16483,11 +19179,15 @@ class RandomSamplingAlgorithm(SamplingAlgorithm):
     def __init__(
         self,
         *,
+        logbase: Optional[str] = None,
         rule: Optional[Union[str, "_models.RandomSamplingAlgorithmRule"]] = None,
         seed: Optional[int] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
+        :keyword logbase: An optional positive number or e in string format to be used as base for log
+         based random sampling.
+        :paramtype logbase: str
         :keyword rule: The specific type of random algorithm. Known values are: "Random" and "Sobol".
         :paramtype rule: str or ~azure.mgmt.machinelearningservices.models.RandomSamplingAlgorithmRule
         :keyword seed: An optional integer to use as the seed for random number generation.
@@ -16495,6 +19195,7 @@ def __init__(
         """
         super().__init__(**kwargs)
         self.sampling_algorithm_type: str = "Random"
+        self.logbase = logbase
         self.rule = rule
         self.seed = seed
 
@@ -16533,8 +19234,8 @@ def __init__(
         minutes: List[int],
         month_days: Optional[List[int]] = None,
         week_days: Optional[List[Union[str, "_models.WeekDay"]]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword hours: [Required] List of hours for the schedule. Required.
         :paramtype hours: list[int]
@@ -16605,8 +19306,8 @@ def __init__(
         start_time: Optional[str] = None,
         time_zone: str = "UTC",
         schedule: Optional["_models.RecurrenceSchedule"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword end_time: Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer
          https://en.wikipedia.org/wiki/ISO_8601.
@@ -16657,7 +19358,9 @@ class RegenerateEndpointKeysRequest(_serialization.Model):
         "key_value": {"key": "keyValue", "type": "str"},
     }
 
-    def __init__(self, *, key_type: Union[str, "_models.KeyType"], key_value: Optional[str] = None, **kwargs):
+    def __init__(
+        self, *, key_type: Union[str, "_models.KeyType"], key_value: Optional[str] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword key_type: [Required] Specification for which type of key to generate. Primary or
          Secondary. Required. Known values are: "Primary" and "Secondary".
@@ -16670,6 +19373,94 @@ def __init__(self, *, key_type: Union[str, "_models.KeyType"], key_value: Option
         self.key_value = key_value
 
 
+class Registry(TrackedResource):
+    """Registry.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar id: Fully qualified resource ID for the resource. Ex -
+     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}.
+    :vartype id: str
+    :ivar name: The name of the resource.
+    :vartype name: str
+    :ivar type: The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or
+     "Microsoft.Storage/storageAccounts".
+    :vartype type: str
+    :ivar system_data: Azure Resource Manager metadata containing createdBy and modifiedBy
+     information.
+    :vartype system_data: ~azure.mgmt.machinelearningservices.models.SystemData
+    :ivar tags: Resource tags.
+    :vartype tags: dict[str, str]
+    :ivar location: The geo-location where the resource lives. Required.
+    :vartype location: str
+    :ivar identity: Managed service identity (system assigned and/or user assigned identities).
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+    :ivar kind: Metadata used by portal/tooling/etc to render different UX experiences for
+     resources of the same type.
+    :vartype kind: str
+    :ivar properties: [Required] Additional attributes of the entity. Required.
+    :vartype properties: ~azure.mgmt.machinelearningservices.models.RegistryProperties
+    :ivar sku: Sku details required for ARM contract for Autoscaling.
+    :vartype sku: ~azure.mgmt.machinelearningservices.models.Sku
+    """
+
+    _validation = {
+        "id": {"readonly": True},
+        "name": {"readonly": True},
+        "type": {"readonly": True},
+        "system_data": {"readonly": True},
+        "location": {"required": True},
+        "properties": {"required": True},
+    }
+
+    _attribute_map = {
+        "id": {"key": "id", "type": "str"},
+        "name": {"key": "name", "type": "str"},
+        "type": {"key": "type", "type": "str"},
+        "system_data": {"key": "systemData", "type": "SystemData"},
+        "tags": {"key": "tags", "type": "{str}"},
+        "location": {"key": "location", "type": "str"},
+        "identity": {"key": "identity", "type": "ManagedServiceIdentity"},
+        "kind": {"key": "kind", "type": "str"},
+        "properties": {"key": "properties", "type": "RegistryProperties"},
+        "sku": {"key": "sku", "type": "Sku"},
+    }
+
+    def __init__(
+        self,
+        *,
+        location: str,
+        properties: "_models.RegistryProperties",
+        tags: Optional[Dict[str, str]] = None,
+        identity: Optional["_models.ManagedServiceIdentity"] = None,
+        kind: Optional[str] = None,
+        sku: Optional["_models.Sku"] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword tags: Resource tags.
+        :paramtype tags: dict[str, str]
+        :keyword location: The geo-location where the resource lives. Required.
+        :paramtype location: str
+        :keyword identity: Managed service identity (system assigned and/or user assigned identities).
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
+        :keyword kind: Metadata used by portal/tooling/etc to render different UX experiences for
+         resources of the same type.
+        :paramtype kind: str
+        :keyword properties: [Required] Additional attributes of the entity. Required.
+        :paramtype properties: ~azure.mgmt.machinelearningservices.models.RegistryProperties
+        :keyword sku: Sku details required for ARM contract for Autoscaling.
+        :paramtype sku: ~azure.mgmt.machinelearningservices.models.Sku
+        """
+        super().__init__(tags=tags, location=location, **kwargs)
+        self.identity = identity
+        self.kind = kind
+        self.properties = properties
+        self.sku = sku
+
+
 class RegistryListCredentialsResult(_serialization.Model):
     """RegistryListCredentialsResult.
 
@@ -16694,15 +19485,173 @@ class RegistryListCredentialsResult(_serialization.Model):
         "passwords": {"key": "passwords", "type": "[Password]"},
     }
 
-    def __init__(self, *, passwords: Optional[List["_models.Password"]] = None, **kwargs):
+    def __init__(self, *, passwords: Optional[List["_models.Password"]] = None, **kwargs: Any) -> None:
         """
         :keyword passwords:
         :paramtype passwords: list[~azure.mgmt.machinelearningservices.models.Password]
         """
         super().__init__(**kwargs)
-        self.location = None
-        self.username = None
-        self.passwords = passwords
+        self.location = None
+        self.username = None
+        self.passwords = passwords
+
+
+class RegistryProperties(ResourceBase):
+    """Details of the Registry.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar public_network_access:
+    :vartype public_network_access: str
+    :ivar discovery_url:
+    :vartype discovery_url: str
+    :ivar intellectual_property_publisher:
+    :vartype intellectual_property_publisher: str
+    :ivar managed_resource_group: Managed resource group created for the registry.
+    :vartype managed_resource_group: ~azure.mgmt.machinelearningservices.models.ArmResourceId
+    :ivar ml_flow_registry_uri:
+    :vartype ml_flow_registry_uri: str
+    :ivar private_link_count:
+    :vartype private_link_count: int
+    :ivar region_details: Details of each region the registry is in.
+    :vartype region_details:
+     list[~azure.mgmt.machinelearningservices.models.RegistryRegionArmDetails]
+    """
+
+    _attribute_map = {
+        "description": {"key": "description", "type": "str"},
+        "properties": {"key": "properties", "type": "{str}"},
+        "tags": {"key": "tags", "type": "{str}"},
+        "public_network_access": {"key": "publicNetworkAccess", "type": "str"},
+        "discovery_url": {"key": "discoveryUrl", "type": "str"},
+        "intellectual_property_publisher": {"key": "intellectualPropertyPublisher", "type": "str"},
+        "managed_resource_group": {"key": "managedResourceGroup", "type": "ArmResourceId"},
+        "ml_flow_registry_uri": {"key": "mlFlowRegistryUri", "type": "str"},
+        "private_link_count": {"key": "privateLinkCount", "type": "int"},
+        "region_details": {"key": "regionDetails", "type": "[RegistryRegionArmDetails]"},
+    }
+
+    def __init__(
+        self,
+        *,
+        description: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        tags: Optional[Dict[str, str]] = None,
+        public_network_access: Optional[str] = None,
+        discovery_url: Optional[str] = None,
+        intellectual_property_publisher: Optional[str] = None,
+        managed_resource_group: Optional["_models.ArmResourceId"] = None,
+        ml_flow_registry_uri: Optional[str] = None,
+        private_link_count: Optional[int] = None,
+        region_details: Optional[List["_models.RegistryRegionArmDetails"]] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword public_network_access:
+        :paramtype public_network_access: str
+        :keyword discovery_url:
+        :paramtype discovery_url: str
+        :keyword intellectual_property_publisher:
+        :paramtype intellectual_property_publisher: str
+        :keyword managed_resource_group: Managed resource group created for the registry.
+        :paramtype managed_resource_group: ~azure.mgmt.machinelearningservices.models.ArmResourceId
+        :keyword ml_flow_registry_uri:
+        :paramtype ml_flow_registry_uri: str
+        :keyword private_link_count:
+        :paramtype private_link_count: int
+        :keyword region_details: Details of each region the registry is in.
+        :paramtype region_details:
+         list[~azure.mgmt.machinelearningservices.models.RegistryRegionArmDetails]
+        """
+        super().__init__(description=description, properties=properties, tags=tags, **kwargs)
+        self.public_network_access = public_network_access
+        self.discovery_url = discovery_url
+        self.intellectual_property_publisher = intellectual_property_publisher
+        self.managed_resource_group = managed_resource_group
+        self.ml_flow_registry_uri = ml_flow_registry_uri
+        self.private_link_count = private_link_count
+        self.region_details = region_details
+
+
+class RegistryRegionArmDetails(_serialization.Model):
+    """Details for each region the registry is in.
+
+    :ivar acr_details: List of ACR accounts.
+    :vartype acr_details: list[~azure.mgmt.machinelearningservices.models.AcrDetails]
+    :ivar location: The location where the registry exists.
+    :vartype location: str
+    :ivar storage_account_details: List of storage accounts.
+    :vartype storage_account_details:
+     list[~azure.mgmt.machinelearningservices.models.StorageAccountDetails]
+    """
+
+    _attribute_map = {
+        "acr_details": {"key": "acrDetails", "type": "[AcrDetails]"},
+        "location": {"key": "location", "type": "str"},
+        "storage_account_details": {"key": "storageAccountDetails", "type": "[StorageAccountDetails]"},
+    }
+
+    def __init__(
+        self,
+        *,
+        acr_details: Optional[List["_models.AcrDetails"]] = None,
+        location: Optional[str] = None,
+        storage_account_details: Optional[List["_models.StorageAccountDetails"]] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword acr_details: List of ACR accounts.
+        :paramtype acr_details: list[~azure.mgmt.machinelearningservices.models.AcrDetails]
+        :keyword location: The location where the registry exists.
+        :paramtype location: str
+        :keyword storage_account_details: List of storage accounts.
+        :paramtype storage_account_details:
+         list[~azure.mgmt.machinelearningservices.models.StorageAccountDetails]
+        """
+        super().__init__(**kwargs)
+        self.acr_details = acr_details
+        self.location = location
+        self.storage_account_details = storage_account_details
+
+
+class RegistryTrackedResourceArmPaginatedResult(_serialization.Model):
+    """A paginated list of Registry entities.
+
+    :ivar next_link: The link to the next page of Registry objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type Registry.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.Registry]
+    """
+
+    _attribute_map = {
+        "next_link": {"key": "nextLink", "type": "str"},
+        "value": {"key": "value", "type": "[Registry]"},
+    }
+
+    def __init__(
+        self, *, next_link: Optional[str] = None, value: Optional[List["_models.Registry"]] = None, **kwargs: Any
+    ) -> None:
+        """
+        :keyword next_link: The link to the next page of Registry objects. If null, there are no
+         additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type Registry.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.Registry]
+        """
+        super().__init__(**kwargs)
+        self.next_link = next_link
+        self.value = value
 
 
 class Regression(TableVertical, AutoMLVertical):  # pylint: disable=too-many-instance-attributes
@@ -16728,11 +19677,19 @@ class Regression(TableVertical, AutoMLVertical):  # pylint: disable=too-many-ins
     :ivar featurization_settings: Featurization inputs needed for AutoML job.
     :vartype featurization_settings:
      ~azure.mgmt.machinelearningservices.models.TableVerticalFeaturizationSettings
+    :ivar fixed_parameters: Model/training parameters that will remain constant throughout
+     training.
+    :vartype fixed_parameters: ~azure.mgmt.machinelearningservices.models.TableFixedParameters
     :ivar limit_settings: Execution constraints for AutoMLJob.
     :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.TableVerticalLimitSettings
     :ivar n_cross_validations: Number of cross validation folds to be applied on training dataset
      when validation dataset is not provided.
     :vartype n_cross_validations: ~azure.mgmt.machinelearningservices.models.NCrossValidations
+    :ivar search_space: Search space for sampling different combinations of models and their
+     hyperparameters.
+    :vartype search_space: list[~azure.mgmt.machinelearningservices.models.TableParameterSubspace]
+    :ivar sweep_settings: Settings for model sweeping and hyperparameter tuning.
+    :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.TableSweepSettings
     :ivar test_data: Test data input.
     :vartype test_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
     :ivar test_data_size: The fraction of test dataset that needs to be set aside for validation
@@ -16772,8 +19729,11 @@ class Regression(TableVertical, AutoMLVertical):  # pylint: disable=too-many-ins
         "training_data": {"key": "trainingData", "type": "MLTableJobInput"},
         "cv_split_column_names": {"key": "cvSplitColumnNames", "type": "[str]"},
         "featurization_settings": {"key": "featurizationSettings", "type": "TableVerticalFeaturizationSettings"},
+        "fixed_parameters": {"key": "fixedParameters", "type": "TableFixedParameters"},
         "limit_settings": {"key": "limitSettings", "type": "TableVerticalLimitSettings"},
         "n_cross_validations": {"key": "nCrossValidations", "type": "NCrossValidations"},
+        "search_space": {"key": "searchSpace", "type": "[TableParameterSubspace]"},
+        "sweep_settings": {"key": "sweepSettings", "type": "TableSweepSettings"},
         "test_data": {"key": "testData", "type": "MLTableJobInput"},
         "test_data_size": {"key": "testDataSize", "type": "float"},
         "validation_data": {"key": "validationData", "type": "MLTableJobInput"},
@@ -16791,8 +19751,11 @@ def __init__(
         target_column_name: Optional[str] = None,
         cv_split_column_names: Optional[List[str]] = None,
         featurization_settings: Optional["_models.TableVerticalFeaturizationSettings"] = None,
+        fixed_parameters: Optional["_models.TableFixedParameters"] = None,
         limit_settings: Optional["_models.TableVerticalLimitSettings"] = None,
         n_cross_validations: Optional["_models.NCrossValidations"] = None,
+        search_space: Optional[List["_models.TableParameterSubspace"]] = None,
+        sweep_settings: Optional["_models.TableSweepSettings"] = None,
         test_data: Optional["_models.MLTableJobInput"] = None,
         test_data_size: Optional[float] = None,
         validation_data: Optional["_models.MLTableJobInput"] = None,
@@ -16800,8 +19763,8 @@ def __init__(
         weight_column_name: Optional[str] = None,
         primary_metric: Optional[Union[str, "_models.RegressionPrimaryMetrics"]] = None,
         training_settings: Optional["_models.RegressionTrainingSettings"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
          "Warning", "Error", and "Critical".
@@ -16816,6 +19779,9 @@ def __init__(
         :keyword featurization_settings: Featurization inputs needed for AutoML job.
         :paramtype featurization_settings:
          ~azure.mgmt.machinelearningservices.models.TableVerticalFeaturizationSettings
+        :keyword fixed_parameters: Model/training parameters that will remain constant throughout
+         training.
+        :paramtype fixed_parameters: ~azure.mgmt.machinelearningservices.models.TableFixedParameters
         :keyword limit_settings: Execution constraints for AutoMLJob.
         :paramtype limit_settings:
          ~azure.mgmt.machinelearningservices.models.TableVerticalLimitSettings
@@ -16823,6 +19789,12 @@ def __init__(
          dataset
          when validation dataset is not provided.
         :paramtype n_cross_validations: ~azure.mgmt.machinelearningservices.models.NCrossValidations
+        :keyword search_space: Search space for sampling different combinations of models and their
+         hyperparameters.
+        :paramtype search_space:
+         list[~azure.mgmt.machinelearningservices.models.TableParameterSubspace]
+        :keyword sweep_settings: Settings for model sweeping and hyperparameter tuning.
+        :paramtype sweep_settings: ~azure.mgmt.machinelearningservices.models.TableSweepSettings
         :keyword test_data: Test data input.
         :paramtype test_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
         :keyword test_data_size: The fraction of test dataset that needs to be set aside for validation
@@ -16852,8 +19824,11 @@ def __init__(
         super().__init__(
             cv_split_column_names=cv_split_column_names,
             featurization_settings=featurization_settings,
+            fixed_parameters=fixed_parameters,
             limit_settings=limit_settings,
             n_cross_validations=n_cross_validations,
+            search_space=search_space,
+            sweep_settings=sweep_settings,
             test_data=test_data,
             test_data_size=test_data_size,
             validation_data=validation_data,
@@ -16872,8 +19847,11 @@ def __init__(
         self.training_settings = training_settings
         self.cv_split_column_names = cv_split_column_names
         self.featurization_settings = featurization_settings
+        self.fixed_parameters = fixed_parameters
         self.limit_settings = limit_settings
         self.n_cross_validations = n_cross_validations
+        self.search_space = search_space
+        self.sweep_settings = sweep_settings
         self.test_data = test_data
         self.test_data_size = test_data_size
         self.validation_data = validation_data
@@ -16901,6 +19879,14 @@ class RegressionTrainingSettings(TrainingSettings):
     :ivar stack_ensemble_settings: Stack ensemble settings for stack ensemble run.
     :vartype stack_ensemble_settings:
      ~azure.mgmt.machinelearningservices.models.StackEnsembleSettings
+    :ivar training_mode: TrainingMode mode - Setting to 'auto' is same as setting it to
+     'non-distributed' for now, however in the future may result in mixed mode or heuristics based
+     mode selection. Default is 'auto'.
+     If 'Distributed' then only distributed featurization is used and distributed algorithms are
+     chosen.
+     If 'NonDistributed' then only non distributed algorithms are chosen. Known values are: "Auto",
+     "Distributed", and "NonDistributed".
+    :vartype training_mode: str or ~azure.mgmt.machinelearningservices.models.TrainingMode
     :ivar allowed_training_algorithms: Allowed models for regression task.
     :vartype allowed_training_algorithms: list[str or
      ~azure.mgmt.machinelearningservices.models.RegressionModels]
@@ -16917,6 +19903,7 @@ class RegressionTrainingSettings(TrainingSettings):
         "enable_vote_ensemble": {"key": "enableVoteEnsemble", "type": "bool"},
         "ensemble_model_download_timeout": {"key": "ensembleModelDownloadTimeout", "type": "duration"},
         "stack_ensemble_settings": {"key": "stackEnsembleSettings", "type": "StackEnsembleSettings"},
+        "training_mode": {"key": "trainingMode", "type": "str"},
         "allowed_training_algorithms": {"key": "allowedTrainingAlgorithms", "type": "[str]"},
         "blocked_training_algorithms": {"key": "blockedTrainingAlgorithms", "type": "[str]"},
     }
@@ -16931,10 +19918,11 @@ def __init__(
         enable_vote_ensemble: bool = True,
         ensemble_model_download_timeout: datetime.timedelta = "PT5M",
         stack_ensemble_settings: Optional["_models.StackEnsembleSettings"] = None,
+        training_mode: Optional[Union[str, "_models.TrainingMode"]] = None,
         allowed_training_algorithms: Optional[List[Union[str, "_models.RegressionModels"]]] = None,
         blocked_training_algorithms: Optional[List[Union[str, "_models.RegressionModels"]]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword enable_dnn_training: Enable recommendation of DNN models.
         :paramtype enable_dnn_training: bool
@@ -16953,6 +19941,14 @@ def __init__(
         :keyword stack_ensemble_settings: Stack ensemble settings for stack ensemble run.
         :paramtype stack_ensemble_settings:
          ~azure.mgmt.machinelearningservices.models.StackEnsembleSettings
+        :keyword training_mode: TrainingMode mode - Setting to 'auto' is same as setting it to
+         'non-distributed' for now, however in the future may result in mixed mode or heuristics based
+         mode selection. Default is 'auto'.
+         If 'Distributed' then only distributed featurization is used and distributed algorithms are
+         chosen.
+         If 'NonDistributed' then only non distributed algorithms are chosen. Known values are: "Auto",
+         "Distributed", and "NonDistributed".
+        :paramtype training_mode: str or ~azure.mgmt.machinelearningservices.models.TrainingMode
         :keyword allowed_training_algorithms: Allowed models for regression task.
         :paramtype allowed_training_algorithms: list[str or
          ~azure.mgmt.machinelearningservices.models.RegressionModels]
@@ -16968,6 +19964,7 @@ def __init__(
             enable_vote_ensemble=enable_vote_ensemble,
             ensemble_model_download_timeout=ensemble_model_download_timeout,
             stack_ensemble_settings=stack_ensemble_settings,
+            training_mode=training_mode,
             **kwargs
         )
         self.allowed_training_algorithms = allowed_training_algorithms
@@ -16991,7 +19988,7 @@ class ResourceId(_serialization.Model):
         "id": {"key": "id", "type": "str"},
     }
 
-    def __init__(self, *, id: str, **kwargs):  # pylint: disable=redefined-builtin
+    def __init__(self, *, id: str, **kwargs: Any) -> None:  # pylint: disable=redefined-builtin
         """
         :keyword id: The ID of the resource. Required.
         :paramtype id: str
@@ -17021,7 +20018,7 @@ class ResourceName(_serialization.Model):
         "localized_value": {"key": "localizedValue", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.value = None
@@ -17065,7 +20062,7 @@ class ResourceQuota(_serialization.Model):
         "unit": {"key": "unit", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.id = None
@@ -17097,7 +20094,7 @@ class Route(_serialization.Model):
         "port": {"key": "port", "type": "int"},
     }
 
-    def __init__(self, *, path: str, port: int, **kwargs):
+    def __init__(self, *, path: str, port: int, **kwargs: Any) -> None:
         """
         :keyword path: [Required] The path for the route. Required.
         :paramtype path: str
@@ -17115,10 +20112,12 @@ class SASAuthTypeWorkspaceConnectionProperties(WorkspaceConnectionPropertiesV2):
     All required parameters must be populated in order to send to Azure.
 
     :ivar auth_type: Authentication type of the connection target. Required. Known values are:
-     "PAT", "ManagedIdentity", "UsernamePassword", "None", and "SAS".
+     "PAT", "ManagedIdentity", "UsernamePassword", "None", "SAS", "ServicePrincipal", and
+     "AccessKey".
     :vartype auth_type: str or ~azure.mgmt.machinelearningservices.models.ConnectionAuthType
     :ivar category: Category of the connection. Known values are: "PythonFeed",
-     "ContainerRegistry", and "Git".
+     "ContainerRegistry", "Git", "FeatureStore", "S3", "Snowflake", "AzureSqlDb", and
+     "AzureSynapseAnalytics".
     :vartype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
     :ivar target:
     :vartype target: str
@@ -17152,11 +20151,12 @@ def __init__(
         value: Optional[str] = None,
         value_format: Optional[Union[str, "_models.ValueFormat"]] = None,
         credentials: Optional["_models.WorkspaceConnectionSharedAccessSignature"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword category: Category of the connection. Known values are: "PythonFeed",
-         "ContainerRegistry", and "Git".
+         "ContainerRegistry", "Git", "FeatureStore", "S3", "Snowflake", "AzureSqlDb", and
+         "AzureSynapseAnalytics".
         :paramtype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
         :keyword target:
         :paramtype target: str
@@ -17179,7 +20179,8 @@ class SasDatastoreCredentials(DatastoreCredentials):
     All required parameters must be populated in order to send to Azure.
 
     :ivar credentials_type: [Required] Credential type used to authentication with storage.
-     Required. Known values are: "AccountKey", "Certificate", "None", "Sas", and "ServicePrincipal".
+     Required. Known values are: "AccountKey", "Certificate", "None", "Sas", "ServicePrincipal",
+     "KerberosKeytab", and "KerberosPassword".
     :vartype credentials_type: str or ~azure.mgmt.machinelearningservices.models.CredentialsType
     :ivar secrets: [Required] Storage container secrets. Required.
     :vartype secrets: ~azure.mgmt.machinelearningservices.models.SasDatastoreSecrets
@@ -17195,7 +20196,7 @@ class SasDatastoreCredentials(DatastoreCredentials):
         "secrets": {"key": "secrets", "type": "SasDatastoreSecrets"},
     }
 
-    def __init__(self, *, secrets: "_models.SasDatastoreSecrets", **kwargs):
+    def __init__(self, *, secrets: "_models.SasDatastoreSecrets", **kwargs: Any) -> None:
         """
         :keyword secrets: [Required] Storage container secrets. Required.
         :paramtype secrets: ~azure.mgmt.machinelearningservices.models.SasDatastoreSecrets
@@ -17211,7 +20212,8 @@ class SasDatastoreSecrets(DatastoreSecrets):
     All required parameters must be populated in order to send to Azure.
 
     :ivar secrets_type: [Required] Credential type used to authentication with storage. Required.
-     Known values are: "AccountKey", "Certificate", "Sas", and "ServicePrincipal".
+     Known values are: "AccountKey", "Certificate", "Sas", "ServicePrincipal", "KerberosPassword",
+     and "KerberosKeytab".
     :vartype secrets_type: str or ~azure.mgmt.machinelearningservices.models.SecretsType
     :ivar sas_token: Storage container SAS token.
     :vartype sas_token: str
@@ -17226,7 +20228,7 @@ class SasDatastoreSecrets(DatastoreSecrets):
         "sas_token": {"key": "sasToken", "type": "str"},
     }
 
-    def __init__(self, *, sas_token: Optional[str] = None, **kwargs):
+    def __init__(self, *, sas_token: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword sas_token: Storage container SAS token.
         :paramtype sas_token: str
@@ -17266,8 +20268,8 @@ def __init__(
         max_node_count: int,
         min_node_count: int = 0,
         node_idle_time_before_scale_down: Optional[datetime.timedelta] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword max_node_count: Max number of nodes to use. Required.
         :paramtype max_node_count: int
@@ -17294,7 +20296,7 @@ class ScaleSettingsInformation(_serialization.Model):
         "scale_settings": {"key": "scaleSettings", "type": "ScaleSettings"},
     }
 
-    def __init__(self, *, scale_settings: Optional["_models.ScaleSettings"] = None, **kwargs):
+    def __init__(self, *, scale_settings: Optional["_models.ScaleSettings"] = None, **kwargs: Any) -> None:
         """
         :keyword scale_settings: scale settings for AML Compute.
         :paramtype scale_settings: ~azure.mgmt.machinelearningservices.models.ScaleSettings
@@ -17341,7 +20343,7 @@ class Schedule(Resource):
         "properties": {"key": "properties", "type": "ScheduleProperties"},
     }
 
-    def __init__(self, *, properties: "_models.ScheduleProperties", **kwargs):
+    def __init__(self, *, properties: "_models.ScheduleProperties", **kwargs: Any) -> None:
         """
         :keyword properties: [Required] Additional attributes of the entity. Required.
         :paramtype properties: ~azure.mgmt.machinelearningservices.models.ScheduleProperties
@@ -17375,8 +20377,8 @@ def __init__(
         id: Optional[str] = None,  # pylint: disable=redefined-builtin
         provisioning_status: Optional[Union[str, "_models.ScheduleProvisioningState"]] = None,
         status: Optional[Union[str, "_models.ScheduleStatus"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword id: A system assigned id for the schedule.
         :paramtype id: str
@@ -17448,8 +20450,8 @@ def __init__(
         tags: Optional[Dict[str, str]] = None,
         display_name: Optional[str] = None,
         is_enabled: bool = True,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -17489,7 +20491,9 @@ class ScheduleResourceArmPaginatedResult(_serialization.Model):
         "value": {"key": "value", "type": "[Schedule]"},
     }
 
-    def __init__(self, *, next_link: Optional[str] = None, value: Optional[List["_models.Schedule"]] = None, **kwargs):
+    def __init__(
+        self, *, next_link: Optional[str] = None, value: Optional[List["_models.Schedule"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword next_link: The link to the next page of Schedule objects. If null, there are no
          additional pages.
@@ -17505,7 +20509,7 @@ def __init__(self, *, next_link: Optional[str] = None, value: Optional[List["_mo
 class ScriptReference(_serialization.Model):
     """Script reference.
 
-    :ivar script_source: The storage source of the script: workspace.
+    :ivar script_source: The storage source of the script: inline, workspace.
     :vartype script_source: str
     :ivar script_data: The location of scripts in the mounted volume.
     :vartype script_data: str
@@ -17529,10 +20533,10 @@ def __init__(
         script_data: Optional[str] = None,
         script_arguments: Optional[str] = None,
         timeout: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
-        :keyword script_source: The storage source of the script: workspace.
+        :keyword script_source: The storage source of the script: inline, workspace.
         :paramtype script_source: str
         :keyword script_data: The location of scripts in the mounted volume.
         :paramtype script_data: str
@@ -17567,8 +20571,8 @@ def __init__(
         *,
         startup_script: Optional["_models.ScriptReference"] = None,
         creation_script: Optional["_models.ScriptReference"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword startup_script: Script that's run every time the machine starts.
         :paramtype startup_script: ~azure.mgmt.machinelearningservices.models.ScriptReference
@@ -17591,7 +20595,7 @@ class ServiceManagedResourcesSettings(_serialization.Model):
         "cosmos_db": {"key": "cosmosDb", "type": "CosmosDbSettings"},
     }
 
-    def __init__(self, *, cosmos_db: Optional["_models.CosmosDbSettings"] = None, **kwargs):
+    def __init__(self, *, cosmos_db: Optional["_models.CosmosDbSettings"] = None, **kwargs: Any) -> None:
         """
         :keyword cosmos_db: The settings for the service managed cosmosdb account.
         :paramtype cosmos_db: ~azure.mgmt.machinelearningservices.models.CosmosDbSettings
@@ -17600,13 +20604,81 @@ def __init__(self, *, cosmos_db: Optional["_models.CosmosDbSettings"] = None, **
         self.cosmos_db = cosmos_db
 
 
+class ServicePrincipalAuthTypeWorkspaceConnectionProperties(WorkspaceConnectionPropertiesV2):
+    """ServicePrincipalAuthTypeWorkspaceConnectionProperties.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar auth_type: Authentication type of the connection target. Required. Known values are:
+     "PAT", "ManagedIdentity", "UsernamePassword", "None", "SAS", "ServicePrincipal", and
+     "AccessKey".
+    :vartype auth_type: str or ~azure.mgmt.machinelearningservices.models.ConnectionAuthType
+    :ivar category: Category of the connection. Known values are: "PythonFeed",
+     "ContainerRegistry", "Git", "FeatureStore", "S3", "Snowflake", "AzureSqlDb", and
+     "AzureSynapseAnalytics".
+    :vartype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
+    :ivar target:
+    :vartype target: str
+    :ivar value: Value details of the workspace connection.
+    :vartype value: str
+    :ivar value_format: format for the workspace connection value. "JSON"
+    :vartype value_format: str or ~azure.mgmt.machinelearningservices.models.ValueFormat
+    :ivar credentials:
+    :vartype credentials:
+     ~azure.mgmt.machinelearningservices.models.WorkspaceConnectionServicePrincipal
+    """
+
+    _validation = {
+        "auth_type": {"required": True},
+    }
+
+    _attribute_map = {
+        "auth_type": {"key": "authType", "type": "str"},
+        "category": {"key": "category", "type": "str"},
+        "target": {"key": "target", "type": "str"},
+        "value": {"key": "value", "type": "str"},
+        "value_format": {"key": "valueFormat", "type": "str"},
+        "credentials": {"key": "credentials", "type": "WorkspaceConnectionServicePrincipal"},
+    }
+
+    def __init__(
+        self,
+        *,
+        category: Optional[Union[str, "_models.ConnectionCategory"]] = None,
+        target: Optional[str] = None,
+        value: Optional[str] = None,
+        value_format: Optional[Union[str, "_models.ValueFormat"]] = None,
+        credentials: Optional["_models.WorkspaceConnectionServicePrincipal"] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword category: Category of the connection. Known values are: "PythonFeed",
+         "ContainerRegistry", "Git", "FeatureStore", "S3", "Snowflake", "AzureSqlDb", and
+         "AzureSynapseAnalytics".
+        :paramtype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
+        :keyword target:
+        :paramtype target: str
+        :keyword value: Value details of the workspace connection.
+        :paramtype value: str
+        :keyword value_format: format for the workspace connection value. "JSON"
+        :paramtype value_format: str or ~azure.mgmt.machinelearningservices.models.ValueFormat
+        :keyword credentials:
+        :paramtype credentials:
+         ~azure.mgmt.machinelearningservices.models.WorkspaceConnectionServicePrincipal
+        """
+        super().__init__(category=category, target=target, value=value, value_format=value_format, **kwargs)
+        self.auth_type: str = "ServicePrincipal"
+        self.credentials = credentials
+
+
 class ServicePrincipalDatastoreCredentials(DatastoreCredentials):
     """Service Principal datastore credentials configuration.
 
     All required parameters must be populated in order to send to Azure.
 
     :ivar credentials_type: [Required] Credential type used to authentication with storage.
-     Required. Known values are: "AccountKey", "Certificate", "None", "Sas", and "ServicePrincipal".
+     Required. Known values are: "AccountKey", "Certificate", "None", "Sas", "ServicePrincipal",
+     "KerberosKeytab", and "KerberosPassword".
     :vartype credentials_type: str or ~azure.mgmt.machinelearningservices.models.CredentialsType
     :ivar authority_url: Authority URL used for authentication.
     :vartype authority_url: str
@@ -17644,8 +20716,8 @@ def __init__(
         tenant_id: str,
         authority_url: Optional[str] = None,
         resource_url: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword authority_url: Authority URL used for authentication.
         :paramtype authority_url: str
@@ -17674,7 +20746,8 @@ class ServicePrincipalDatastoreSecrets(DatastoreSecrets):
     All required parameters must be populated in order to send to Azure.
 
     :ivar secrets_type: [Required] Credential type used to authentication with storage. Required.
-     Known values are: "AccountKey", "Certificate", "Sas", and "ServicePrincipal".
+     Known values are: "AccountKey", "Certificate", "Sas", "ServicePrincipal", "KerberosPassword",
+     and "KerberosKeytab".
     :vartype secrets_type: str or ~azure.mgmt.machinelearningservices.models.SecretsType
     :ivar client_secret: Service principal secret.
     :vartype client_secret: str
@@ -17689,7 +20762,7 @@ class ServicePrincipalDatastoreSecrets(DatastoreSecrets):
         "client_secret": {"key": "clientSecret", "type": "str"},
     }
 
-    def __init__(self, *, client_secret: Optional[str] = None, **kwargs):
+    def __init__(self, *, client_secret: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword client_secret: Service principal secret.
         :paramtype client_secret: str
@@ -17707,298 +20780,632 @@ class SetupScripts(_serialization.Model):
     """
 
     _attribute_map = {
-        "scripts": {"key": "scripts", "type": "ScriptsToExecute"},
+        "scripts": {"key": "scripts", "type": "ScriptsToExecute"},
+    }
+
+    def __init__(self, *, scripts: Optional["_models.ScriptsToExecute"] = None, **kwargs: Any) -> None:
+        """
+        :keyword scripts: Customized setup scripts.
+        :paramtype scripts: ~azure.mgmt.machinelearningservices.models.ScriptsToExecute
+        """
+        super().__init__(**kwargs)
+        self.scripts = scripts
+
+
+class SharedPrivateLinkResource(_serialization.Model):
+    """SharedPrivateLinkResource.
+
+    :ivar name: Unique name of the private link.
+    :vartype name: str
+    :ivar private_link_resource_id: The resource id that private link links to.
+    :vartype private_link_resource_id: str
+    :ivar group_id: The private link resource group id.
+    :vartype group_id: str
+    :ivar request_message: Request message.
+    :vartype request_message: str
+    :ivar status: Indicates whether the connection has been Approved/Rejected/Removed by the owner
+     of the service. Known values are: "Pending", "Approved", "Rejected", "Disconnected", and
+     "Timeout".
+    :vartype status: str or
+     ~azure.mgmt.machinelearningservices.models.PrivateEndpointServiceConnectionStatus
+    """
+
+    _attribute_map = {
+        "name": {"key": "name", "type": "str"},
+        "private_link_resource_id": {"key": "properties.privateLinkResourceId", "type": "str"},
+        "group_id": {"key": "properties.groupId", "type": "str"},
+        "request_message": {"key": "properties.requestMessage", "type": "str"},
+        "status": {"key": "properties.status", "type": "str"},
+    }
+
+    def __init__(
+        self,
+        *,
+        name: Optional[str] = None,
+        private_link_resource_id: Optional[str] = None,
+        group_id: Optional[str] = None,
+        request_message: Optional[str] = None,
+        status: Optional[Union[str, "_models.PrivateEndpointServiceConnectionStatus"]] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword name: Unique name of the private link.
+        :paramtype name: str
+        :keyword private_link_resource_id: The resource id that private link links to.
+        :paramtype private_link_resource_id: str
+        :keyword group_id: The private link resource group id.
+        :paramtype group_id: str
+        :keyword request_message: Request message.
+        :paramtype request_message: str
+        :keyword status: Indicates whether the connection has been Approved/Rejected/Removed by the
+         owner of the service. Known values are: "Pending", "Approved", "Rejected", "Disconnected", and
+         "Timeout".
+        :paramtype status: str or
+         ~azure.mgmt.machinelearningservices.models.PrivateEndpointServiceConnectionStatus
+        """
+        super().__init__(**kwargs)
+        self.name = name
+        self.private_link_resource_id = private_link_resource_id
+        self.group_id = group_id
+        self.request_message = request_message
+        self.status = status
+
+
+class Sku(_serialization.Model):
+    """The resource model definition representing SKU.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar name: The name of the SKU. Ex - P3. It is typically a letter+number code. Required.
+    :vartype name: str
+    :ivar tier: This field is required to be implemented by the Resource Provider if the service
+     has more than one tier, but is not required on a PUT. Known values are: "Free", "Basic",
+     "Standard", and "Premium".
+    :vartype tier: str or ~azure.mgmt.machinelearningservices.models.SkuTier
+    :ivar size: The SKU size. When the name field is the combination of tier and some other value,
+     this would be the standalone code.
+    :vartype size: str
+    :ivar family: If the service has different generations of hardware, for the same SKU, then that
+     can be captured here.
+    :vartype family: str
+    :ivar capacity: If the SKU supports scale out/in then the capacity integer should be included.
+     If scale out/in is not possible for the resource this may be omitted.
+    :vartype capacity: int
+    """
+
+    _validation = {
+        "name": {"required": True},
+    }
+
+    _attribute_map = {
+        "name": {"key": "name", "type": "str"},
+        "tier": {"key": "tier", "type": "str"},
+        "size": {"key": "size", "type": "str"},
+        "family": {"key": "family", "type": "str"},
+        "capacity": {"key": "capacity", "type": "int"},
+    }
+
+    def __init__(
+        self,
+        *,
+        name: str,
+        tier: Optional[Union[str, "_models.SkuTier"]] = None,
+        size: Optional[str] = None,
+        family: Optional[str] = None,
+        capacity: Optional[int] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword name: The name of the SKU. Ex - P3. It is typically a letter+number code. Required.
+        :paramtype name: str
+        :keyword tier: This field is required to be implemented by the Resource Provider if the service
+         has more than one tier, but is not required on a PUT. Known values are: "Free", "Basic",
+         "Standard", and "Premium".
+        :paramtype tier: str or ~azure.mgmt.machinelearningservices.models.SkuTier
+        :keyword size: The SKU size. When the name field is the combination of tier and some other
+         value, this would be the standalone code.
+        :paramtype size: str
+        :keyword family: If the service has different generations of hardware, for the same SKU, then
+         that can be captured here.
+        :paramtype family: str
+        :keyword capacity: If the SKU supports scale out/in then the capacity integer should be
+         included. If scale out/in is not possible for the resource this may be omitted.
+        :paramtype capacity: int
+        """
+        super().__init__(**kwargs)
+        self.name = name
+        self.tier = tier
+        self.size = size
+        self.family = family
+        self.capacity = capacity
+
+
+class SkuCapacity(_serialization.Model):
+    """SKU capacity information.
+
+    :ivar default: Gets or sets the default capacity.
+    :vartype default: int
+    :ivar maximum: Gets or sets the maximum.
+    :vartype maximum: int
+    :ivar minimum: Gets or sets the minimum.
+    :vartype minimum: int
+    :ivar scale_type: Gets or sets the type of the scale. Known values are: "Automatic", "Manual",
+     and "None".
+    :vartype scale_type: str or ~azure.mgmt.machinelearningservices.models.SkuScaleType
+    """
+
+    _attribute_map = {
+        "default": {"key": "default", "type": "int"},
+        "maximum": {"key": "maximum", "type": "int"},
+        "minimum": {"key": "minimum", "type": "int"},
+        "scale_type": {"key": "scaleType", "type": "str"},
+    }
+
+    def __init__(
+        self,
+        *,
+        default: int = 0,
+        maximum: int = 0,
+        minimum: int = 0,
+        scale_type: Optional[Union[str, "_models.SkuScaleType"]] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword default: Gets or sets the default capacity.
+        :paramtype default: int
+        :keyword maximum: Gets or sets the maximum.
+        :paramtype maximum: int
+        :keyword minimum: Gets or sets the minimum.
+        :paramtype minimum: int
+        :keyword scale_type: Gets or sets the type of the scale. Known values are: "Automatic",
+         "Manual", and "None".
+        :paramtype scale_type: str or ~azure.mgmt.machinelearningservices.models.SkuScaleType
+        """
+        super().__init__(**kwargs)
+        self.default = default
+        self.maximum = maximum
+        self.minimum = minimum
+        self.scale_type = scale_type
+
+
+class SkuResource(_serialization.Model):
+    """Fulfills ARM Contract requirement to list all available SKUS for a resource.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar capacity: Gets or sets the Sku Capacity.
+    :vartype capacity: ~azure.mgmt.machinelearningservices.models.SkuCapacity
+    :ivar resource_type: The resource type name.
+    :vartype resource_type: str
+    :ivar sku: Gets or sets the Sku.
+    :vartype sku: ~azure.mgmt.machinelearningservices.models.SkuSetting
+    """
+
+    _validation = {
+        "resource_type": {"readonly": True},
+    }
+
+    _attribute_map = {
+        "capacity": {"key": "capacity", "type": "SkuCapacity"},
+        "resource_type": {"key": "resourceType", "type": "str"},
+        "sku": {"key": "sku", "type": "SkuSetting"},
     }
 
-    def __init__(self, *, scripts: Optional["_models.ScriptsToExecute"] = None, **kwargs):
+    def __init__(
+        self,
+        *,
+        capacity: Optional["_models.SkuCapacity"] = None,
+        sku: Optional["_models.SkuSetting"] = None,
+        **kwargs: Any
+    ) -> None:
         """
-        :keyword scripts: Customized setup scripts.
-        :paramtype scripts: ~azure.mgmt.machinelearningservices.models.ScriptsToExecute
+        :keyword capacity: Gets or sets the Sku Capacity.
+        :paramtype capacity: ~azure.mgmt.machinelearningservices.models.SkuCapacity
+        :keyword sku: Gets or sets the Sku.
+        :paramtype sku: ~azure.mgmt.machinelearningservices.models.SkuSetting
         """
         super().__init__(**kwargs)
-        self.scripts = scripts
+        self.capacity = capacity
+        self.resource_type = None
+        self.sku = sku
 
 
-class SharedPrivateLinkResource(_serialization.Model):
-    """SharedPrivateLinkResource.
+class SkuResourceArmPaginatedResult(_serialization.Model):
+    """A paginated list of SkuResource entities.
 
-    :ivar name: Unique name of the private link.
-    :vartype name: str
-    :ivar private_link_resource_id: The resource id that private link links to.
-    :vartype private_link_resource_id: str
-    :ivar group_id: The private link resource group id.
-    :vartype group_id: str
-    :ivar request_message: Request message.
-    :vartype request_message: str
-    :ivar status: Indicates whether the connection has been Approved/Rejected/Removed by the owner
-     of the service. Known values are: "Pending", "Approved", "Rejected", "Disconnected", and
-     "Timeout".
-    :vartype status: str or
-     ~azure.mgmt.machinelearningservices.models.PrivateEndpointServiceConnectionStatus
+    :ivar next_link: The link to the next page of SkuResource objects. If null, there are no
+     additional pages.
+    :vartype next_link: str
+    :ivar value: An array of objects of type SkuResource.
+    :vartype value: list[~azure.mgmt.machinelearningservices.models.SkuResource]
     """
 
     _attribute_map = {
-        "name": {"key": "name", "type": "str"},
-        "private_link_resource_id": {"key": "properties.privateLinkResourceId", "type": "str"},
-        "group_id": {"key": "properties.groupId", "type": "str"},
-        "request_message": {"key": "properties.requestMessage", "type": "str"},
-        "status": {"key": "properties.status", "type": "str"},
+        "next_link": {"key": "nextLink", "type": "str"},
+        "value": {"key": "value", "type": "[SkuResource]"},
     }
 
     def __init__(
-        self,
-        *,
-        name: Optional[str] = None,
-        private_link_resource_id: Optional[str] = None,
-        group_id: Optional[str] = None,
-        request_message: Optional[str] = None,
-        status: Optional[Union[str, "_models.PrivateEndpointServiceConnectionStatus"]] = None,
-        **kwargs
-    ):
+        self, *, next_link: Optional[str] = None, value: Optional[List["_models.SkuResource"]] = None, **kwargs: Any
+    ) -> None:
         """
-        :keyword name: Unique name of the private link.
-        :paramtype name: str
-        :keyword private_link_resource_id: The resource id that private link links to.
-        :paramtype private_link_resource_id: str
-        :keyword group_id: The private link resource group id.
-        :paramtype group_id: str
-        :keyword request_message: Request message.
-        :paramtype request_message: str
-        :keyword status: Indicates whether the connection has been Approved/Rejected/Removed by the
-         owner of the service. Known values are: "Pending", "Approved", "Rejected", "Disconnected", and
-         "Timeout".
-        :paramtype status: str or
-         ~azure.mgmt.machinelearningservices.models.PrivateEndpointServiceConnectionStatus
+        :keyword next_link: The link to the next page of SkuResource objects. If null, there are no
+         additional pages.
+        :paramtype next_link: str
+        :keyword value: An array of objects of type SkuResource.
+        :paramtype value: list[~azure.mgmt.machinelearningservices.models.SkuResource]
         """
         super().__init__(**kwargs)
-        self.name = name
-        self.private_link_resource_id = private_link_resource_id
-        self.group_id = group_id
-        self.request_message = request_message
-        self.status = status
+        self.next_link = next_link
+        self.value = value
 
 
-class Sku(_serialization.Model):
-    """The resource model definition representing SKU.
+class SkuSetting(_serialization.Model):
+    """SkuSetting fulfills the need for stripped down SKU info in ARM contract.
 
     All required parameters must be populated in order to send to Azure.
 
-    :ivar name: The name of the SKU. Ex - P3. It is typically a letter+number code. Required.
+    :ivar name: [Required] The name of the SKU. Ex - P3. It is typically a letter+number code.
+     Required.
     :vartype name: str
     :ivar tier: This field is required to be implemented by the Resource Provider if the service
      has more than one tier, but is not required on a PUT. Known values are: "Free", "Basic",
      "Standard", and "Premium".
     :vartype tier: str or ~azure.mgmt.machinelearningservices.models.SkuTier
-    :ivar size: The SKU size. When the name field is the combination of tier and some other value,
-     this would be the standalone code.
-    :vartype size: str
-    :ivar family: If the service has different generations of hardware, for the same SKU, then that
-     can be captured here.
-    :vartype family: str
-    :ivar capacity: If the SKU supports scale out/in then the capacity integer should be included.
-     If scale out/in is not possible for the resource this may be omitted.
-    :vartype capacity: int
     """
 
     _validation = {
-        "name": {"required": True},
+        "name": {"required": True, "pattern": r"[a-zA-Z0-9_]"},
     }
 
     _attribute_map = {
         "name": {"key": "name", "type": "str"},
         "tier": {"key": "tier", "type": "str"},
-        "size": {"key": "size", "type": "str"},
-        "family": {"key": "family", "type": "str"},
-        "capacity": {"key": "capacity", "type": "int"},
     }
 
-    def __init__(
-        self,
-        *,
-        name: str,
-        tier: Optional[Union[str, "_models.SkuTier"]] = None,
-        size: Optional[str] = None,
-        family: Optional[str] = None,
-        capacity: Optional[int] = None,
-        **kwargs
-    ):
+    def __init__(self, *, name: str, tier: Optional[Union[str, "_models.SkuTier"]] = None, **kwargs: Any) -> None:
         """
-        :keyword name: The name of the SKU. Ex - P3. It is typically a letter+number code. Required.
+        :keyword name: [Required] The name of the SKU. Ex - P3. It is typically a letter+number code.
+         Required.
         :paramtype name: str
         :keyword tier: This field is required to be implemented by the Resource Provider if the service
          has more than one tier, but is not required on a PUT. Known values are: "Free", "Basic",
          "Standard", and "Premium".
         :paramtype tier: str or ~azure.mgmt.machinelearningservices.models.SkuTier
-        :keyword size: The SKU size. When the name field is the combination of tier and some other
-         value, this would be the standalone code.
-        :paramtype size: str
-        :keyword family: If the service has different generations of hardware, for the same SKU, then
-         that can be captured here.
-        :paramtype family: str
-        :keyword capacity: If the SKU supports scale out/in then the capacity integer should be
-         included. If scale out/in is not possible for the resource this may be omitted.
-        :paramtype capacity: int
         """
         super().__init__(**kwargs)
         self.name = name
         self.tier = tier
-        self.size = size
-        self.family = family
-        self.capacity = capacity
 
 
-class SkuCapacity(_serialization.Model):
-    """SKU capacity information.
+class SparkJob(JobBaseProperties):  # pylint: disable=too-many-instance-attributes
+    """Spark job definition.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar description: The asset description text.
+    :vartype description: str
+    :ivar properties: The asset property dictionary.
+    :vartype properties: dict[str, str]
+    :ivar tags: Tag dictionary. Tags can be added, removed, and updated.
+    :vartype tags: dict[str, str]
+    :ivar component_id: ARM resource ID of the component resource.
+    :vartype component_id: str
+    :ivar compute_id: ARM resource ID of the compute resource.
+    :vartype compute_id: str
+    :ivar display_name: Display name of job.
+    :vartype display_name: str
+    :ivar experiment_name: The name of the experiment the job belongs to. If not set, the job is
+     placed in the "Default" experiment.
+    :vartype experiment_name: str
+    :ivar identity: Identity configuration. If set, this should be one of AmlToken,
+     ManagedIdentity, UserIdentity or null.
+     Defaults to AmlToken if null.
+    :vartype identity: ~azure.mgmt.machinelearningservices.models.IdentityConfiguration
+    :ivar is_archived: Is the asset archived?.
+    :vartype is_archived: bool
+    :ivar job_type: [Required] Specifies the type of job. Required. Known values are: "AutoML",
+     "Command", "Labeling", "Sweep", "Pipeline", and "Spark".
+    :vartype job_type: str or ~azure.mgmt.machinelearningservices.models.JobType
+    :ivar services: List of JobEndpoints.
+     For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
+    :vartype services: dict[str, ~azure.mgmt.machinelearningservices.models.JobService]
+    :ivar status: Status of the job. Known values are: "NotStarted", "Starting", "Provisioning",
+     "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed", "Failed",
+     "Canceled", "NotResponding", "Paused", "Unknown", and "Scheduled".
+    :vartype status: str or ~azure.mgmt.machinelearningservices.models.JobStatus
+    :ivar archives: Archive files used in the job.
+    :vartype archives: list[str]
+    :ivar args: Arguments for the job.
+    :vartype args: str
+    :ivar code_id: [Required] ARM resource ID of the code asset. Required.
+    :vartype code_id: str
+    :ivar conf: Spark configured properties.
+    :vartype conf: dict[str, str]
+    :ivar entry: [Required] The entry to execute on startup of the job. Required.
+    :vartype entry: ~azure.mgmt.machinelearningservices.models.SparkJobEntry
+    :ivar environment_id: The ARM resource ID of the Environment specification for the job.
+    :vartype environment_id: str
+    :ivar files: Files used in the job.
+    :vartype files: list[str]
+    :ivar inputs: Mapping of input data bindings used in the job.
+    :vartype inputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobInput]
+    :ivar jars: Jar files used in the job.
+    :vartype jars: list[str]
+    :ivar outputs: Mapping of output data bindings used in the job.
+    :vartype outputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobOutput]
+    :ivar py_files: Python files used in the job.
+    :vartype py_files: list[str]
+    :ivar resources: Compute Resource configuration for the job.
+    :vartype resources: ~azure.mgmt.machinelearningservices.models.SparkResourceConfiguration
+    """
+
+    _validation = {
+        "job_type": {"required": True},
+        "status": {"readonly": True},
+        "code_id": {"required": True, "pattern": r"[a-zA-Z0-9_]"},
+        "entry": {"required": True},
+    }
+
+    _attribute_map = {
+        "description": {"key": "description", "type": "str"},
+        "properties": {"key": "properties", "type": "{str}"},
+        "tags": {"key": "tags", "type": "{str}"},
+        "component_id": {"key": "componentId", "type": "str"},
+        "compute_id": {"key": "computeId", "type": "str"},
+        "display_name": {"key": "displayName", "type": "str"},
+        "experiment_name": {"key": "experimentName", "type": "str"},
+        "identity": {"key": "identity", "type": "IdentityConfiguration"},
+        "is_archived": {"key": "isArchived", "type": "bool"},
+        "job_type": {"key": "jobType", "type": "str"},
+        "services": {"key": "services", "type": "{JobService}"},
+        "status": {"key": "status", "type": "str"},
+        "archives": {"key": "archives", "type": "[str]"},
+        "args": {"key": "args", "type": "str"},
+        "code_id": {"key": "codeId", "type": "str"},
+        "conf": {"key": "conf", "type": "{str}"},
+        "entry": {"key": "entry", "type": "SparkJobEntry"},
+        "environment_id": {"key": "environmentId", "type": "str"},
+        "files": {"key": "files", "type": "[str]"},
+        "inputs": {"key": "inputs", "type": "{JobInput}"},
+        "jars": {"key": "jars", "type": "[str]"},
+        "outputs": {"key": "outputs", "type": "{JobOutput}"},
+        "py_files": {"key": "pyFiles", "type": "[str]"},
+        "resources": {"key": "resources", "type": "SparkResourceConfiguration"},
+    }
+
+    def __init__(  # pylint: disable=too-many-locals
+        self,
+        *,
+        code_id: str,
+        entry: "_models.SparkJobEntry",
+        description: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        tags: Optional[Dict[str, str]] = None,
+        component_id: Optional[str] = None,
+        compute_id: Optional[str] = None,
+        display_name: Optional[str] = None,
+        experiment_name: str = "Default",
+        identity: Optional["_models.IdentityConfiguration"] = None,
+        is_archived: bool = False,
+        services: Optional[Dict[str, "_models.JobService"]] = None,
+        archives: Optional[List[str]] = None,
+        args: Optional[str] = None,
+        conf: Optional[Dict[str, str]] = None,
+        environment_id: Optional[str] = None,
+        files: Optional[List[str]] = None,
+        inputs: Optional[Dict[str, "_models.JobInput"]] = None,
+        jars: Optional[List[str]] = None,
+        outputs: Optional[Dict[str, "_models.JobOutput"]] = None,
+        py_files: Optional[List[str]] = None,
+        resources: Optional["_models.SparkResourceConfiguration"] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword description: The asset description text.
+        :paramtype description: str
+        :keyword properties: The asset property dictionary.
+        :paramtype properties: dict[str, str]
+        :keyword tags: Tag dictionary. Tags can be added, removed, and updated.
+        :paramtype tags: dict[str, str]
+        :keyword component_id: ARM resource ID of the component resource.
+        :paramtype component_id: str
+        :keyword compute_id: ARM resource ID of the compute resource.
+        :paramtype compute_id: str
+        :keyword display_name: Display name of job.
+        :paramtype display_name: str
+        :keyword experiment_name: The name of the experiment the job belongs to. If not set, the job is
+         placed in the "Default" experiment.
+        :paramtype experiment_name: str
+        :keyword identity: Identity configuration. If set, this should be one of AmlToken,
+         ManagedIdentity, UserIdentity or null.
+         Defaults to AmlToken if null.
+        :paramtype identity: ~azure.mgmt.machinelearningservices.models.IdentityConfiguration
+        :keyword is_archived: Is the asset archived?.
+        :paramtype is_archived: bool
+        :keyword services: List of JobEndpoints.
+         For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
+        :paramtype services: dict[str, ~azure.mgmt.machinelearningservices.models.JobService]
+        :keyword archives: Archive files used in the job.
+        :paramtype archives: list[str]
+        :keyword args: Arguments for the job.
+        :paramtype args: str
+        :keyword code_id: [Required] ARM resource ID of the code asset. Required.
+        :paramtype code_id: str
+        :keyword conf: Spark configured properties.
+        :paramtype conf: dict[str, str]
+        :keyword entry: [Required] The entry to execute on startup of the job. Required.
+        :paramtype entry: ~azure.mgmt.machinelearningservices.models.SparkJobEntry
+        :keyword environment_id: The ARM resource ID of the Environment specification for the job.
+        :paramtype environment_id: str
+        :keyword files: Files used in the job.
+        :paramtype files: list[str]
+        :keyword inputs: Mapping of input data bindings used in the job.
+        :paramtype inputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobInput]
+        :keyword jars: Jar files used in the job.
+        :paramtype jars: list[str]
+        :keyword outputs: Mapping of output data bindings used in the job.
+        :paramtype outputs: dict[str, ~azure.mgmt.machinelearningservices.models.JobOutput]
+        :keyword py_files: Python files used in the job.
+        :paramtype py_files: list[str]
+        :keyword resources: Compute Resource configuration for the job.
+        :paramtype resources: ~azure.mgmt.machinelearningservices.models.SparkResourceConfiguration
+        """
+        super().__init__(
+            description=description,
+            properties=properties,
+            tags=tags,
+            component_id=component_id,
+            compute_id=compute_id,
+            display_name=display_name,
+            experiment_name=experiment_name,
+            identity=identity,
+            is_archived=is_archived,
+            services=services,
+            **kwargs
+        )
+        self.job_type: str = "Spark"
+        self.archives = archives
+        self.args = args
+        self.code_id = code_id
+        self.conf = conf
+        self.entry = entry
+        self.environment_id = environment_id
+        self.files = files
+        self.inputs = inputs
+        self.jars = jars
+        self.outputs = outputs
+        self.py_files = py_files
+        self.resources = resources
+
+
+class SparkJobEntry(_serialization.Model):
+    """Spark job entry point definition.
 
-    :ivar default: Gets or sets the default capacity.
-    :vartype default: int
-    :ivar maximum: Gets or sets the maximum.
-    :vartype maximum: int
-    :ivar minimum: Gets or sets the minimum.
-    :vartype minimum: int
-    :ivar scale_type: Gets or sets the type of the scale. Known values are: "Automatic", "Manual",
-     and "None".
-    :vartype scale_type: str or ~azure.mgmt.machinelearningservices.models.SkuScaleType
+    You probably want to use the sub-classes and not this class directly. Known sub-classes are:
+    SparkJobPythonEntry, SparkJobScalaEntry
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar spark_job_entry_type: [Required] Type of the job's entry point. Required. Known values
+     are: "SparkJobPythonEntry" and "SparkJobScalaEntry".
+    :vartype spark_job_entry_type: str or
+     ~azure.mgmt.machinelearningservices.models.SparkJobEntryType
     """
 
+    _validation = {
+        "spark_job_entry_type": {"required": True},
+    }
+
     _attribute_map = {
-        "default": {"key": "default", "type": "int"},
-        "maximum": {"key": "maximum", "type": "int"},
-        "minimum": {"key": "minimum", "type": "int"},
-        "scale_type": {"key": "scaleType", "type": "str"},
+        "spark_job_entry_type": {"key": "sparkJobEntryType", "type": "str"},
     }
 
-    def __init__(
-        self,
-        *,
-        default: int = 0,
-        maximum: int = 0,
-        minimum: int = 0,
-        scale_type: Optional[Union[str, "_models.SkuScaleType"]] = None,
-        **kwargs
-    ):
-        """
-        :keyword default: Gets or sets the default capacity.
-        :paramtype default: int
-        :keyword maximum: Gets or sets the maximum.
-        :paramtype maximum: int
-        :keyword minimum: Gets or sets the minimum.
-        :paramtype minimum: int
-        :keyword scale_type: Gets or sets the type of the scale. Known values are: "Automatic",
-         "Manual", and "None".
-        :paramtype scale_type: str or ~azure.mgmt.machinelearningservices.models.SkuScaleType
-        """
+    _subtype_map = {
+        "spark_job_entry_type": {
+            "SparkJobPythonEntry": "SparkJobPythonEntry",
+            "SparkJobScalaEntry": "SparkJobScalaEntry",
+        }
+    }
+
+    def __init__(self, **kwargs: Any) -> None:
+        """ """
         super().__init__(**kwargs)
-        self.default = default
-        self.maximum = maximum
-        self.minimum = minimum
-        self.scale_type = scale_type
+        self.spark_job_entry_type: Optional[str] = None
 
 
-class SkuResource(_serialization.Model):
-    """Fulfills ARM Contract requirement to list all available SKUS for a resource.
+class SparkJobPythonEntry(SparkJobEntry):
+    """SparkJobPythonEntry.
 
-    Variables are only populated by the server, and will be ignored when sending a request.
+    All required parameters must be populated in order to send to Azure.
 
-    :ivar capacity: Gets or sets the Sku Capacity.
-    :vartype capacity: ~azure.mgmt.machinelearningservices.models.SkuCapacity
-    :ivar resource_type: The resource type name.
-    :vartype resource_type: str
-    :ivar sku: Gets or sets the Sku.
-    :vartype sku: ~azure.mgmt.machinelearningservices.models.SkuSetting
+    :ivar spark_job_entry_type: [Required] Type of the job's entry point. Required. Known values
+     are: "SparkJobPythonEntry" and "SparkJobScalaEntry".
+    :vartype spark_job_entry_type: str or
+     ~azure.mgmt.machinelearningservices.models.SparkJobEntryType
+    :ivar file: [Required] Relative python file path for job entry point. Required.
+    :vartype file: str
     """
 
     _validation = {
-        "resource_type": {"readonly": True},
+        "spark_job_entry_type": {"required": True},
+        "file": {"required": True, "min_length": 1, "pattern": r"[a-zA-Z0-9_]"},
     }
 
     _attribute_map = {
-        "capacity": {"key": "capacity", "type": "SkuCapacity"},
-        "resource_type": {"key": "resourceType", "type": "str"},
-        "sku": {"key": "sku", "type": "SkuSetting"},
+        "spark_job_entry_type": {"key": "sparkJobEntryType", "type": "str"},
+        "file": {"key": "file", "type": "str"},
     }
 
-    def __init__(
-        self, *, capacity: Optional["_models.SkuCapacity"] = None, sku: Optional["_models.SkuSetting"] = None, **kwargs
-    ):
+    def __init__(self, *, file: str, **kwargs: Any) -> None:
         """
-        :keyword capacity: Gets or sets the Sku Capacity.
-        :paramtype capacity: ~azure.mgmt.machinelearningservices.models.SkuCapacity
-        :keyword sku: Gets or sets the Sku.
-        :paramtype sku: ~azure.mgmt.machinelearningservices.models.SkuSetting
+        :keyword file: [Required] Relative python file path for job entry point. Required.
+        :paramtype file: str
         """
         super().__init__(**kwargs)
-        self.capacity = capacity
-        self.resource_type = None
-        self.sku = sku
+        self.spark_job_entry_type: str = "SparkJobPythonEntry"
+        self.file = file
 
 
-class SkuResourceArmPaginatedResult(_serialization.Model):
-    """A paginated list of SkuResource entities.
+class SparkJobScalaEntry(SparkJobEntry):
+    """SparkJobScalaEntry.
 
-    :ivar next_link: The link to the next page of SkuResource objects. If null, there are no
-     additional pages.
-    :vartype next_link: str
-    :ivar value: An array of objects of type SkuResource.
-    :vartype value: list[~azure.mgmt.machinelearningservices.models.SkuResource]
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar spark_job_entry_type: [Required] Type of the job's entry point. Required. Known values
+     are: "SparkJobPythonEntry" and "SparkJobScalaEntry".
+    :vartype spark_job_entry_type: str or
+     ~azure.mgmt.machinelearningservices.models.SparkJobEntryType
+    :ivar class_name: [Required] Scala class name used as entry point. Required.
+    :vartype class_name: str
     """
 
+    _validation = {
+        "spark_job_entry_type": {"required": True},
+        "class_name": {"required": True, "min_length": 1, "pattern": r"[a-zA-Z0-9_]"},
+    }
+
     _attribute_map = {
-        "next_link": {"key": "nextLink", "type": "str"},
-        "value": {"key": "value", "type": "[SkuResource]"},
+        "spark_job_entry_type": {"key": "sparkJobEntryType", "type": "str"},
+        "class_name": {"key": "className", "type": "str"},
     }
 
-    def __init__(
-        self, *, next_link: Optional[str] = None, value: Optional[List["_models.SkuResource"]] = None, **kwargs
-    ):
+    def __init__(self, *, class_name: str, **kwargs: Any) -> None:
         """
-        :keyword next_link: The link to the next page of SkuResource objects. If null, there are no
-         additional pages.
-        :paramtype next_link: str
-        :keyword value: An array of objects of type SkuResource.
-        :paramtype value: list[~azure.mgmt.machinelearningservices.models.SkuResource]
+        :keyword class_name: [Required] Scala class name used as entry point. Required.
+        :paramtype class_name: str
         """
         super().__init__(**kwargs)
-        self.next_link = next_link
-        self.value = value
-
+        self.spark_job_entry_type: str = "SparkJobScalaEntry"
+        self.class_name = class_name
 
-class SkuSetting(_serialization.Model):
-    """SkuSetting fulfills the need for stripped down SKU info in ARM contract.
 
-    All required parameters must be populated in order to send to Azure.
+class SparkResourceConfiguration(_serialization.Model):
+    """SparkResourceConfiguration.
 
-    :ivar name: [Required] The name of the SKU. Ex - P3. It is typically a letter+number code.
-     Required.
-    :vartype name: str
-    :ivar tier: This field is required to be implemented by the Resource Provider if the service
-     has more than one tier, but is not required on a PUT. Known values are: "Free", "Basic",
-     "Standard", and "Premium".
-    :vartype tier: str or ~azure.mgmt.machinelearningservices.models.SkuTier
+    :ivar instance_type: Optional type of VM used as supported by the compute target.
+    :vartype instance_type: str
+    :ivar runtime_version: Version of spark runtime used for the job.
+    :vartype runtime_version: str
     """
 
-    _validation = {
-        "name": {"required": True, "pattern": r"[a-zA-Z0-9_]"},
-    }
-
     _attribute_map = {
-        "name": {"key": "name", "type": "str"},
-        "tier": {"key": "tier", "type": "str"},
+        "instance_type": {"key": "instanceType", "type": "str"},
+        "runtime_version": {"key": "runtimeVersion", "type": "str"},
     }
 
-    def __init__(self, *, name: str, tier: Optional[Union[str, "_models.SkuTier"]] = None, **kwargs):
+    def __init__(self, *, instance_type: Optional[str] = None, runtime_version: str = "3.1", **kwargs: Any) -> None:
         """
-        :keyword name: [Required] The name of the SKU. Ex - P3. It is typically a letter+number code.
-         Required.
-        :paramtype name: str
-        :keyword tier: This field is required to be implemented by the Resource Provider if the service
-         has more than one tier, but is not required on a PUT. Known values are: "Free", "Basic",
-         "Standard", and "Premium".
-        :paramtype tier: str or ~azure.mgmt.machinelearningservices.models.SkuTier
+        :keyword instance_type: Optional type of VM used as supported by the compute target.
+        :paramtype instance_type: str
+        :keyword runtime_version: Version of spark runtime used for the job.
+        :paramtype runtime_version: str
         """
         super().__init__(**kwargs)
-        self.name = name
-        self.tier = tier
+        self.instance_type = instance_type
+        self.runtime_version = runtime_version
 
 
 class SslConfiguration(_serialization.Model):
@@ -18037,8 +21444,8 @@ def __init__(
         cname: Optional[str] = None,
         leaf_domain_label: Optional[str] = None,
         overwrite_existing_domain: Optional[bool] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword status: Enable or disable ssl for scoring. Known values are: "Disabled", "Enabled",
          and "Auto".
@@ -18093,8 +21500,8 @@ def __init__(
         stack_meta_learner_k_wargs: Optional[JSON] = None,
         stack_meta_learner_train_percentage: float = 0.2,
         stack_meta_learner_type: Optional[Union[str, "_models.StackMetaLearnerType"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword stack_meta_learner_k_wargs: Optional parameters to pass to the initializer of the
          meta-learner.
@@ -18116,6 +21523,81 @@ def __init__(
         self.stack_meta_learner_type = stack_meta_learner_type
 
 
+class StatusMessage(_serialization.Model):
+    """Active message associated with project.
+
+    Variables are only populated by the server, and will be ignored when sending a request.
+
+    :ivar code: Service-defined message code.
+    :vartype code: str
+    :ivar created_date_time: Time in UTC at which the message was created.
+    :vartype created_date_time: ~datetime.datetime
+    :ivar level: Severity level of message. Known values are: "Error", "Information", and
+     "Warning".
+    :vartype level: str or ~azure.mgmt.machinelearningservices.models.StatusMessageLevel
+    :ivar message: A human-readable representation of the message code.
+    :vartype message: str
+    """
+
+    _validation = {
+        "code": {"readonly": True},
+        "created_date_time": {"readonly": True},
+        "level": {"readonly": True},
+        "message": {"readonly": True},
+    }
+
+    _attribute_map = {
+        "code": {"key": "code", "type": "str"},
+        "created_date_time": {"key": "createdDateTime", "type": "iso-8601"},
+        "level": {"key": "level", "type": "str"},
+        "message": {"key": "message", "type": "str"},
+    }
+
+    def __init__(self, **kwargs: Any) -> None:
+        """ """
+        super().__init__(**kwargs)
+        self.code = None
+        self.created_date_time = None
+        self.level = None
+        self.message = None
+
+
+class StorageAccountDetails(_serialization.Model):
+    """Details of storage account to be used for the Registry.
+
+    :ivar system_created_storage_account:
+    :vartype system_created_storage_account:
+     ~azure.mgmt.machinelearningservices.models.SystemCreatedStorageAccount
+    :ivar user_created_storage_account:
+    :vartype user_created_storage_account:
+     ~azure.mgmt.machinelearningservices.models.UserCreatedStorageAccount
+    """
+
+    _attribute_map = {
+        "system_created_storage_account": {"key": "systemCreatedStorageAccount", "type": "SystemCreatedStorageAccount"},
+        "user_created_storage_account": {"key": "userCreatedStorageAccount", "type": "UserCreatedStorageAccount"},
+    }
+
+    def __init__(
+        self,
+        *,
+        system_created_storage_account: Optional["_models.SystemCreatedStorageAccount"] = None,
+        user_created_storage_account: Optional["_models.UserCreatedStorageAccount"] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword system_created_storage_account:
+        :paramtype system_created_storage_account:
+         ~azure.mgmt.machinelearningservices.models.SystemCreatedStorageAccount
+        :keyword user_created_storage_account:
+        :paramtype user_created_storage_account:
+         ~azure.mgmt.machinelearningservices.models.UserCreatedStorageAccount
+        """
+        super().__init__(**kwargs)
+        self.system_created_storage_account = system_created_storage_account
+        self.user_created_storage_account = user_created_storage_account
+
+
 class SweepJob(JobBaseProperties):  # pylint: disable=too-many-instance-attributes
     """Sweep job definition.
 
@@ -18145,14 +21627,14 @@ class SweepJob(JobBaseProperties):  # pylint: disable=too-many-instance-attribut
     :ivar is_archived: Is the asset archived?.
     :vartype is_archived: bool
     :ivar job_type: [Required] Specifies the type of job. Required. Known values are: "AutoML",
-     "Command", "Sweep", and "Pipeline".
+     "Command", "Labeling", "Sweep", "Pipeline", and "Spark".
     :vartype job_type: str or ~azure.mgmt.machinelearningservices.models.JobType
     :ivar services: List of JobEndpoints.
      For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
     :vartype services: dict[str, ~azure.mgmt.machinelearningservices.models.JobService]
     :ivar status: Status of the job. Known values are: "NotStarted", "Starting", "Provisioning",
      "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed", "Failed",
-     "Canceled", "NotResponding", "Paused", and "Unknown".
+     "Canceled", "NotResponding", "Paused", "Unknown", and "Scheduled".
     :vartype status: str or ~azure.mgmt.machinelearningservices.models.JobStatus
     :ivar early_termination: Early termination policies enable canceling poor-performing runs
      before they complete.
@@ -18227,8 +21709,8 @@ def __init__(
         inputs: Optional[Dict[str, "_models.JobInput"]] = None,
         limits: Optional["_models.SweepJobLimits"] = None,
         outputs: Optional[Dict[str, "_models.JobOutput"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -18335,8 +21817,8 @@ def __init__(
         max_concurrent_trials: Optional[int] = None,
         max_total_trials: Optional[int] = None,
         trial_timeout: Optional[datetime.timedelta] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword timeout: The max run duration in ISO 8601 format, after which the job will be
          cancelled. Only supports duration with precision as low as Seconds.
@@ -18370,7 +21852,7 @@ class SynapseSpark(Compute):  # pylint: disable=too-many-instance-attributes
     :vartype compute_location: str
     :ivar provisioning_state: The provision state of the cluster. Valid values are Unknown,
      Updating, Provisioning, Succeeded, and Failed. Known values are: "Unknown", "Updating",
-     "Creating", "Deleting", "Succeeded", "Failed", and "Canceled".
+     "Creating", "Deleting", "Succeeded", "Failed", "Canceled", and "SoftDeleted".
     :vartype provisioning_state: str or
      ~azure.mgmt.machinelearningservices.models.ProvisioningState
     :ivar description: The description of the Machine Learning compute.
@@ -18424,8 +21906,8 @@ def __init__(
         resource_id: Optional[str] = None,
         disable_local_auth: Optional[bool] = None,
         properties: Optional["_models.SynapseSparkProperties"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword compute_location: Location for the underlying compute.
         :paramtype compute_location: str
@@ -18501,8 +21983,8 @@ def __init__(
         resource_group: Optional[str] = None,
         workspace_name: Optional[str] = None,
         pool_name: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword auto_scale_properties: Auto scale properties.
         :paramtype auto_scale_properties:
@@ -18540,6 +22022,100 @@ def __init__(
         self.pool_name = pool_name
 
 
+class SystemCreatedAcrAccount(_serialization.Model):
+    """SystemCreatedAcrAccount.
+
+    :ivar acr_account_sku:
+    :vartype acr_account_sku: str
+    :ivar arm_resource_id: ARM ResourceId of a resource.
+    :vartype arm_resource_id: ~azure.mgmt.machinelearningservices.models.ArmResourceId
+    """
+
+    _attribute_map = {
+        "acr_account_sku": {"key": "acrAccountSku", "type": "str"},
+        "arm_resource_id": {"key": "armResourceId", "type": "ArmResourceId"},
+    }
+
+    def __init__(
+        self,
+        *,
+        acr_account_sku: Optional[str] = None,
+        arm_resource_id: Optional["_models.ArmResourceId"] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword acr_account_sku:
+        :paramtype acr_account_sku: str
+        :keyword arm_resource_id: ARM ResourceId of a resource.
+        :paramtype arm_resource_id: ~azure.mgmt.machinelearningservices.models.ArmResourceId
+        """
+        super().__init__(**kwargs)
+        self.acr_account_sku = acr_account_sku
+        self.arm_resource_id = arm_resource_id
+
+
+class SystemCreatedStorageAccount(_serialization.Model):
+    """SystemCreatedStorageAccount.
+
+    :ivar arm_resource_id: ARM ResourceId of a resource.
+    :vartype arm_resource_id: ~azure.mgmt.machinelearningservices.models.ArmResourceId
+    :ivar storage_account_hns_enabled:
+    :vartype storage_account_hns_enabled: bool
+    :ivar storage_account_type: Allowed values:
+     "Standard_LRS",
+     "Standard_GRS",
+     "Standard_RAGRS",
+     "Standard_ZRS",
+     "Standard_GZRS",
+     "Standard_RAGZRS",
+     "Premium_LRS",
+     "Premium_ZRS".
+    :vartype storage_account_type: str
+    :ivar allow_blob_public_access:
+    :vartype allow_blob_public_access: bool
+    """
+
+    _attribute_map = {
+        "arm_resource_id": {"key": "armResourceId", "type": "ArmResourceId"},
+        "storage_account_hns_enabled": {"key": "storageAccountHnsEnabled", "type": "bool"},
+        "storage_account_type": {"key": "storageAccountType", "type": "str"},
+        "allow_blob_public_access": {"key": "allowBlobPublicAccess", "type": "bool"},
+    }
+
+    def __init__(
+        self,
+        *,
+        arm_resource_id: Optional["_models.ArmResourceId"] = None,
+        storage_account_hns_enabled: Optional[bool] = None,
+        storage_account_type: Optional[str] = None,
+        allow_blob_public_access: Optional[bool] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword arm_resource_id: ARM ResourceId of a resource.
+        :paramtype arm_resource_id: ~azure.mgmt.machinelearningservices.models.ArmResourceId
+        :keyword storage_account_hns_enabled:
+        :paramtype storage_account_hns_enabled: bool
+        :keyword storage_account_type: Allowed values:
+         "Standard_LRS",
+         "Standard_GRS",
+         "Standard_RAGRS",
+         "Standard_ZRS",
+         "Standard_GZRS",
+         "Standard_RAGZRS",
+         "Premium_LRS",
+         "Premium_ZRS".
+        :paramtype storage_account_type: str
+        :keyword allow_blob_public_access:
+        :paramtype allow_blob_public_access: bool
+        """
+        super().__init__(**kwargs)
+        self.arm_resource_id = arm_resource_id
+        self.storage_account_hns_enabled = storage_account_hns_enabled
+        self.storage_account_type = storage_account_type
+        self.allow_blob_public_access = allow_blob_public_access
+
+
 class SystemData(_serialization.Model):
     """Metadata pertaining to creation and last modification of the resource.
 
@@ -18577,8 +22153,8 @@ def __init__(
         last_modified_by: Optional[str] = None,
         last_modified_by_type: Optional[Union[str, "_models.CreatedByType"]] = None,
         last_modified_at: Optional[datetime.datetime] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword created_by: The identity that created the resource.
         :paramtype created_by: str
@@ -18625,17 +22201,383 @@ class SystemService(_serialization.Model):
     }
 
     _attribute_map = {
-        "system_service_type": {"key": "systemServiceType", "type": "str"},
-        "public_ip_address": {"key": "publicIpAddress", "type": "str"},
-        "version": {"key": "version", "type": "str"},
+        "system_service_type": {"key": "systemServiceType", "type": "str"},
+        "public_ip_address": {"key": "publicIpAddress", "type": "str"},
+        "version": {"key": "version", "type": "str"},
+    }
+
+    def __init__(self, **kwargs: Any) -> None:
+        """ """
+        super().__init__(**kwargs)
+        self.system_service_type = None
+        self.public_ip_address = None
+        self.version = None
+
+
+class TableFixedParameters(_serialization.Model):  # pylint: disable=too-many-instance-attributes
+    """Fixed training parameters that won't be swept over during AutoML Table training.
+
+    :ivar booster: Specify the boosting type, e.g gbdt for XGBoost.
+    :vartype booster: str
+    :ivar boosting_type: Specify the boosting type, e.g gbdt for LightGBM.
+    :vartype boosting_type: str
+    :ivar grow_policy: Specify the grow policy, which controls the way new nodes are added to the
+     tree.
+    :vartype grow_policy: str
+    :ivar learning_rate: The learning rate for the training procedure.
+    :vartype learning_rate: float
+    :ivar max_bin: Specify the Maximum number of discrete bins to bucket continuous features .
+    :vartype max_bin: int
+    :ivar max_depth: Specify the max depth to limit the tree depth explicitly.
+    :vartype max_depth: int
+    :ivar max_leaves: Specify the max leaves to limit the tree leaves explicitly.
+    :vartype max_leaves: int
+    :ivar min_data_in_leaf: The minimum number of data per leaf.
+    :vartype min_data_in_leaf: int
+    :ivar min_split_gain: Minimum loss reduction required to make a further partition on a leaf
+     node of the tree.
+    :vartype min_split_gain: float
+    :ivar model_name: The name of the model to train.
+    :vartype model_name: str
+    :ivar n_estimators: Specify the number of trees (or rounds) in an model.
+    :vartype n_estimators: int
+    :ivar num_leaves: Specify the number of leaves.
+    :vartype num_leaves: int
+    :ivar preprocessor_name: The name of the preprocessor to use.
+    :vartype preprocessor_name: str
+    :ivar reg_alpha: L1 regularization term on weights.
+    :vartype reg_alpha: float
+    :ivar reg_lambda: L2 regularization term on weights.
+    :vartype reg_lambda: float
+    :ivar subsample: Subsample ratio of the training instance.
+    :vartype subsample: float
+    :ivar subsample_freq: Frequency of subsample.
+    :vartype subsample_freq: float
+    :ivar tree_method: Specify the tree method.
+    :vartype tree_method: str
+    :ivar with_mean: If true, center before scaling the data with StandardScalar.
+    :vartype with_mean: bool
+    :ivar with_std: If true, scaling the data with Unit Variance with StandardScalar.
+    :vartype with_std: bool
+    """
+
+    _attribute_map = {
+        "booster": {"key": "booster", "type": "str"},
+        "boosting_type": {"key": "boostingType", "type": "str"},
+        "grow_policy": {"key": "growPolicy", "type": "str"},
+        "learning_rate": {"key": "learningRate", "type": "float"},
+        "max_bin": {"key": "maxBin", "type": "int"},
+        "max_depth": {"key": "maxDepth", "type": "int"},
+        "max_leaves": {"key": "maxLeaves", "type": "int"},
+        "min_data_in_leaf": {"key": "minDataInLeaf", "type": "int"},
+        "min_split_gain": {"key": "minSplitGain", "type": "float"},
+        "model_name": {"key": "modelName", "type": "str"},
+        "n_estimators": {"key": "nEstimators", "type": "int"},
+        "num_leaves": {"key": "numLeaves", "type": "int"},
+        "preprocessor_name": {"key": "preprocessorName", "type": "str"},
+        "reg_alpha": {"key": "regAlpha", "type": "float"},
+        "reg_lambda": {"key": "regLambda", "type": "float"},
+        "subsample": {"key": "subsample", "type": "float"},
+        "subsample_freq": {"key": "subsampleFreq", "type": "float"},
+        "tree_method": {"key": "treeMethod", "type": "str"},
+        "with_mean": {"key": "withMean", "type": "bool"},
+        "with_std": {"key": "withStd", "type": "bool"},
+    }
+
+    def __init__(
+        self,
+        *,
+        booster: Optional[str] = None,
+        boosting_type: Optional[str] = None,
+        grow_policy: Optional[str] = None,
+        learning_rate: Optional[float] = None,
+        max_bin: Optional[int] = None,
+        max_depth: Optional[int] = None,
+        max_leaves: Optional[int] = None,
+        min_data_in_leaf: Optional[int] = None,
+        min_split_gain: Optional[float] = None,
+        model_name: Optional[str] = None,
+        n_estimators: Optional[int] = None,
+        num_leaves: Optional[int] = None,
+        preprocessor_name: Optional[str] = None,
+        reg_alpha: Optional[float] = None,
+        reg_lambda: Optional[float] = None,
+        subsample: Optional[float] = None,
+        subsample_freq: Optional[float] = None,
+        tree_method: Optional[str] = None,
+        with_mean: bool = False,
+        with_std: bool = False,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword booster: Specify the boosting type, e.g gbdt for XGBoost.
+        :paramtype booster: str
+        :keyword boosting_type: Specify the boosting type, e.g gbdt for LightGBM.
+        :paramtype boosting_type: str
+        :keyword grow_policy: Specify the grow policy, which controls the way new nodes are added to
+         the tree.
+        :paramtype grow_policy: str
+        :keyword learning_rate: The learning rate for the training procedure.
+        :paramtype learning_rate: float
+        :keyword max_bin: Specify the Maximum number of discrete bins to bucket continuous features .
+        :paramtype max_bin: int
+        :keyword max_depth: Specify the max depth to limit the tree depth explicitly.
+        :paramtype max_depth: int
+        :keyword max_leaves: Specify the max leaves to limit the tree leaves explicitly.
+        :paramtype max_leaves: int
+        :keyword min_data_in_leaf: The minimum number of data per leaf.
+        :paramtype min_data_in_leaf: int
+        :keyword min_split_gain: Minimum loss reduction required to make a further partition on a leaf
+         node of the tree.
+        :paramtype min_split_gain: float
+        :keyword model_name: The name of the model to train.
+        :paramtype model_name: str
+        :keyword n_estimators: Specify the number of trees (or rounds) in an model.
+        :paramtype n_estimators: int
+        :keyword num_leaves: Specify the number of leaves.
+        :paramtype num_leaves: int
+        :keyword preprocessor_name: The name of the preprocessor to use.
+        :paramtype preprocessor_name: str
+        :keyword reg_alpha: L1 regularization term on weights.
+        :paramtype reg_alpha: float
+        :keyword reg_lambda: L2 regularization term on weights.
+        :paramtype reg_lambda: float
+        :keyword subsample: Subsample ratio of the training instance.
+        :paramtype subsample: float
+        :keyword subsample_freq: Frequency of subsample.
+        :paramtype subsample_freq: float
+        :keyword tree_method: Specify the tree method.
+        :paramtype tree_method: str
+        :keyword with_mean: If true, center before scaling the data with StandardScalar.
+        :paramtype with_mean: bool
+        :keyword with_std: If true, scaling the data with Unit Variance with StandardScalar.
+        :paramtype with_std: bool
+        """
+        super().__init__(**kwargs)
+        self.booster = booster
+        self.boosting_type = boosting_type
+        self.grow_policy = grow_policy
+        self.learning_rate = learning_rate
+        self.max_bin = max_bin
+        self.max_depth = max_depth
+        self.max_leaves = max_leaves
+        self.min_data_in_leaf = min_data_in_leaf
+        self.min_split_gain = min_split_gain
+        self.model_name = model_name
+        self.n_estimators = n_estimators
+        self.num_leaves = num_leaves
+        self.preprocessor_name = preprocessor_name
+        self.reg_alpha = reg_alpha
+        self.reg_lambda = reg_lambda
+        self.subsample = subsample
+        self.subsample_freq = subsample_freq
+        self.tree_method = tree_method
+        self.with_mean = with_mean
+        self.with_std = with_std
+
+
+class TableParameterSubspace(_serialization.Model):  # pylint: disable=too-many-instance-attributes
+    """TableParameterSubspace.
+
+    :ivar booster: Specify the boosting type, e.g gbdt for XGBoost.
+    :vartype booster: str
+    :ivar boosting_type: Specify the boosting type, e.g gbdt for LightGBM.
+    :vartype boosting_type: str
+    :ivar grow_policy: Specify the grow policy, which controls the way new nodes are added to the
+     tree.
+    :vartype grow_policy: str
+    :ivar learning_rate: The learning rate for the training procedure.
+    :vartype learning_rate: str
+    :ivar max_bin: Specify the Maximum number of discrete bins to bucket continuous features .
+    :vartype max_bin: str
+    :ivar max_depth: Specify the max depth to limit the tree depth explicitly.
+    :vartype max_depth: str
+    :ivar max_leaves: Specify the max leaves to limit the tree leaves explicitly.
+    :vartype max_leaves: str
+    :ivar min_data_in_leaf: The minimum number of data per leaf.
+    :vartype min_data_in_leaf: str
+    :ivar min_split_gain: Minimum loss reduction required to make a further partition on a leaf
+     node of the tree.
+    :vartype min_split_gain: str
+    :ivar model_name: The name of the model to train.
+    :vartype model_name: str
+    :ivar n_estimators: Specify the number of trees (or rounds) in an model.
+    :vartype n_estimators: str
+    :ivar num_leaves: Specify the number of leaves.
+    :vartype num_leaves: str
+    :ivar preprocessor_name: The name of the preprocessor to use.
+    :vartype preprocessor_name: str
+    :ivar reg_alpha: L1 regularization term on weights.
+    :vartype reg_alpha: str
+    :ivar reg_lambda: L2 regularization term on weights.
+    :vartype reg_lambda: str
+    :ivar subsample: Subsample ratio of the training instance.
+    :vartype subsample: str
+    :ivar subsample_freq: Frequency of subsample.
+    :vartype subsample_freq: str
+    :ivar tree_method: Specify the tree method.
+    :vartype tree_method: str
+    :ivar with_mean: If true, center before scaling the data with StandardScalar.
+    :vartype with_mean: str
+    :ivar with_std: If true, scaling the data with Unit Variance with StandardScalar.
+    :vartype with_std: str
+    """
+
+    _attribute_map = {
+        "booster": {"key": "booster", "type": "str"},
+        "boosting_type": {"key": "boostingType", "type": "str"},
+        "grow_policy": {"key": "growPolicy", "type": "str"},
+        "learning_rate": {"key": "learningRate", "type": "str"},
+        "max_bin": {"key": "maxBin", "type": "str"},
+        "max_depth": {"key": "maxDepth", "type": "str"},
+        "max_leaves": {"key": "maxLeaves", "type": "str"},
+        "min_data_in_leaf": {"key": "minDataInLeaf", "type": "str"},
+        "min_split_gain": {"key": "minSplitGain", "type": "str"},
+        "model_name": {"key": "modelName", "type": "str"},
+        "n_estimators": {"key": "nEstimators", "type": "str"},
+        "num_leaves": {"key": "numLeaves", "type": "str"},
+        "preprocessor_name": {"key": "preprocessorName", "type": "str"},
+        "reg_alpha": {"key": "regAlpha", "type": "str"},
+        "reg_lambda": {"key": "regLambda", "type": "str"},
+        "subsample": {"key": "subsample", "type": "str"},
+        "subsample_freq": {"key": "subsampleFreq", "type": "str"},
+        "tree_method": {"key": "treeMethod", "type": "str"},
+        "with_mean": {"key": "withMean", "type": "str"},
+        "with_std": {"key": "withStd", "type": "str"},
+    }
+
+    def __init__(
+        self,
+        *,
+        booster: Optional[str] = None,
+        boosting_type: Optional[str] = None,
+        grow_policy: Optional[str] = None,
+        learning_rate: Optional[str] = None,
+        max_bin: Optional[str] = None,
+        max_depth: Optional[str] = None,
+        max_leaves: Optional[str] = None,
+        min_data_in_leaf: Optional[str] = None,
+        min_split_gain: Optional[str] = None,
+        model_name: Optional[str] = None,
+        n_estimators: Optional[str] = None,
+        num_leaves: Optional[str] = None,
+        preprocessor_name: Optional[str] = None,
+        reg_alpha: Optional[str] = None,
+        reg_lambda: Optional[str] = None,
+        subsample: Optional[str] = None,
+        subsample_freq: Optional[str] = None,
+        tree_method: Optional[str] = None,
+        with_mean: Optional[str] = None,
+        with_std: Optional[str] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword booster: Specify the boosting type, e.g gbdt for XGBoost.
+        :paramtype booster: str
+        :keyword boosting_type: Specify the boosting type, e.g gbdt for LightGBM.
+        :paramtype boosting_type: str
+        :keyword grow_policy: Specify the grow policy, which controls the way new nodes are added to
+         the tree.
+        :paramtype grow_policy: str
+        :keyword learning_rate: The learning rate for the training procedure.
+        :paramtype learning_rate: str
+        :keyword max_bin: Specify the Maximum number of discrete bins to bucket continuous features .
+        :paramtype max_bin: str
+        :keyword max_depth: Specify the max depth to limit the tree depth explicitly.
+        :paramtype max_depth: str
+        :keyword max_leaves: Specify the max leaves to limit the tree leaves explicitly.
+        :paramtype max_leaves: str
+        :keyword min_data_in_leaf: The minimum number of data per leaf.
+        :paramtype min_data_in_leaf: str
+        :keyword min_split_gain: Minimum loss reduction required to make a further partition on a leaf
+         node of the tree.
+        :paramtype min_split_gain: str
+        :keyword model_name: The name of the model to train.
+        :paramtype model_name: str
+        :keyword n_estimators: Specify the number of trees (or rounds) in an model.
+        :paramtype n_estimators: str
+        :keyword num_leaves: Specify the number of leaves.
+        :paramtype num_leaves: str
+        :keyword preprocessor_name: The name of the preprocessor to use.
+        :paramtype preprocessor_name: str
+        :keyword reg_alpha: L1 regularization term on weights.
+        :paramtype reg_alpha: str
+        :keyword reg_lambda: L2 regularization term on weights.
+        :paramtype reg_lambda: str
+        :keyword subsample: Subsample ratio of the training instance.
+        :paramtype subsample: str
+        :keyword subsample_freq: Frequency of subsample.
+        :paramtype subsample_freq: str
+        :keyword tree_method: Specify the tree method.
+        :paramtype tree_method: str
+        :keyword with_mean: If true, center before scaling the data with StandardScalar.
+        :paramtype with_mean: str
+        :keyword with_std: If true, scaling the data with Unit Variance with StandardScalar.
+        :paramtype with_std: str
+        """
+        super().__init__(**kwargs)
+        self.booster = booster
+        self.boosting_type = boosting_type
+        self.grow_policy = grow_policy
+        self.learning_rate = learning_rate
+        self.max_bin = max_bin
+        self.max_depth = max_depth
+        self.max_leaves = max_leaves
+        self.min_data_in_leaf = min_data_in_leaf
+        self.min_split_gain = min_split_gain
+        self.model_name = model_name
+        self.n_estimators = n_estimators
+        self.num_leaves = num_leaves
+        self.preprocessor_name = preprocessor_name
+        self.reg_alpha = reg_alpha
+        self.reg_lambda = reg_lambda
+        self.subsample = subsample
+        self.subsample_freq = subsample_freq
+        self.tree_method = tree_method
+        self.with_mean = with_mean
+        self.with_std = with_std
+
+
+class TableSweepSettings(_serialization.Model):
+    """TableSweepSettings.
+
+    All required parameters must be populated in order to send to Azure.
+
+    :ivar early_termination: Type of early termination policy for the sweeping job.
+    :vartype early_termination: ~azure.mgmt.machinelearningservices.models.EarlyTerminationPolicy
+    :ivar sampling_algorithm: [Required] Type of sampling algorithm. Required. Known values are:
+     "Grid", "Random", and "Bayesian".
+    :vartype sampling_algorithm: str or
+     ~azure.mgmt.machinelearningservices.models.SamplingAlgorithmType
+    """
+
+    _validation = {
+        "sampling_algorithm": {"required": True},
+    }
+
+    _attribute_map = {
+        "early_termination": {"key": "earlyTermination", "type": "EarlyTerminationPolicy"},
+        "sampling_algorithm": {"key": "samplingAlgorithm", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
-        """ """
+    def __init__(
+        self,
+        *,
+        sampling_algorithm: Union[str, "_models.SamplingAlgorithmType"],
+        early_termination: Optional["_models.EarlyTerminationPolicy"] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword early_termination: Type of early termination policy for the sweeping job.
+        :paramtype early_termination: ~azure.mgmt.machinelearningservices.models.EarlyTerminationPolicy
+        :keyword sampling_algorithm: [Required] Type of sampling algorithm. Required. Known values are:
+         "Grid", "Random", and "Bayesian".
+        :paramtype sampling_algorithm: str or
+         ~azure.mgmt.machinelearningservices.models.SamplingAlgorithmType
+        """
         super().__init__(**kwargs)
-        self.system_service_type = None
-        self.public_ip_address = None
-        self.version = None
+        self.early_termination = early_termination
+        self.sampling_algorithm = sampling_algorithm
 
 
 class TableVerticalFeaturizationSettings(FeaturizationSettings):
@@ -18682,8 +22624,8 @@ def __init__(
         enable_dnn_featurization: bool = False,
         mode: Optional[Union[str, "_models.FeaturizationMode"]] = None,
         transformer_params: Optional[Dict[str, List["_models.ColumnTransformer"]]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword dataset_language: Dataset language, useful for the text data.
         :paramtype dataset_language: str
@@ -18727,8 +22669,14 @@ class TableVerticalLimitSettings(_serialization.Model):
     :vartype max_concurrent_trials: int
     :ivar max_cores_per_trial: Max cores per iteration.
     :vartype max_cores_per_trial: int
+    :ivar max_nodes: Maximum nodes to use for the experiment.
+    :vartype max_nodes: int
     :ivar max_trials: Number of iterations.
     :vartype max_trials: int
+    :ivar sweep_concurrent_trials: Number of concurrent sweeping runs that user wants to trigger.
+    :vartype sweep_concurrent_trials: int
+    :ivar sweep_trials: Number of sweeping runs that user wants to trigger.
+    :vartype sweep_trials: int
     :ivar timeout: AutoML job timeout.
     :vartype timeout: ~datetime.timedelta
     :ivar trial_timeout: Iteration timeout.
@@ -18740,7 +22688,10 @@ class TableVerticalLimitSettings(_serialization.Model):
         "exit_score": {"key": "exitScore", "type": "float"},
         "max_concurrent_trials": {"key": "maxConcurrentTrials", "type": "int"},
         "max_cores_per_trial": {"key": "maxCoresPerTrial", "type": "int"},
+        "max_nodes": {"key": "maxNodes", "type": "int"},
         "max_trials": {"key": "maxTrials", "type": "int"},
+        "sweep_concurrent_trials": {"key": "sweepConcurrentTrials", "type": "int"},
+        "sweep_trials": {"key": "sweepTrials", "type": "int"},
         "timeout": {"key": "timeout", "type": "duration"},
         "trial_timeout": {"key": "trialTimeout", "type": "duration"},
     }
@@ -18752,11 +22703,14 @@ def __init__(
         exit_score: Optional[float] = None,
         max_concurrent_trials: int = 1,
         max_cores_per_trial: int = -1,
+        max_nodes: int = 1,
         max_trials: int = 1000,
+        sweep_concurrent_trials: int = 0,
+        sweep_trials: int = 0,
         timeout: datetime.timedelta = "PT6H",
         trial_timeout: datetime.timedelta = "PT30M",
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword enable_early_termination: Enable early termination, determines whether or not if
          AutoMLJob will terminate early if there is no score improvement in last 20 iterations.
@@ -18767,8 +22721,15 @@ def __init__(
         :paramtype max_concurrent_trials: int
         :keyword max_cores_per_trial: Max cores per iteration.
         :paramtype max_cores_per_trial: int
+        :keyword max_nodes: Maximum nodes to use for the experiment.
+        :paramtype max_nodes: int
         :keyword max_trials: Number of iterations.
         :paramtype max_trials: int
+        :keyword sweep_concurrent_trials: Number of concurrent sweeping runs that user wants to
+         trigger.
+        :paramtype sweep_concurrent_trials: int
+        :keyword sweep_trials: Number of sweeping runs that user wants to trigger.
+        :paramtype sweep_trials: int
         :keyword timeout: AutoML job timeout.
         :paramtype timeout: ~datetime.timedelta
         :keyword trial_timeout: Iteration timeout.
@@ -18779,7 +22740,10 @@ def __init__(
         self.exit_score = exit_score
         self.max_concurrent_trials = max_concurrent_trials
         self.max_cores_per_trial = max_cores_per_trial
+        self.max_nodes = max_nodes
         self.max_trials = max_trials
+        self.sweep_concurrent_trials = sweep_concurrent_trials
+        self.sweep_trials = sweep_trials
         self.timeout = timeout
         self.trial_timeout = trial_timeout
 
@@ -18823,8 +22787,8 @@ def __init__(
         min_instances: int = 1,
         polling_interval: datetime.timedelta = "PT1S",
         target_utilization_percentage: int = 70,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword max_instances: The maximum number of instances that the deployment can scale to. The
          quota will be reserved for max_instances.
@@ -18869,7 +22833,7 @@ class TensorFlow(DistributionConfiguration):
         "worker_count": {"key": "workerCount", "type": "int"},
     }
 
-    def __init__(self, *, parameter_server_count: int = 0, worker_count: Optional[int] = None, **kwargs):
+    def __init__(self, *, parameter_server_count: int = 0, worker_count: Optional[int] = None, **kwargs: Any) -> None:
         """
         :keyword parameter_server_count: Number of parameter server tasks.
         :paramtype parameter_server_count: int
@@ -18882,37 +22846,45 @@ def __init__(self, *, parameter_server_count: int = 0, worker_count: Optional[in
         self.worker_count = worker_count
 
 
-class TextClassification(NlpVertical, AutoMLVertical):
+class TextClassification(NlpVertical, AutoMLVertical):  # pylint: disable=too-many-instance-attributes
     """Text Classification task in AutoML NLP vertical.
     NLP - Natural Language Processing.
 
-        All required parameters must be populated in order to send to Azure.
+    All required parameters must be populated in order to send to Azure.
 
-        :ivar log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
-         "Warning", "Error", and "Critical".
-        :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
-        :ivar target_column_name: Target column name: This is prediction values column.
-         Also known as label column name in context of classification tasks.
-        :vartype target_column_name: str
-        :ivar task_type: [Required] Task type for AutoMLJob. Required. Known values are:
-         "Classification", "Regression", "Forecasting", "ImageClassification",
-         "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
-         "TextClassification", "TextClassificationMultilabel", and "TextNER".
-        :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
-        :ivar training_data: [Required] Training data input. Required.
-        :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
-        :ivar featurization_settings: Featurization inputs needed for AutoML job.
-        :vartype featurization_settings:
-         ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
-        :ivar limit_settings: Execution constraints for AutoMLJob.
-        :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
-        :ivar validation_data: Validation data inputs.
-        :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
-        :ivar primary_metric: Primary metric for Text-Classification task. Known values are:
-         "AUCWeighted", "Accuracy", "NormMacroRecall", "AveragePrecisionScoreWeighted", and
-         "PrecisionScoreWeighted".
-        :vartype primary_metric: str or
-         ~azure.mgmt.machinelearningservices.models.ClassificationPrimaryMetrics
+    :ivar log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
+     "Warning", "Error", and "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: [Required] Task type for AutoMLJob. Required. Known values are:
+     "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", and "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: [Required] Training data input. Required.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar featurization_settings: Featurization inputs needed for AutoML job.
+    :vartype featurization_settings:
+     ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
+    :ivar fixed_parameters: Model/training parameters that will remain constant throughout
+     training.
+    :vartype fixed_parameters: ~azure.mgmt.machinelearningservices.models.NlpFixedParameters
+    :ivar limit_settings: Execution constraints for AutoMLJob.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
+    :ivar search_space: Search space for sampling different combinations of models and their
+     hyperparameters.
+    :vartype search_space: list[~azure.mgmt.machinelearningservices.models.NlpParameterSubspace]
+    :ivar sweep_settings: Settings for model sweeping and hyperparameter tuning.
+    :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.NlpSweepSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar primary_metric: Primary metric for Text-Classification task. Known values are:
+     "AUCWeighted", "Accuracy", "NormMacroRecall", "AveragePrecisionScoreWeighted", and
+     "PrecisionScoreWeighted".
+    :vartype primary_metric: str or
+     ~azure.mgmt.machinelearningservices.models.ClassificationPrimaryMetrics
     """
 
     _validation = {
@@ -18926,7 +22898,10 @@ class TextClassification(NlpVertical, AutoMLVertical):
         "task_type": {"key": "taskType", "type": "str"},
         "training_data": {"key": "trainingData", "type": "MLTableJobInput"},
         "featurization_settings": {"key": "featurizationSettings", "type": "NlpVerticalFeaturizationSettings"},
+        "fixed_parameters": {"key": "fixedParameters", "type": "NlpFixedParameters"},
         "limit_settings": {"key": "limitSettings", "type": "NlpVerticalLimitSettings"},
+        "search_space": {"key": "searchSpace", "type": "[NlpParameterSubspace]"},
+        "sweep_settings": {"key": "sweepSettings", "type": "NlpSweepSettings"},
         "validation_data": {"key": "validationData", "type": "MLTableJobInput"},
         "primary_metric": {"key": "primaryMetric", "type": "str"},
     }
@@ -18938,11 +22913,14 @@ def __init__(
         log_verbosity: Optional[Union[str, "_models.LogVerbosity"]] = None,
         target_column_name: Optional[str] = None,
         featurization_settings: Optional["_models.NlpVerticalFeaturizationSettings"] = None,
+        fixed_parameters: Optional["_models.NlpFixedParameters"] = None,
         limit_settings: Optional["_models.NlpVerticalLimitSettings"] = None,
+        search_space: Optional[List["_models.NlpParameterSubspace"]] = None,
+        sweep_settings: Optional["_models.NlpSweepSettings"] = None,
         validation_data: Optional["_models.MLTableJobInput"] = None,
         primary_metric: Optional[Union[str, "_models.ClassificationPrimaryMetrics"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
          "Warning", "Error", and "Critical".
@@ -18955,8 +22933,16 @@ def __init__(
         :keyword featurization_settings: Featurization inputs needed for AutoML job.
         :paramtype featurization_settings:
          ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
+        :keyword fixed_parameters: Model/training parameters that will remain constant throughout
+         training.
+        :paramtype fixed_parameters: ~azure.mgmt.machinelearningservices.models.NlpFixedParameters
         :keyword limit_settings: Execution constraints for AutoMLJob.
         :paramtype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
+        :keyword search_space: Search space for sampling different combinations of models and their
+         hyperparameters.
+        :paramtype search_space: list[~azure.mgmt.machinelearningservices.models.NlpParameterSubspace]
+        :keyword sweep_settings: Settings for model sweeping and hyperparameter tuning.
+        :paramtype sweep_settings: ~azure.mgmt.machinelearningservices.models.NlpSweepSettings
         :keyword validation_data: Validation data inputs.
         :paramtype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
         :keyword primary_metric: Primary metric for Text-Classification task. Known values are:
@@ -18967,7 +22953,10 @@ def __init__(
         """
         super().__init__(
             featurization_settings=featurization_settings,
+            fixed_parameters=fixed_parameters,
             limit_settings=limit_settings,
+            search_space=search_space,
+            sweep_settings=sweep_settings,
             validation_data=validation_data,
             log_verbosity=log_verbosity,
             target_column_name=target_column_name,
@@ -18980,44 +22969,55 @@ def __init__(
         self.training_data = training_data
         self.primary_metric = primary_metric
         self.featurization_settings = featurization_settings
+        self.fixed_parameters = fixed_parameters
         self.limit_settings = limit_settings
+        self.search_space = search_space
+        self.sweep_settings = sweep_settings
         self.validation_data = validation_data
 
 
-class TextClassificationMultilabel(NlpVertical, AutoMLVertical):
+class TextClassificationMultilabel(NlpVertical, AutoMLVertical):  # pylint: disable=too-many-instance-attributes
     """Text Classification Multilabel task in AutoML NLP vertical.
     NLP - Natural Language Processing.
 
-        Variables are only populated by the server, and will be ignored when sending a request.
+    Variables are only populated by the server, and will be ignored when sending a request.
 
-        All required parameters must be populated in order to send to Azure.
+    All required parameters must be populated in order to send to Azure.
 
-        :ivar log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
-         "Warning", "Error", and "Critical".
-        :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
-        :ivar target_column_name: Target column name: This is prediction values column.
-         Also known as label column name in context of classification tasks.
-        :vartype target_column_name: str
-        :ivar task_type: [Required] Task type for AutoMLJob. Required. Known values are:
-         "Classification", "Regression", "Forecasting", "ImageClassification",
-         "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
-         "TextClassification", "TextClassificationMultilabel", and "TextNER".
-        :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
-        :ivar training_data: [Required] Training data input. Required.
-        :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
-        :ivar featurization_settings: Featurization inputs needed for AutoML job.
-        :vartype featurization_settings:
-         ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
-        :ivar limit_settings: Execution constraints for AutoMLJob.
-        :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
-        :ivar validation_data: Validation data inputs.
-        :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
-        :ivar primary_metric: Primary metric for Text-Classification-Multilabel task.
-         Currently only Accuracy is supported as primary metric, hence user need not set it explicitly.
-         Known values are: "AUCWeighted", "Accuracy", "NormMacroRecall",
-         "AveragePrecisionScoreWeighted", "PrecisionScoreWeighted", and "IOU".
-        :vartype primary_metric: str or
-         ~azure.mgmt.machinelearningservices.models.ClassificationMultilabelPrimaryMetrics
+    :ivar log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
+     "Warning", "Error", and "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: [Required] Task type for AutoMLJob. Required. Known values are:
+     "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", and "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: [Required] Training data input. Required.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar featurization_settings: Featurization inputs needed for AutoML job.
+    :vartype featurization_settings:
+     ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
+    :ivar fixed_parameters: Model/training parameters that will remain constant throughout
+     training.
+    :vartype fixed_parameters: ~azure.mgmt.machinelearningservices.models.NlpFixedParameters
+    :ivar limit_settings: Execution constraints for AutoMLJob.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
+    :ivar search_space: Search space for sampling different combinations of models and their
+     hyperparameters.
+    :vartype search_space: list[~azure.mgmt.machinelearningservices.models.NlpParameterSubspace]
+    :ivar sweep_settings: Settings for model sweeping and hyperparameter tuning.
+    :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.NlpSweepSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar primary_metric: Primary metric for Text-Classification-Multilabel task.
+     Currently only Accuracy is supported as primary metric, hence user need not set it explicitly.
+     Known values are: "AUCWeighted", "Accuracy", "NormMacroRecall",
+     "AveragePrecisionScoreWeighted", "PrecisionScoreWeighted", and "IOU".
+    :vartype primary_metric: str or
+     ~azure.mgmt.machinelearningservices.models.ClassificationMultilabelPrimaryMetrics
     """
 
     _validation = {
@@ -19032,7 +23032,10 @@ class TextClassificationMultilabel(NlpVertical, AutoMLVertical):
         "task_type": {"key": "taskType", "type": "str"},
         "training_data": {"key": "trainingData", "type": "MLTableJobInput"},
         "featurization_settings": {"key": "featurizationSettings", "type": "NlpVerticalFeaturizationSettings"},
+        "fixed_parameters": {"key": "fixedParameters", "type": "NlpFixedParameters"},
         "limit_settings": {"key": "limitSettings", "type": "NlpVerticalLimitSettings"},
+        "search_space": {"key": "searchSpace", "type": "[NlpParameterSubspace]"},
+        "sweep_settings": {"key": "sweepSettings", "type": "NlpSweepSettings"},
         "validation_data": {"key": "validationData", "type": "MLTableJobInput"},
         "primary_metric": {"key": "primaryMetric", "type": "str"},
     }
@@ -19044,10 +23047,13 @@ def __init__(
         log_verbosity: Optional[Union[str, "_models.LogVerbosity"]] = None,
         target_column_name: Optional[str] = None,
         featurization_settings: Optional["_models.NlpVerticalFeaturizationSettings"] = None,
+        fixed_parameters: Optional["_models.NlpFixedParameters"] = None,
         limit_settings: Optional["_models.NlpVerticalLimitSettings"] = None,
+        search_space: Optional[List["_models.NlpParameterSubspace"]] = None,
+        sweep_settings: Optional["_models.NlpSweepSettings"] = None,
         validation_data: Optional["_models.MLTableJobInput"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
          "Warning", "Error", and "Critical".
@@ -19060,14 +23066,25 @@ def __init__(
         :keyword featurization_settings: Featurization inputs needed for AutoML job.
         :paramtype featurization_settings:
          ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
+        :keyword fixed_parameters: Model/training parameters that will remain constant throughout
+         training.
+        :paramtype fixed_parameters: ~azure.mgmt.machinelearningservices.models.NlpFixedParameters
         :keyword limit_settings: Execution constraints for AutoMLJob.
         :paramtype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
+        :keyword search_space: Search space for sampling different combinations of models and their
+         hyperparameters.
+        :paramtype search_space: list[~azure.mgmt.machinelearningservices.models.NlpParameterSubspace]
+        :keyword sweep_settings: Settings for model sweeping and hyperparameter tuning.
+        :paramtype sweep_settings: ~azure.mgmt.machinelearningservices.models.NlpSweepSettings
         :keyword validation_data: Validation data inputs.
         :paramtype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
         """
         super().__init__(
             featurization_settings=featurization_settings,
+            fixed_parameters=fixed_parameters,
             limit_settings=limit_settings,
+            search_space=search_space,
+            sweep_settings=sweep_settings,
             validation_data=validation_data,
             log_verbosity=log_verbosity,
             target_column_name=target_column_name,
@@ -19080,45 +23097,56 @@ def __init__(
         self.training_data = training_data
         self.primary_metric = None
         self.featurization_settings = featurization_settings
+        self.fixed_parameters = fixed_parameters
         self.limit_settings = limit_settings
+        self.search_space = search_space
+        self.sweep_settings = sweep_settings
         self.validation_data = validation_data
 
 
-class TextNer(NlpVertical, AutoMLVertical):
+class TextNer(NlpVertical, AutoMLVertical):  # pylint: disable=too-many-instance-attributes
     """Text-NER task in AutoML NLP vertical.
     NER - Named Entity Recognition.
     NLP - Natural Language Processing.
 
-        Variables are only populated by the server, and will be ignored when sending a request.
+    Variables are only populated by the server, and will be ignored when sending a request.
 
-        All required parameters must be populated in order to send to Azure.
+    All required parameters must be populated in order to send to Azure.
 
-        :ivar log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
-         "Warning", "Error", and "Critical".
-        :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
-        :ivar target_column_name: Target column name: This is prediction values column.
-         Also known as label column name in context of classification tasks.
-        :vartype target_column_name: str
-        :ivar task_type: [Required] Task type for AutoMLJob. Required. Known values are:
-         "Classification", "Regression", "Forecasting", "ImageClassification",
-         "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
-         "TextClassification", "TextClassificationMultilabel", and "TextNER".
-        :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
-        :ivar training_data: [Required] Training data input. Required.
-        :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
-        :ivar featurization_settings: Featurization inputs needed for AutoML job.
-        :vartype featurization_settings:
-         ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
-        :ivar limit_settings: Execution constraints for AutoMLJob.
-        :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
-        :ivar validation_data: Validation data inputs.
-        :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
-        :ivar primary_metric: Primary metric for Text-NER task.
-         Only 'Accuracy' is supported for Text-NER, so user need not set this explicitly. Known values
-         are: "AUCWeighted", "Accuracy", "NormMacroRecall", "AveragePrecisionScoreWeighted", and
-         "PrecisionScoreWeighted".
-        :vartype primary_metric: str or
-         ~azure.mgmt.machinelearningservices.models.ClassificationPrimaryMetrics
+    :ivar log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
+     "Warning", "Error", and "Critical".
+    :vartype log_verbosity: str or ~azure.mgmt.machinelearningservices.models.LogVerbosity
+    :ivar target_column_name: Target column name: This is prediction values column.
+     Also known as label column name in context of classification tasks.
+    :vartype target_column_name: str
+    :ivar task_type: [Required] Task type for AutoMLJob. Required. Known values are:
+     "Classification", "Regression", "Forecasting", "ImageClassification",
+     "ImageClassificationMultilabel", "ImageObjectDetection", "ImageInstanceSegmentation",
+     "TextClassification", "TextClassificationMultilabel", and "TextNER".
+    :vartype task_type: str or ~azure.mgmt.machinelearningservices.models.TaskType
+    :ivar training_data: [Required] Training data input. Required.
+    :vartype training_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar featurization_settings: Featurization inputs needed for AutoML job.
+    :vartype featurization_settings:
+     ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
+    :ivar fixed_parameters: Model/training parameters that will remain constant throughout
+     training.
+    :vartype fixed_parameters: ~azure.mgmt.machinelearningservices.models.NlpFixedParameters
+    :ivar limit_settings: Execution constraints for AutoMLJob.
+    :vartype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
+    :ivar search_space: Search space for sampling different combinations of models and their
+     hyperparameters.
+    :vartype search_space: list[~azure.mgmt.machinelearningservices.models.NlpParameterSubspace]
+    :ivar sweep_settings: Settings for model sweeping and hyperparameter tuning.
+    :vartype sweep_settings: ~azure.mgmt.machinelearningservices.models.NlpSweepSettings
+    :ivar validation_data: Validation data inputs.
+    :vartype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
+    :ivar primary_metric: Primary metric for Text-NER task.
+     Only 'Accuracy' is supported for Text-NER, so user need not set this explicitly. Known values
+     are: "AUCWeighted", "Accuracy", "NormMacroRecall", "AveragePrecisionScoreWeighted", and
+     "PrecisionScoreWeighted".
+    :vartype primary_metric: str or
+     ~azure.mgmt.machinelearningservices.models.ClassificationPrimaryMetrics
     """
 
     _validation = {
@@ -19133,7 +23161,10 @@ class TextNer(NlpVertical, AutoMLVertical):
         "task_type": {"key": "taskType", "type": "str"},
         "training_data": {"key": "trainingData", "type": "MLTableJobInput"},
         "featurization_settings": {"key": "featurizationSettings", "type": "NlpVerticalFeaturizationSettings"},
+        "fixed_parameters": {"key": "fixedParameters", "type": "NlpFixedParameters"},
         "limit_settings": {"key": "limitSettings", "type": "NlpVerticalLimitSettings"},
+        "search_space": {"key": "searchSpace", "type": "[NlpParameterSubspace]"},
+        "sweep_settings": {"key": "sweepSettings", "type": "NlpSweepSettings"},
         "validation_data": {"key": "validationData", "type": "MLTableJobInput"},
         "primary_metric": {"key": "primaryMetric", "type": "str"},
     }
@@ -19145,10 +23176,13 @@ def __init__(
         log_verbosity: Optional[Union[str, "_models.LogVerbosity"]] = None,
         target_column_name: Optional[str] = None,
         featurization_settings: Optional["_models.NlpVerticalFeaturizationSettings"] = None,
+        fixed_parameters: Optional["_models.NlpFixedParameters"] = None,
         limit_settings: Optional["_models.NlpVerticalLimitSettings"] = None,
+        search_space: Optional[List["_models.NlpParameterSubspace"]] = None,
+        sweep_settings: Optional["_models.NlpSweepSettings"] = None,
         validation_data: Optional["_models.MLTableJobInput"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword log_verbosity: Log verbosity for the job. Known values are: "NotSet", "Debug", "Info",
          "Warning", "Error", and "Critical".
@@ -19161,14 +23195,25 @@ def __init__(
         :keyword featurization_settings: Featurization inputs needed for AutoML job.
         :paramtype featurization_settings:
          ~azure.mgmt.machinelearningservices.models.NlpVerticalFeaturizationSettings
+        :keyword fixed_parameters: Model/training parameters that will remain constant throughout
+         training.
+        :paramtype fixed_parameters: ~azure.mgmt.machinelearningservices.models.NlpFixedParameters
         :keyword limit_settings: Execution constraints for AutoMLJob.
         :paramtype limit_settings: ~azure.mgmt.machinelearningservices.models.NlpVerticalLimitSettings
+        :keyword search_space: Search space for sampling different combinations of models and their
+         hyperparameters.
+        :paramtype search_space: list[~azure.mgmt.machinelearningservices.models.NlpParameterSubspace]
+        :keyword sweep_settings: Settings for model sweeping and hyperparameter tuning.
+        :paramtype sweep_settings: ~azure.mgmt.machinelearningservices.models.NlpSweepSettings
         :keyword validation_data: Validation data inputs.
         :paramtype validation_data: ~azure.mgmt.machinelearningservices.models.MLTableJobInput
         """
         super().__init__(
             featurization_settings=featurization_settings,
+            fixed_parameters=fixed_parameters,
             limit_settings=limit_settings,
+            search_space=search_space,
+            sweep_settings=sweep_settings,
             validation_data=validation_data,
             log_verbosity=log_verbosity,
             target_column_name=target_column_name,
@@ -19181,10 +23226,33 @@ def __init__(
         self.training_data = training_data
         self.primary_metric = None
         self.featurization_settings = featurization_settings
+        self.fixed_parameters = fixed_parameters
         self.limit_settings = limit_settings
+        self.search_space = search_space
+        self.sweep_settings = sweep_settings
         self.validation_data = validation_data
 
 
+class TmpfsOptions(_serialization.Model):
+    """TmpfsOptions.
+
+    :ivar size: Mention the Tmpfs size.
+    :vartype size: int
+    """
+
+    _attribute_map = {
+        "size": {"key": "size", "type": "int"},
+    }
+
+    def __init__(self, *, size: Optional[int] = None, **kwargs: Any) -> None:
+        """
+        :keyword size: Mention the Tmpfs size.
+        :paramtype size: int
+        """
+        super().__init__(**kwargs)
+        self.size = size
+
+
 class TrialComponent(_serialization.Model):
     """Trial component definition.
 
@@ -19230,8 +23298,8 @@ def __init__(
         distribution: Optional["_models.DistributionConfiguration"] = None,
         environment_variables: Optional[Dict[str, str]] = None,
         resources: Optional["_models.JobResourceConfiguration"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword code_id: ARM resource ID of the code asset.
         :paramtype code_id: str
@@ -19294,8 +23362,8 @@ def __init__(
         uri: str,
         description: Optional[str] = None,
         mode: Optional[Union[str, "_models.InputDeliveryMode"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: Description for the input.
         :paramtype description: str
@@ -19322,7 +23390,12 @@ class TritonModelJobOutput(AssetJobOutput, JobOutput):
     :ivar job_output_type: [Required] Specifies the type of job. Required. Known values are:
      "uri_file", "uri_folder", "mltable", "custom_model", "mlflow_model", and "triton_model".
     :vartype job_output_type: str or ~azure.mgmt.machinelearningservices.models.JobOutputType
-    :ivar mode: Output Asset Delivery Mode. Known values are: "ReadWriteMount" and "Upload".
+    :ivar asset_name: Output Asset Name.
+    :vartype asset_name: str
+    :ivar asset_version: Output Asset Version.
+    :vartype asset_version: str
+    :ivar mode: Output Asset Delivery Mode. Known values are: "ReadWriteMount", "Upload", and
+     "Direct".
     :vartype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
     :ivar uri: Output Asset URI.
     :vartype uri: str
@@ -19335,6 +23408,8 @@ class TritonModelJobOutput(AssetJobOutput, JobOutput):
     _attribute_map = {
         "description": {"key": "description", "type": "str"},
         "job_output_type": {"key": "jobOutputType", "type": "str"},
+        "asset_name": {"key": "assetName", "type": "str"},
+        "asset_version": {"key": "assetVersion", "type": "str"},
         "mode": {"key": "mode", "type": "str"},
         "uri": {"key": "uri", "type": "str"},
     }
@@ -19343,27 +23418,39 @@ def __init__(
         self,
         *,
         description: Optional[str] = None,
+        asset_name: Optional[str] = None,
+        asset_version: Optional[str] = None,
         mode: Optional[Union[str, "_models.OutputDeliveryMode"]] = None,
         uri: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: Description for the output.
         :paramtype description: str
-        :keyword mode: Output Asset Delivery Mode. Known values are: "ReadWriteMount" and "Upload".
+        :keyword asset_name: Output Asset Name.
+        :paramtype asset_name: str
+        :keyword asset_version: Output Asset Version.
+        :paramtype asset_version: str
+        :keyword mode: Output Asset Delivery Mode. Known values are: "ReadWriteMount", "Upload", and
+         "Direct".
         :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
         :keyword uri: Output Asset URI.
         :paramtype uri: str
         """
-        super().__init__(mode=mode, uri=uri, description=description, **kwargs)
+        super().__init__(
+            asset_name=asset_name, asset_version=asset_version, mode=mode, uri=uri, description=description, **kwargs
+        )
         self.description = description
         self.job_output_type: str = "triton_model"
+        self.asset_name = asset_name
+        self.asset_version = asset_version
         self.mode = mode
         self.uri = uri
 
 
 class TruncationSelectionPolicy(EarlyTerminationPolicy):
-    """Defines an early termination policy that cancels a given percentage of runs at each evaluation interval.
+    """Defines an early termination policy that cancels a given percentage of runs at each evaluation
+    interval.
 
     All required parameters must be populated in order to send to Azure.
 
@@ -19391,8 +23478,8 @@ class TruncationSelectionPolicy(EarlyTerminationPolicy):
     }
 
     def __init__(
-        self, *, delay_evaluation: int = 0, evaluation_interval: int = 0, truncation_percentage: int = 0, **kwargs
-    ):
+        self, *, delay_evaluation: int = 0, evaluation_interval: int = 0, truncation_percentage: int = 0, **kwargs: Any
+    ) -> None:
         """
         :keyword delay_evaluation: Number of intervals by which to delay the first evaluation.
         :paramtype delay_evaluation: int
@@ -19439,7 +23526,9 @@ class UpdateWorkspaceQuotas(_serialization.Model):
         "status": {"key": "status", "type": "str"},
     }
 
-    def __init__(self, *, limit: Optional[int] = None, status: Optional[Union[str, "_models.Status"]] = None, **kwargs):
+    def __init__(
+        self, *, limit: Optional[int] = None, status: Optional[Union[str, "_models.Status"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword limit: The maximum permitted quota of the resource.
         :paramtype limit: int
@@ -19478,7 +23567,7 @@ class UpdateWorkspaceQuotasResult(_serialization.Model):
         "next_link": {"key": "nextLink", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.value = None
@@ -19503,9 +23592,8 @@ class UriFileDataVersion(DataVersionBaseProperties):
     :ivar data_type: [Required] Specifies the type of data. Required. Known values are: "uri_file",
      "uri_folder", and "mltable".
     :vartype data_type: str or ~azure.mgmt.machinelearningservices.models.DataType
-    :ivar data_uri: [Required] Uri of the data. Usage/meaning depends on
-     Microsoft.MachineLearning.ManagementFrontEnd.Contracts.V20221001.Assets.DataVersionBase.DataType.
-     Required.
+    :ivar data_uri: [Required] Uri of the data. Example:
+     https://go.microsoft.com/fwlink/?linkid=2202330. Required.
     :vartype data_uri: str
     """
 
@@ -19533,8 +23621,8 @@ def __init__(
         tags: Optional[Dict[str, str]] = None,
         is_anonymous: bool = False,
         is_archived: bool = False,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -19546,9 +23634,8 @@ def __init__(
         :paramtype is_anonymous: bool
         :keyword is_archived: Is the asset archived?.
         :paramtype is_archived: bool
-        :keyword data_uri: [Required] Uri of the data. Usage/meaning depends on
-         Microsoft.MachineLearning.ManagementFrontEnd.Contracts.V20221001.Assets.DataVersionBase.DataType.
-         Required.
+        :keyword data_uri: [Required] Uri of the data. Example:
+         https://go.microsoft.com/fwlink/?linkid=2202330. Required.
         :paramtype data_uri: str
         """
         super().__init__(
@@ -19599,8 +23686,8 @@ def __init__(
         uri: str,
         description: Optional[str] = None,
         mode: Optional[Union[str, "_models.InputDeliveryMode"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: Description for the input.
         :paramtype description: str
@@ -19627,7 +23714,12 @@ class UriFileJobOutput(AssetJobOutput, JobOutput):
     :ivar job_output_type: [Required] Specifies the type of job. Required. Known values are:
      "uri_file", "uri_folder", "mltable", "custom_model", "mlflow_model", and "triton_model".
     :vartype job_output_type: str or ~azure.mgmt.machinelearningservices.models.JobOutputType
-    :ivar mode: Output Asset Delivery Mode. Known values are: "ReadWriteMount" and "Upload".
+    :ivar asset_name: Output Asset Name.
+    :vartype asset_name: str
+    :ivar asset_version: Output Asset Version.
+    :vartype asset_version: str
+    :ivar mode: Output Asset Delivery Mode. Known values are: "ReadWriteMount", "Upload", and
+     "Direct".
     :vartype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
     :ivar uri: Output Asset URI.
     :vartype uri: str
@@ -19640,6 +23732,8 @@ class UriFileJobOutput(AssetJobOutput, JobOutput):
     _attribute_map = {
         "description": {"key": "description", "type": "str"},
         "job_output_type": {"key": "jobOutputType", "type": "str"},
+        "asset_name": {"key": "assetName", "type": "str"},
+        "asset_version": {"key": "assetVersion", "type": "str"},
         "mode": {"key": "mode", "type": "str"},
         "uri": {"key": "uri", "type": "str"},
     }
@@ -19648,21 +23742,32 @@ def __init__(
         self,
         *,
         description: Optional[str] = None,
+        asset_name: Optional[str] = None,
+        asset_version: Optional[str] = None,
         mode: Optional[Union[str, "_models.OutputDeliveryMode"]] = None,
         uri: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: Description for the output.
         :paramtype description: str
-        :keyword mode: Output Asset Delivery Mode. Known values are: "ReadWriteMount" and "Upload".
+        :keyword asset_name: Output Asset Name.
+        :paramtype asset_name: str
+        :keyword asset_version: Output Asset Version.
+        :paramtype asset_version: str
+        :keyword mode: Output Asset Delivery Mode. Known values are: "ReadWriteMount", "Upload", and
+         "Direct".
         :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
         :keyword uri: Output Asset URI.
         :paramtype uri: str
         """
-        super().__init__(mode=mode, uri=uri, description=description, **kwargs)
+        super().__init__(
+            asset_name=asset_name, asset_version=asset_version, mode=mode, uri=uri, description=description, **kwargs
+        )
         self.description = description
         self.job_output_type: str = "uri_file"
+        self.asset_name = asset_name
+        self.asset_version = asset_version
         self.mode = mode
         self.uri = uri
 
@@ -19685,9 +23790,8 @@ class UriFolderDataVersion(DataVersionBaseProperties):
     :ivar data_type: [Required] Specifies the type of data. Required. Known values are: "uri_file",
      "uri_folder", and "mltable".
     :vartype data_type: str or ~azure.mgmt.machinelearningservices.models.DataType
-    :ivar data_uri: [Required] Uri of the data. Usage/meaning depends on
-     Microsoft.MachineLearning.ManagementFrontEnd.Contracts.V20221001.Assets.DataVersionBase.DataType.
-     Required.
+    :ivar data_uri: [Required] Uri of the data. Example:
+     https://go.microsoft.com/fwlink/?linkid=2202330. Required.
     :vartype data_uri: str
     """
 
@@ -19715,8 +23819,8 @@ def __init__(
         tags: Optional[Dict[str, str]] = None,
         is_anonymous: bool = False,
         is_archived: bool = False,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: The asset description text.
         :paramtype description: str
@@ -19728,9 +23832,8 @@ def __init__(
         :paramtype is_anonymous: bool
         :keyword is_archived: Is the asset archived?.
         :paramtype is_archived: bool
-        :keyword data_uri: [Required] Uri of the data. Usage/meaning depends on
-         Microsoft.MachineLearning.ManagementFrontEnd.Contracts.V20221001.Assets.DataVersionBase.DataType.
-         Required.
+        :keyword data_uri: [Required] Uri of the data. Example:
+         https://go.microsoft.com/fwlink/?linkid=2202330. Required.
         :paramtype data_uri: str
         """
         super().__init__(
@@ -19781,8 +23884,8 @@ def __init__(
         uri: str,
         description: Optional[str] = None,
         mode: Optional[Union[str, "_models.InputDeliveryMode"]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: Description for the input.
         :paramtype description: str
@@ -19809,7 +23912,12 @@ class UriFolderJobOutput(AssetJobOutput, JobOutput):
     :ivar job_output_type: [Required] Specifies the type of job. Required. Known values are:
      "uri_file", "uri_folder", "mltable", "custom_model", "mlflow_model", and "triton_model".
     :vartype job_output_type: str or ~azure.mgmt.machinelearningservices.models.JobOutputType
-    :ivar mode: Output Asset Delivery Mode. Known values are: "ReadWriteMount" and "Upload".
+    :ivar asset_name: Output Asset Name.
+    :vartype asset_name: str
+    :ivar asset_version: Output Asset Version.
+    :vartype asset_version: str
+    :ivar mode: Output Asset Delivery Mode. Known values are: "ReadWriteMount", "Upload", and
+     "Direct".
     :vartype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
     :ivar uri: Output Asset URI.
     :vartype uri: str
@@ -19822,6 +23930,8 @@ class UriFolderJobOutput(AssetJobOutput, JobOutput):
     _attribute_map = {
         "description": {"key": "description", "type": "str"},
         "job_output_type": {"key": "jobOutputType", "type": "str"},
+        "asset_name": {"key": "assetName", "type": "str"},
+        "asset_version": {"key": "assetVersion", "type": "str"},
         "mode": {"key": "mode", "type": "str"},
         "uri": {"key": "uri", "type": "str"},
     }
@@ -19830,21 +23940,32 @@ def __init__(
         self,
         *,
         description: Optional[str] = None,
+        asset_name: Optional[str] = None,
+        asset_version: Optional[str] = None,
         mode: Optional[Union[str, "_models.OutputDeliveryMode"]] = None,
         uri: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword description: Description for the output.
         :paramtype description: str
-        :keyword mode: Output Asset Delivery Mode. Known values are: "ReadWriteMount" and "Upload".
+        :keyword asset_name: Output Asset Name.
+        :paramtype asset_name: str
+        :keyword asset_version: Output Asset Version.
+        :paramtype asset_version: str
+        :keyword mode: Output Asset Delivery Mode. Known values are: "ReadWriteMount", "Upload", and
+         "Direct".
         :paramtype mode: str or ~azure.mgmt.machinelearningservices.models.OutputDeliveryMode
         :keyword uri: Output Asset URI.
         :paramtype uri: str
         """
-        super().__init__(mode=mode, uri=uri, description=description, **kwargs)
+        super().__init__(
+            asset_name=asset_name, asset_version=asset_version, mode=mode, uri=uri, description=description, **kwargs
+        )
         self.description = description
         self.job_output_type: str = "uri_folder"
+        self.asset_name = asset_name
+        self.asset_version = asset_version
         self.mode = mode
         self.uri = uri
 
@@ -19890,7 +24011,7 @@ class Usage(_serialization.Model):
         "name": {"key": "name", "type": "UsageName"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.id = None
@@ -19923,7 +24044,7 @@ class UsageName(_serialization.Model):
         "localized_value": {"key": "localizedValue", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.value = None
@@ -19960,8 +24081,8 @@ def __init__(
         admin_user_name: str,
         admin_user_ssh_public_key: Optional[str] = None,
         admin_user_password: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword admin_user_name: Name of the administrator user account which can be used to SSH to
          nodes. Required.
@@ -19998,13 +24119,53 @@ class UserAssignedIdentity(_serialization.Model):
         "client_id": {"key": "clientId", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.principal_id = None
         self.client_id = None
 
 
+class UserCreatedAcrAccount(_serialization.Model):
+    """UserCreatedAcrAccount.
+
+    :ivar arm_resource_id: ARM ResourceId of a resource.
+    :vartype arm_resource_id: ~azure.mgmt.machinelearningservices.models.ArmResourceId
+    """
+
+    _attribute_map = {
+        "arm_resource_id": {"key": "armResourceId", "type": "ArmResourceId"},
+    }
+
+    def __init__(self, *, arm_resource_id: Optional["_models.ArmResourceId"] = None, **kwargs: Any) -> None:
+        """
+        :keyword arm_resource_id: ARM ResourceId of a resource.
+        :paramtype arm_resource_id: ~azure.mgmt.machinelearningservices.models.ArmResourceId
+        """
+        super().__init__(**kwargs)
+        self.arm_resource_id = arm_resource_id
+
+
+class UserCreatedStorageAccount(_serialization.Model):
+    """UserCreatedStorageAccount.
+
+    :ivar arm_resource_id: ARM ResourceId of a resource.
+    :vartype arm_resource_id: ~azure.mgmt.machinelearningservices.models.ArmResourceId
+    """
+
+    _attribute_map = {
+        "arm_resource_id": {"key": "armResourceId", "type": "ArmResourceId"},
+    }
+
+    def __init__(self, *, arm_resource_id: Optional["_models.ArmResourceId"] = None, **kwargs: Any) -> None:
+        """
+        :keyword arm_resource_id: ARM ResourceId of a resource.
+        :paramtype arm_resource_id: ~azure.mgmt.machinelearningservices.models.ArmResourceId
+        """
+        super().__init__(**kwargs)
+        self.arm_resource_id = arm_resource_id
+
+
 class UserIdentity(IdentityConfiguration):
     """User identity configuration.
 
@@ -20024,7 +24185,7 @@ class UserIdentity(IdentityConfiguration):
         "identity_type": {"key": "identityType", "type": "str"},
     }
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any) -> None:
         """ """
         super().__init__(**kwargs)
         self.identity_type: str = "UserIdentity"
@@ -20036,10 +24197,12 @@ class UsernamePasswordAuthTypeWorkspaceConnectionProperties(WorkspaceConnectionP
     All required parameters must be populated in order to send to Azure.
 
     :ivar auth_type: Authentication type of the connection target. Required. Known values are:
-     "PAT", "ManagedIdentity", "UsernamePassword", "None", and "SAS".
+     "PAT", "ManagedIdentity", "UsernamePassword", "None", "SAS", "ServicePrincipal", and
+     "AccessKey".
     :vartype auth_type: str or ~azure.mgmt.machinelearningservices.models.ConnectionAuthType
     :ivar category: Category of the connection. Known values are: "PythonFeed",
-     "ContainerRegistry", and "Git".
+     "ContainerRegistry", "Git", "FeatureStore", "S3", "Snowflake", "AzureSqlDb", and
+     "AzureSynapseAnalytics".
     :vartype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
     :ivar target:
     :vartype target: str
@@ -20073,11 +24236,12 @@ def __init__(
         value: Optional[str] = None,
         value_format: Optional[Union[str, "_models.ValueFormat"]] = None,
         credentials: Optional["_models.WorkspaceConnectionUsernamePassword"] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword category: Category of the connection. Known values are: "PythonFeed",
-         "ContainerRegistry", and "Git".
+         "ContainerRegistry", "Git", "FeatureStore", "S3", "Snowflake", "AzureSqlDb", and
+         "AzureSynapseAnalytics".
         :paramtype category: str or ~azure.mgmt.machinelearningservices.models.ConnectionCategory
         :keyword target:
         :paramtype target: str
@@ -20105,7 +24269,7 @@ class VirtualMachineSchema(_serialization.Model):
         "properties": {"key": "properties", "type": "VirtualMachineSchemaProperties"},
     }
 
-    def __init__(self, *, properties: Optional["_models.VirtualMachineSchemaProperties"] = None, **kwargs):
+    def __init__(self, *, properties: Optional["_models.VirtualMachineSchemaProperties"] = None, **kwargs: Any) -> None:
         """
         :keyword properties:
         :paramtype properties:
@@ -20132,7 +24296,7 @@ class VirtualMachine(Compute, VirtualMachineSchema):  # pylint: disable=too-many
     :vartype compute_location: str
     :ivar provisioning_state: The provision state of the cluster. Valid values are Unknown,
      Updating, Provisioning, Succeeded, and Failed. Known values are: "Unknown", "Updating",
-     "Creating", "Deleting", "Succeeded", "Failed", and "Canceled".
+     "Creating", "Deleting", "Succeeded", "Failed", "Canceled", and "SoftDeleted".
     :vartype provisioning_state: str or
      ~azure.mgmt.machinelearningservices.models.ProvisioningState
     :ivar description: The description of the Machine Learning compute.
@@ -20184,8 +24348,8 @@ def __init__(
         description: Optional[str] = None,
         resource_id: Optional[str] = None,
         disable_local_auth: Optional[bool] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword properties:
         :paramtype properties:
@@ -20238,7 +24402,7 @@ class VirtualMachineImage(_serialization.Model):
         "id": {"key": "id", "type": "str"},
     }
 
-    def __init__(self, *, id: str, **kwargs):  # pylint: disable=redefined-builtin
+    def __init__(self, *, id: str, **kwargs: Any) -> None:  # pylint: disable=redefined-builtin
         """
         :keyword id: Virtual Machine image path. Required.
         :paramtype id: str
@@ -20284,8 +24448,8 @@ def __init__(
         address: Optional[str] = None,
         administrator_account: Optional["_models.VirtualMachineSshCredentials"] = None,
         is_notebook_instance_compute: Optional[bool] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword virtual_machine_size: Virtual Machine size.
         :paramtype virtual_machine_size: str
@@ -20323,7 +24487,9 @@ class VirtualMachineSecretsSchema(_serialization.Model):
         "administrator_account": {"key": "administratorAccount", "type": "VirtualMachineSshCredentials"},
     }
 
-    def __init__(self, *, administrator_account: Optional["_models.VirtualMachineSshCredentials"] = None, **kwargs):
+    def __init__(
+        self, *, administrator_account: Optional["_models.VirtualMachineSshCredentials"] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword administrator_account: Admin credentials for virtual machine.
         :paramtype administrator_account:
@@ -20356,7 +24522,9 @@ class VirtualMachineSecrets(ComputeSecrets, VirtualMachineSecretsSchema):
         "compute_type": {"key": "computeType", "type": "str"},
     }
 
-    def __init__(self, *, administrator_account: Optional["_models.VirtualMachineSshCredentials"] = None, **kwargs):
+    def __init__(
+        self, *, administrator_account: Optional["_models.VirtualMachineSshCredentials"] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword administrator_account: Admin credentials for virtual machine.
         :paramtype administrator_account:
@@ -20429,8 +24597,8 @@ def __init__(
         *,
         estimated_vm_prices: Optional["_models.EstimatedVMPrices"] = None,
         supported_compute_types: Optional[List[str]] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword estimated_vm_prices: The estimated price information for using a VM.
         :paramtype estimated_vm_prices: ~azure.mgmt.machinelearningservices.models.EstimatedVMPrices
@@ -20463,7 +24631,7 @@ class VirtualMachineSizeListResult(_serialization.Model):
         "value": {"key": "value", "type": "[VirtualMachineSize]"},
     }
 
-    def __init__(self, *, value: Optional[List["_models.VirtualMachineSize"]] = None, **kwargs):
+    def __init__(self, *, value: Optional[List["_models.VirtualMachineSize"]] = None, **kwargs: Any) -> None:
         """
         :keyword value: The list of virtual machine sizes supported by AmlCompute.
         :paramtype value: list[~azure.mgmt.machinelearningservices.models.VirtualMachineSize]
@@ -20499,8 +24667,8 @@ def __init__(
         password: Optional[str] = None,
         public_key_data: Optional[str] = None,
         private_key_data: Optional[str] = None,
-        **kwargs
-    ):
+        **kwargs: Any
+    ) -> None:
         """
         :keyword username: Username of admin account.
         :paramtype username: str
@@ -20518,6 +24686,103 @@ def __init__(
         self.private_key_data = private_key_data
 
 
+class VolumeDefinition(_serialization.Model):
+    """VolumeDefinition.
+
+    :ivar type: Type of Volume Definition. Possible Values: bind,volume,tmpfs,npipe. Known values
+     are: "bind", "volume", "tmpfs", and "npipe".
+    :vartype type: str or ~azure.mgmt.machinelearningservices.models.VolumeDefinitionType
+    :ivar read_only: Indicate whether to mount volume as readOnly. Default value for this is false.
+    :vartype read_only: bool
+    :ivar source: Source of the mount. For bind mounts this is the host path.
+    :vartype source: str
+    :ivar target: Target of the mount. For bind mounts this is the path in the container.
+    :vartype target: str
+    :ivar consistency: Consistency of the volume.
+    :vartype consistency: str
+    :ivar bind: Bind Options of the mount.
+    :vartype bind: ~azure.mgmt.machinelearningservices.models.BindOptions
+    :ivar volume: Volume Options of the mount.
+    :vartype volume: ~azure.mgmt.machinelearningservices.models.VolumeOptions
+    :ivar tmpfs: tmpfs option of the mount.
+    :vartype tmpfs: ~azure.mgmt.machinelearningservices.models.TmpfsOptions
+    """
+
+    _attribute_map = {
+        "type": {"key": "type", "type": "str"},
+        "read_only": {"key": "readOnly", "type": "bool"},
+        "source": {"key": "source", "type": "str"},
+        "target": {"key": "target", "type": "str"},
+        "consistency": {"key": "consistency", "type": "str"},
+        "bind": {"key": "bind", "type": "BindOptions"},
+        "volume": {"key": "volume", "type": "VolumeOptions"},
+        "tmpfs": {"key": "tmpfs", "type": "TmpfsOptions"},
+    }
+
+    def __init__(
+        self,
+        *,
+        type: Union[str, "_models.VolumeDefinitionType"] = "bind",
+        read_only: Optional[bool] = None,
+        source: Optional[str] = None,
+        target: Optional[str] = None,
+        consistency: Optional[str] = None,
+        bind: Optional["_models.BindOptions"] = None,
+        volume: Optional["_models.VolumeOptions"] = None,
+        tmpfs: Optional["_models.TmpfsOptions"] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword type: Type of Volume Definition. Possible Values: bind,volume,tmpfs,npipe. Known
+         values are: "bind", "volume", "tmpfs", and "npipe".
+        :paramtype type: str or ~azure.mgmt.machinelearningservices.models.VolumeDefinitionType
+        :keyword read_only: Indicate whether to mount volume as readOnly. Default value for this is
+         false.
+        :paramtype read_only: bool
+        :keyword source: Source of the mount. For bind mounts this is the host path.
+        :paramtype source: str
+        :keyword target: Target of the mount. For bind mounts this is the path in the container.
+        :paramtype target: str
+        :keyword consistency: Consistency of the volume.
+        :paramtype consistency: str
+        :keyword bind: Bind Options of the mount.
+        :paramtype bind: ~azure.mgmt.machinelearningservices.models.BindOptions
+        :keyword volume: Volume Options of the mount.
+        :paramtype volume: ~azure.mgmt.machinelearningservices.models.VolumeOptions
+        :keyword tmpfs: tmpfs option of the mount.
+        :paramtype tmpfs: ~azure.mgmt.machinelearningservices.models.TmpfsOptions
+        """
+        super().__init__(**kwargs)
+        self.type = type
+        self.read_only = read_only
+        self.source = source
+        self.target = target
+        self.consistency = consistency
+        self.bind = bind
+        self.volume = volume
+        self.tmpfs = tmpfs
+
+
+class VolumeOptions(_serialization.Model):
+    """VolumeOptions.
+
+    :ivar nocopy: Indicate whether volume is nocopy.
+    :vartype nocopy: bool
+    """
+
+    _attribute_map = {
+        "nocopy": {"key": "nocopy", "type": "bool"},
+    }
+
+    def __init__(self, *, nocopy: Optional[bool] = None, **kwargs: Any) -> None:
+        """
+        :keyword nocopy: Indicate whether volume is nocopy.
+        :paramtype nocopy: bool
+        """
+        super().__init__(**kwargs)
+        self.nocopy = nocopy
+
+
 class Workspace(Resource):  # pylint: disable=too-many-instance-attributes
     """An object that represents a machine learning workspace.
 
@@ -20563,7 +24828,7 @@ class Workspace(Resource):  # pylint: disable=too-many-instance-attributes
     :vartype discovery_url: str
     :ivar provisioning_state: The current deployment state of workspace resource. The
      provisioningState is to indicate states for resource provisioning. Known values are: "Unknown",
-     "Updating", "Creating", "Deleting", "Succeeded", "Failed", and "Canceled".
+     "Updating", "Creating", "Deleting", "Succeeded", "Failed", "Canceled", and "SoftDeleted".
     :vartype provisioning_state: str or
      ~azure.mgmt.machinelearningservices.models.ProvisioningState
     :ivar encryption: The encryption settings of Azure ML workspace.
@@ -20611,6 +24876,14 @@ class Workspace(Resource):  # pylint: disable=too-many-instance-attributes
     :ivar v1_legacy_mode: Enabling v1_legacy_mode may prevent you from using features provided by
      the v2 API.
     :vartype v1_legacy_mode: bool
+    :ivar soft_deleted_at: The timestamp when the workspace was soft deleted.
+    :vartype soft_deleted_at: str
+    :ivar scheduled_purge_date: The timestamp when the soft deleted workspace is going to be
+     purged.
+    :vartype scheduled_purge_date: str
+    :ivar system_datastores_auth_mode: The auth mode used for accessing the system datastores of
+     the workspace.
+    :vartype system_datastores_auth_mode: str
     """
 
     _validation = {
@@ -20627,6 +24900,8 @@ class Workspace(Resource):  # pylint: disable=too-many-instance-attributes
         "tenant_id": {"readonly": True},
         "storage_hns_enabled": {"readonly": True},
         "ml_flow_tracking_uri": {"readonly": True},
+        "soft_deleted_at": {"readonly": True},
+        "scheduled_purge_date": {"readonly": True},
     }
 
     _attribute_map = {
@@ -20672,6 +24947,9 @@ class Workspace(Resource):  # pylint: disable=too-many-instance-attributes
         "storage_hns_enabled": {"key": "properties.storageHnsEnabled", "type": "bool"},
         "ml_flow_tracking_uri": {"key": "properties.mlFlowTrackingUri", "type": "str"},
         "v1_legacy_mode": {"key": "properties.v1LegacyMode", "type": "bool"},
+        "soft_deleted_at": {"key": "properties.softDeletedAt", "type": "str"},
+        "scheduled_purge_date": {"key": "properties.scheduledPurgeDate", "type": "str"},
+        "system_datastores_auth_mode": {"key": "properties.systemDatastoresAuthMode", "type": "str"},
     }
 
     def __init__(  # pylint: disable=too-many-locals
@@ -20697,8 +24975,9 @@ def __init__(  # pylint: disable=too-many-locals
         service_managed_resources_settings: Optional["_models.ServiceManagedResourcesSettings"] = None,
         primary_user_assigned_identity: Optional[str] = None,
         v1_legacy_mode: bool = False,
-        **kwargs
-    ):
+        system_datastores_auth_mode: Optional[str] = None,
+        **kwargs: Any
+    ) -> None:
         """
         :keyword identity: The identity of the resource.
         :paramtype identity: ~azure.mgmt.machinelearningservices.models.ManagedServiceIdentity
@@ -20753,6 +25032,9 @@ def __init__(  # pylint: disable=too-many-locals
         :keyword v1_legacy_mode: Enabling v1_legacy_mode may prevent you from using features provided
          by the v2 API.
         :paramtype v1_legacy_mode: bool
+        :keyword system_datastores_auth_mode: The auth mode used for accessing the system datastores of
+         the workspace.
+        :paramtype system_datastores_auth_mode: str
         """
         super().__init__(**kwargs)
         self.identity = identity
@@ -20784,6 +25066,37 @@ def __init__(  # pylint: disable=too-many-locals
         self.storage_hns_enabled = None
         self.ml_flow_tracking_uri = None
         self.v1_legacy_mode = v1_legacy_mode
+        self.soft_deleted_at = None
+        self.scheduled_purge_date = None
+        self.system_datastores_auth_mode = system_datastores_auth_mode
+
+
+class WorkspaceConnectionAccessKey(_serialization.Model):
+    """WorkspaceConnectionAccessKey.
+
+    :ivar access_key_id:
+    :vartype access_key_id: str
+    :ivar secret_access_key:
+    :vartype secret_access_key: str
+    """
+
+    _attribute_map = {
+        "access_key_id": {"key": "accessKeyId", "type": "str"},
+        "secret_access_key": {"key": "secretAccessKey", "type": "str"},
+    }
+
+    def __init__(
+        self, *, access_key_id: Optional[str] = None, secret_access_key: Optional[str] = None, **kwargs: Any
+    ) -> None:
+        """
+        :keyword access_key_id:
+        :paramtype access_key_id: str
+        :keyword secret_access_key:
+        :paramtype secret_access_key: str
+        """
+        super().__init__(**kwargs)
+        self.access_key_id = access_key_id
+        self.secret_access_key = secret_access_key
 
 
 class WorkspaceConnectionManagedIdentity(_serialization.Model):
@@ -20800,7 +25113,7 @@ class WorkspaceConnectionManagedIdentity(_serialization.Model):
         "client_id": {"key": "clientId", "type": "str"},
     }
 
-    def __init__(self, *, resource_id: Optional[str] = None, client_id: Optional[str] = None, **kwargs):
+    def __init__(self, *, resource_id: Optional[str] = None, client_id: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword resource_id:
         :paramtype resource_id: str
@@ -20823,7 +25136,7 @@ class WorkspaceConnectionPersonalAccessToken(_serialization.Model):
         "pat": {"key": "pat", "type": "str"},
     }
 
-    def __init__(self, *, pat: Optional[str] = None, **kwargs):
+    def __init__(self, *, pat: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword pat:
         :paramtype pat: str
@@ -20870,7 +25183,7 @@ class WorkspaceConnectionPropertiesV2BasicResource(Resource):
         "properties": {"key": "properties", "type": "WorkspaceConnectionPropertiesV2"},
     }
 
-    def __init__(self, *, properties: "_models.WorkspaceConnectionPropertiesV2", **kwargs):
+    def __init__(self, *, properties: "_models.WorkspaceConnectionPropertiesV2", **kwargs: Any) -> None:
         """
         :keyword properties: Required.
         :paramtype properties:
@@ -20902,8 +25215,8 @@ class WorkspaceConnectionPropertiesV2BasicResourceArmPaginatedResult(_serializat
     }
 
     def __init__(
-        self, *, value: Optional[List["_models.WorkspaceConnectionPropertiesV2BasicResource"]] = None, **kwargs
-    ):
+        self, *, value: Optional[List["_models.WorkspaceConnectionPropertiesV2BasicResource"]] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword value:
         :paramtype value:
@@ -20914,6 +25227,45 @@ def __init__(
         self.next_link = None
 
 
+class WorkspaceConnectionServicePrincipal(_serialization.Model):
+    """WorkspaceConnectionServicePrincipal.
+
+    :ivar client_id:
+    :vartype client_id: str
+    :ivar client_secret:
+    :vartype client_secret: str
+    :ivar tenant_id:
+    :vartype tenant_id: str
+    """
+
+    _attribute_map = {
+        "client_id": {"key": "clientId", "type": "str"},
+        "client_secret": {"key": "clientSecret", "type": "str"},
+        "tenant_id": {"key": "tenantId", "type": "str"},
+    }
+
+    def __init__(
+        self,
+        *,
+        client_id: Optional[str] = None,
+        client_secret: Optional[str] = None,
+        tenant_id: Optional[str] = None,
+        **kwargs: Any
+    ) -> None:
+        """
+        :keyword client_id:
+        :paramtype client_id: str
+        :keyword client_secret:
+        :paramtype client_secret: str
+        :keyword tenant_id:
+        :paramtype tenant_id: str
+        """
+        super().__init__(**kwargs)
+        self.client_id = client_id
+        self.client_secret = client_secret
+        self.tenant_id = tenant_id
+
+
 class WorkspaceConnectionSharedAccessSignature(_serialization.Model):
     """WorkspaceConnectionSharedAccessSignature.
 
@@ -20925,7 +25277,7 @@ class WorkspaceConnectionSharedAccessSignature(_serialization.Model):
         "sas": {"key": "sas", "type": "str"},
     }
 
-    def __init__(self, *, sas: Optional[str] = None, **kwargs):
+    def __init__(self, *, sas: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword sas:
         :paramtype sas: str
@@ -20948,7 +25300,7 @@ class WorkspaceConnectionUsernamePassword(_serialization.Model):
         "password": {"key": "password", "type": "str"},
     }
 
-    def __init__(self, *, username: Optional[str] = None, password: Optional[str] = None, **kwargs):
+    def __init__(self, *, username: Optional[str] = None, password: Optional[str] = None, **kwargs: Any) -> None:
         """
         :keyword username:
         :paramtype username: str
@@ -20976,7 +25328,9 @@ class WorkspaceListResult(_serialization.Model):
         "next_link": {"key": "nextLink", "type": "str"},
     }
 
-    def __init__(self, *, value: Optional[List["_models.Workspace"]] = None, next_link: Optional[str] = None, **kwargs):
+    def __init__(
+        self, *, value: Optional[List["_models.Workspace"]] = None, next_link: Optional[str] = None, **kwargs: Any
+    ) -> None:
         """
         :keyword value: The list of machine learning workspaces. Since this list may be incomplete, the
          nextLink field should be used to request the next list of machine learning workspaces.
@@ -21019,6 +25373,8 @@ class WorkspaceUpdateParameters(_serialization.Model):  # pylint: disable=too-ma
     :vartype application_insights: str
     :ivar container_registry: ARM id of the container registry associated with this workspace.
     :vartype container_registry: str
+    :ivar encryption: The encryption settings of the workspace.
+    :vartype encryption: ~azure.mgmt.machinelearningservices.models.EncryptionUpdateProperties
     """
 
     _attribute_map = {
@@ -21036,6 +25392,7 @@ class WorkspaceUpdateParameters(_serialization.Model):  # pylint: disable=too-ma
         "public_network_access": {"key": "properties.publicNetworkAccess", "type": "str"},
         "application_insights": {"key": "properties.applicationInsights", "type": "str"},
         "container_registry": {"key": "properties.containerRegistry", "type": "str"},
+        "encryption": {"key": "properties.encryption", "type": "EncryptionUpdateProperties"},
     }
 
     def __init__(
@@ -21052,8 +25409,9 @@ def __init__(
         public_network_access: Optional[Union[str, "_models.PublicNetworkAccess"]] = None,
         application_insights: Optional[str] = None,
         container_registry: Optional[str] = None,
-        **kwargs
-    ):
+        encryption: Optional["_models.EncryptionUpdateProperties"] = None,
+        **kwargs: Any
+    ) -> None:
         """
         :keyword tags: The resource tags for the machine learning workspace.
         :paramtype tags: dict[str, str]
@@ -21082,6 +25440,8 @@ def __init__(
         :paramtype application_insights: str
         :keyword container_registry: ARM id of the container registry associated with this workspace.
         :paramtype container_registry: str
+        :keyword encryption: The encryption settings of the workspace.
+        :paramtype encryption: ~azure.mgmt.machinelearningservices.models.EncryptionUpdateProperties
         """
         super().__init__(**kwargs)
         self.tags = tags
@@ -21095,3 +25455,4 @@ def __init__(
         self.public_network_access = public_network_access
         self.application_insights = application_insights
         self.container_registry = container_registry
+        self.encryption = encryption
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/__init__.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/__init__.py
index 0065485916d9d..60c27f074b69d 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/__init__.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/__init__.py
@@ -15,6 +15,14 @@
 from ._private_endpoint_connections_operations import PrivateEndpointConnectionsOperations
 from ._private_link_resources_operations import PrivateLinkResourcesOperations
 from ._workspace_connections_operations import WorkspaceConnectionsOperations
+from ._registry_code_containers_operations import RegistryCodeContainersOperations
+from ._registry_code_versions_operations import RegistryCodeVersionsOperations
+from ._registry_component_containers_operations import RegistryComponentContainersOperations
+from ._registry_component_versions_operations import RegistryComponentVersionsOperations
+from ._registry_environment_containers_operations import RegistryEnvironmentContainersOperations
+from ._registry_environment_versions_operations import RegistryEnvironmentVersionsOperations
+from ._registry_model_containers_operations import RegistryModelContainersOperations
+from ._registry_model_versions_operations import RegistryModelVersionsOperations
 from ._batch_endpoints_operations import BatchEndpointsOperations
 from ._batch_deployments_operations import BatchDeploymentsOperations
 from ._code_containers_operations import CodeContainersOperations
@@ -27,11 +35,13 @@
 from ._environment_containers_operations import EnvironmentContainersOperations
 from ._environment_versions_operations import EnvironmentVersionsOperations
 from ._jobs_operations import JobsOperations
+from ._labeling_jobs_operations import LabelingJobsOperations
 from ._model_containers_operations import ModelContainersOperations
 from ._model_versions_operations import ModelVersionsOperations
 from ._online_endpoints_operations import OnlineEndpointsOperations
 from ._online_deployments_operations import OnlineDeploymentsOperations
 from ._schedules_operations import SchedulesOperations
+from ._registries_operations import RegistriesOperations
 from ._workspace_features_operations import WorkspaceFeaturesOperations
 
 from ._patch import __all__ as _patch_all
@@ -48,6 +58,14 @@
     "PrivateEndpointConnectionsOperations",
     "PrivateLinkResourcesOperations",
     "WorkspaceConnectionsOperations",
+    "RegistryCodeContainersOperations",
+    "RegistryCodeVersionsOperations",
+    "RegistryComponentContainersOperations",
+    "RegistryComponentVersionsOperations",
+    "RegistryEnvironmentContainersOperations",
+    "RegistryEnvironmentVersionsOperations",
+    "RegistryModelContainersOperations",
+    "RegistryModelVersionsOperations",
     "BatchEndpointsOperations",
     "BatchDeploymentsOperations",
     "CodeContainersOperations",
@@ -60,11 +78,13 @@
     "EnvironmentContainersOperations",
     "EnvironmentVersionsOperations",
     "JobsOperations",
+    "LabelingJobsOperations",
     "ModelContainersOperations",
     "ModelVersionsOperations",
     "OnlineEndpointsOperations",
     "OnlineDeploymentsOperations",
     "SchedulesOperations",
+    "RegistriesOperations",
     "WorkspaceFeaturesOperations",
 ]
 __all__.extend([p for p in _patch_all if p not in __all__])
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_batch_deployments_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_batch_deployments_operations.py
index 944b8300d2f51..e264716015747 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_batch_deployments_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_batch_deployments_operations.py
@@ -57,7 +57,9 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -102,7 +104,9 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -142,7 +146,9 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -182,7 +188,9 @@ def build_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -229,7 +237,9 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -321,7 +331,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.BatchDeploymentTrackedResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -413,7 +423,7 @@ def _delete_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -490,7 +500,7 @@ def begin_delete(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -567,7 +577,7 @@ def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.BatchDeployment] = kwargs.pop("cls", None)
@@ -628,7 +638,7 @@ def _update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -830,7 +840,7 @@ def begin_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -899,7 +909,7 @@ def _create_or_update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1100,7 +1110,7 @@ def begin_create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_batch_endpoints_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_batch_endpoints_operations.py
index 5813ab488a03e..9ba6ac125e075 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_batch_endpoints_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_batch_endpoints_operations.py
@@ -55,7 +55,9 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -92,7 +94,9 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -126,7 +130,9 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -160,7 +166,9 @@ def build_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -199,7 +207,9 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -238,7 +248,9 @@ def build_list_keys_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -315,7 +327,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.BatchEndpointTrackedResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -405,7 +417,7 @@ def _delete_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -479,7 +491,7 @@ def begin_delete(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -553,7 +565,7 @@ def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.BatchEndpoint] = kwargs.pop("cls", None)
@@ -612,7 +624,7 @@ def _update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -800,7 +812,7 @@ def begin_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -867,7 +879,7 @@ def _create_or_update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1055,7 +1067,7 @@ def begin_create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1134,7 +1146,7 @@ def list_keys(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.EndpointAuthKeys] = kwargs.pop("cls", None)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_code_containers_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_code_containers_operations.py
index 637d9072c910e..4e790963f4642 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_code_containers_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_code_containers_operations.py
@@ -47,7 +47,9 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -82,7 +84,9 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -116,7 +120,9 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -150,7 +156,9 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -223,7 +231,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.CodeContainerResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -329,7 +337,7 @@ def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -394,7 +402,7 @@ def get(self, resource_group_name: str, workspace_name: str, name: str, **kwargs
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.CodeContainer] = kwargs.pop("cls", None)
@@ -542,7 +550,7 @@ def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_code_versions_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_code_versions_operations.py
index 561baa83676ce..e94ccffa3ebdf 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_code_versions_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_code_versions_operations.py
@@ -55,7 +55,9 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -95,7 +97,9 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -130,7 +134,9 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -165,7 +171,9 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -252,7 +260,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.CodeVersionResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -363,7 +371,7 @@ def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -433,7 +441,7 @@ def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.CodeVersion] = kwargs.pop("cls", None)
@@ -590,7 +598,7 @@ def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_component_containers_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_component_containers_operations.py
index 4b9361f4a37cd..0ce597767ec43 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_component_containers_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_component_containers_operations.py
@@ -53,7 +53,9 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -90,7 +92,9 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -124,7 +128,9 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -158,7 +164,9 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -240,7 +248,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ComponentContainerResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -347,7 +355,7 @@ def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -414,7 +422,7 @@ def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ComponentContainer] = kwargs.pop("cls", None)
@@ -562,7 +570,7 @@ def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_component_versions_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_component_versions_operations.py
index 5e99740483dbc..4d560d02dbc34 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_component_versions_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_component_versions_operations.py
@@ -56,7 +56,9 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -98,7 +100,9 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -133,7 +137,9 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -168,7 +174,9 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -260,7 +268,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ComponentVersionResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -372,7 +380,7 @@ def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -442,7 +450,7 @@ def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ComponentVersion] = kwargs.pop("cls", None)
@@ -599,7 +607,7 @@ def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_compute_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_compute_operations.py
index 51927e5687de6..31077fb07eebd 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_compute_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_compute_operations.py
@@ -7,7 +7,7 @@
 # Changes may cause incorrect behavior and will be lost if the code is regenerated.
 # --------------------------------------------------------------------------
 import sys
-from typing import Any, Callable, Dict, IO, Iterable, Optional, TypeVar, Union, cast, overload
+from typing import Any, Callable, Dict, IO, Iterable, List, Optional, TypeVar, Union, cast, overload
 import urllib.parse
 
 from azure.core.exceptions import (
@@ -49,7 +49,9 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -84,7 +86,9 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -118,7 +122,9 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -155,7 +161,9 @@ def build_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -198,7 +206,9 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -229,13 +239,54 @@ def build_delete_request(
     return HttpRequest(method="DELETE", url=_url, params=_params, headers=_headers, **kwargs)
 
 
+def build_update_custom_services_request(
+    resource_group_name: str, workspace_name: str, compute_name: str, subscription_id: str, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/customServices",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, "str"),
+        "computeName": _SERIALIZER.url("compute_name", compute_name, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    if content_type is not None:
+        _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str")
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs)
+
+
 def build_list_nodes_request(
     resource_group_name: str, workspace_name: str, compute_name: str, subscription_id: str, **kwargs: Any
 ) -> HttpRequest:
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -269,7 +320,9 @@ def build_list_keys_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -303,7 +356,9 @@ def build_start_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -337,7 +392,9 @@ def build_stop_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -371,7 +428,9 @@ def build_restart_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -399,6 +458,45 @@ def build_restart_request(
     return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs)
 
 
+def build_update_idle_shutdown_setting_request(
+    resource_group_name: str, workspace_name: str, compute_name: str, subscription_id: str, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/updateIdleShutdownSetting",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, "str"),
+        "computeName": _SERIALIZER.url("compute_name", compute_name, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    if content_type is not None:
+        _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str")
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs)
+
+
 class ComputeOperations:
     """
     .. warning::
@@ -440,7 +538,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.PaginatedComputeResourcesList] = kwargs.pop("cls", None)
@@ -545,7 +643,7 @@ def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ComputeResource] = kwargs.pop("cls", None)
@@ -604,7 +702,7 @@ def _create_or_update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -792,7 +890,7 @@ def begin_create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -859,7 +957,7 @@ def _update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1035,7 +1133,7 @@ def begin_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1102,7 +1200,7 @@ def _delete_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -1184,7 +1282,7 @@ def begin_delete(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -1228,6 +1326,156 @@ def get_long_running_output(pipeline_response):  # pylint: disable=inconsistent-
         "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}"
     }
 
+    @overload
+    def update_custom_services(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        compute_name: str,
+        custom_services: List[_models.CustomService],
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> None:
+        """Updates the custom services list. The list of custom services provided shall be overwritten.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace. Required.
+        :type workspace_name: str
+        :param compute_name: Name of the Azure Machine Learning compute. Required.
+        :type compute_name: str
+        :param custom_services: New list of Custom Services. Required.
+        :type custom_services: list[~azure.mgmt.machinelearningservices.models.CustomService]
+        :keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None or the result of cls(response)
+        :rtype: None
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @overload
+    def update_custom_services(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        compute_name: str,
+        custom_services: IO,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> None:
+        """Updates the custom services list. The list of custom services provided shall be overwritten.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace. Required.
+        :type workspace_name: str
+        :param compute_name: Name of the Azure Machine Learning compute. Required.
+        :type compute_name: str
+        :param custom_services: New list of Custom Services. Required.
+        :type custom_services: IO
+        :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None or the result of cls(response)
+        :rtype: None
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @distributed_trace
+    def update_custom_services(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        compute_name: str,
+        custom_services: Union[List[_models.CustomService], IO],
+        **kwargs: Any
+    ) -> None:
+        """Updates the custom services list. The list of custom services provided shall be overwritten.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace. Required.
+        :type workspace_name: str
+        :param compute_name: Name of the Azure Machine Learning compute. Required.
+        :type compute_name: str
+        :param custom_services: New list of Custom Services. Is either a list type or a IO type.
+         Required.
+        :type custom_services: list[~azure.mgmt.machinelearningservices.models.CustomService] or IO
+        :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
+         Default value is None.
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None or the result of cls(response)
+        :rtype: None
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[None] = kwargs.pop("cls", None)
+
+        content_type = content_type or "application/json"
+        _json = None
+        _content = None
+        if isinstance(custom_services, (IO, bytes)):
+            _content = custom_services
+        else:
+            _json = self._serialize.body(custom_services, "[CustomService]")
+
+        request = build_update_custom_services_request(
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            compute_name=compute_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            content=_content,
+            template_url=self.update_custom_services.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    update_custom_services.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/customServices"
+    }
+
     @distributed_trace
     def list_nodes(
         self, resource_group_name: str, workspace_name: str, compute_name: str, **kwargs: Any
@@ -1251,7 +1499,7 @@ def list_nodes(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.AmlComputeNodesInformation] = kwargs.pop("cls", None)
@@ -1355,7 +1603,7 @@ def list_keys(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ComputeSecrets] = kwargs.pop("cls", None)
@@ -1409,7 +1657,7 @@ def _start_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -1473,7 +1721,7 @@ def begin_start(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -1530,7 +1778,7 @@ def _stop_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -1594,7 +1842,7 @@ def begin_stop(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -1651,7 +1899,7 @@ def _restart_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -1715,7 +1963,7 @@ def begin_restart(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -1757,3 +2005,155 @@ def get_long_running_output(pipeline_response):  # pylint: disable=inconsistent-
     begin_restart.metadata = {
         "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/restart"
     }
+
+    @overload
+    def update_idle_shutdown_setting(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        compute_name: str,
+        parameters: _models.IdleShutdownSetting,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> None:
+        """Updates the idle shutdown setting of a compute instance.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace. Required.
+        :type workspace_name: str
+        :param compute_name: Name of the Azure Machine Learning compute. Required.
+        :type compute_name: str
+        :param parameters: The object for updating idle shutdown setting of specified ComputeInstance.
+         Required.
+        :type parameters: ~azure.mgmt.machinelearningservices.models.IdleShutdownSetting
+        :keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None or the result of cls(response)
+        :rtype: None
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @overload
+    def update_idle_shutdown_setting(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        compute_name: str,
+        parameters: IO,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> None:
+        """Updates the idle shutdown setting of a compute instance.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace. Required.
+        :type workspace_name: str
+        :param compute_name: Name of the Azure Machine Learning compute. Required.
+        :type compute_name: str
+        :param parameters: The object for updating idle shutdown setting of specified ComputeInstance.
+         Required.
+        :type parameters: IO
+        :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None or the result of cls(response)
+        :rtype: None
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @distributed_trace
+    def update_idle_shutdown_setting(  # pylint: disable=inconsistent-return-statements
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        compute_name: str,
+        parameters: Union[_models.IdleShutdownSetting, IO],
+        **kwargs: Any
+    ) -> None:
+        """Updates the idle shutdown setting of a compute instance.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace. Required.
+        :type workspace_name: str
+        :param compute_name: Name of the Azure Machine Learning compute. Required.
+        :type compute_name: str
+        :param parameters: The object for updating idle shutdown setting of specified ComputeInstance.
+         Is either a model type or a IO type. Required.
+        :type parameters: ~azure.mgmt.machinelearningservices.models.IdleShutdownSetting or IO
+        :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
+         Default value is None.
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None or the result of cls(response)
+        :rtype: None
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[None] = kwargs.pop("cls", None)
+
+        content_type = content_type or "application/json"
+        _json = None
+        _content = None
+        if isinstance(parameters, (IO, bytes)):
+            _content = parameters
+        else:
+            _json = self._serialize.body(parameters, "IdleShutdownSetting")
+
+        request = build_update_idle_shutdown_setting_request(
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            compute_name=compute_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            content=_content,
+            template_url=self.update_idle_shutdown_setting.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    update_idle_shutdown_setting.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/updateIdleShutdownSetting"
+    }
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_data_containers_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_data_containers_operations.py
index 3b5d3acc38c6c..27fcd9c53d1e1 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_data_containers_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_data_containers_operations.py
@@ -53,7 +53,9 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -90,7 +92,9 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -124,7 +128,9 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -158,7 +164,9 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -239,7 +247,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.DataContainerResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -346,7 +354,7 @@ def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -411,7 +419,7 @@ def get(self, resource_group_name: str, workspace_name: str, name: str, **kwargs
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.DataContainer] = kwargs.pop("cls", None)
@@ -559,7 +567,7 @@ def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_data_versions_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_data_versions_operations.py
index 092363a5e2f3d..f79d99d2d5572 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_data_versions_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_data_versions_operations.py
@@ -57,7 +57,9 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -101,7 +103,9 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -136,7 +140,9 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -171,7 +177,9 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -271,7 +279,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.DataVersionBaseResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -384,7 +392,7 @@ def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -454,7 +462,7 @@ def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.DataVersionBase] = kwargs.pop("cls", None)
@@ -611,7 +619,7 @@ def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_datastores_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_datastores_operations.py
index bc01c32a0983d..e565cacfeab47 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_datastores_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_datastores_operations.py
@@ -58,7 +58,9 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -105,7 +107,9 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -139,7 +143,9 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -179,7 +185,9 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -218,7 +226,9 @@ def build_list_secrets_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -310,7 +320,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.DatastoreResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -422,7 +432,7 @@ def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -487,7 +497,7 @@ def get(self, resource_group_name: str, workspace_name: str, name: str, **kwargs
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.Datastore] = kwargs.pop("cls", None)
@@ -644,7 +654,7 @@ def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -732,7 +742,7 @@ def list_secrets(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.DatastoreSecrets] = kwargs.pop("cls", None)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_environment_containers_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_environment_containers_operations.py
index d80de1f2b3c77..eb642cf1293ce 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_environment_containers_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_environment_containers_operations.py
@@ -53,7 +53,9 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -90,7 +92,9 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -124,7 +128,9 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -158,7 +164,9 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -241,7 +249,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.EnvironmentContainerResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -348,7 +356,7 @@ def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -415,7 +423,7 @@ def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.EnvironmentContainer] = kwargs.pop("cls", None)
@@ -563,7 +571,7 @@ def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_environment_versions_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_environment_versions_operations.py
index d898cb3c67dd0..55aa97234462a 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_environment_versions_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_environment_versions_operations.py
@@ -56,7 +56,9 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -98,7 +100,9 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -133,7 +137,9 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -168,7 +174,9 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -260,7 +268,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.EnvironmentVersionResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -372,7 +380,7 @@ def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -442,7 +450,7 @@ def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.EnvironmentVersion] = kwargs.pop("cls", None)
@@ -599,7 +607,7 @@ def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_jobs_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_jobs_operations.py
index cd8bd0bd0c478..b60639625a2d2 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_jobs_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_jobs_operations.py
@@ -52,12 +52,16 @@ def build_list_request(
     job_type: Optional[str] = None,
     tag: Optional[str] = None,
     list_view_type: Optional[Union[str, _models.ListViewType]] = None,
+    scheduled: Optional[bool] = None,
+    schedule_id: Optional[str] = None,
     **kwargs: Any
 ) -> HttpRequest:
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -85,6 +89,10 @@ def build_list_request(
         _params["tag"] = _SERIALIZER.query("tag", tag, "str")
     if list_view_type is not None:
         _params["listViewType"] = _SERIALIZER.query("list_view_type", list_view_type, "str")
+    if scheduled is not None:
+        _params["scheduled"] = _SERIALIZER.query("scheduled", scheduled, "bool")
+    if schedule_id is not None:
+        _params["scheduleId"] = _SERIALIZER.query("schedule_id", schedule_id, "str")
 
     # Construct headers
     _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
@@ -98,7 +106,9 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -132,7 +142,9 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -166,7 +178,9 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -203,7 +217,9 @@ def build_cancel_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -259,6 +275,8 @@ def list(
         job_type: Optional[str] = None,
         tag: Optional[str] = None,
         list_view_type: Optional[Union[str, _models.ListViewType]] = None,
+        scheduled: Optional[bool] = None,
+        schedule_id: Optional[str] = None,
         **kwargs: Any
     ) -> Iterable["_models.JobBase"]:
         """Lists Jobs in the workspace.
@@ -279,6 +297,10 @@ def list(
         :param list_view_type: View type for including/excluding (for example) archived entities. Known
          values are: "ActiveOnly", "ArchivedOnly", and "All". Default value is None.
         :type list_view_type: str or ~azure.mgmt.machinelearningservices.models.ListViewType
+        :param scheduled: Indicator whether the job is scheduled job. Default value is None.
+        :type scheduled: bool
+        :param schedule_id: The scheduled id for listing the job triggered from. Default value is None.
+        :type schedule_id: str
         :keyword callable cls: A custom type or function that will be passed the direct response
         :return: An iterator like instance of either JobBase or the result of cls(response)
         :rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.JobBase]
@@ -287,7 +309,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.JobBaseResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -311,6 +333,8 @@ def prepare_request(next_link=None):
                     job_type=job_type,
                     tag=tag,
                     list_view_type=list_view_type,
+                    scheduled=scheduled,
+                    schedule_id=schedule_id,
                     api_version=api_version,
                     template_url=self.list.metadata["url"],
                     headers=_headers,
@@ -379,7 +403,7 @@ def _delete_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -451,7 +475,7 @@ def begin_delete(self, resource_group_name: str, workspace_name: str, id: str, *
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -523,7 +547,7 @@ def get(self, resource_group_name: str, workspace_name: str, id: str, **kwargs:
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.JobBase] = kwargs.pop("cls", None)
@@ -665,7 +689,7 @@ def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -735,7 +759,7 @@ def _cancel_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -804,7 +828,7 @@ def begin_cancel(self, resource_group_name: str, workspace_name: str, id: str, *
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_labeling_jobs_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_labeling_jobs_operations.py
new file mode 100644
index 0000000000000..a98ce92e571df
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_labeling_jobs_operations.py
@@ -0,0 +1,1303 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+import sys
+from typing import Any, Callable, Dict, IO, Iterable, Optional, TypeVar, Union, cast, overload
+import urllib.parse
+
+from azure.core.exceptions import (
+    ClientAuthenticationError,
+    HttpResponseError,
+    ResourceExistsError,
+    ResourceNotFoundError,
+    ResourceNotModifiedError,
+    map_error,
+)
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.polling import LROPoller, NoPolling, PollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.utils import case_insensitive_dict
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.arm_polling import ARMPolling
+
+from .. import models as _models
+from .._serialization import Serializer
+from .._vendor import _convert_request, _format_url_section
+
+if sys.version_info >= (3, 8):
+    from typing import Literal  # pylint: disable=no-name-in-module, ungrouped-imports
+else:
+    from typing_extensions import Literal  # type: ignore  # pylint: disable=ungrouped-imports
+T = TypeVar("T")
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+
+
+def build_list_request(
+    resource_group_name: str,
+    workspace_name: str,
+    subscription_id: str,
+    *,
+    skip: Optional[str] = None,
+    top: Optional[int] = None,
+    **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/labelingJobs",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+    if skip is not None:
+        _params["$skip"] = _SERIALIZER.query("skip", skip, "str")
+    if top is not None:
+        _params["$top"] = _SERIALIZER.query("top", top, "int")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_delete_request(
+    resource_group_name: str, workspace_name: str, id: str, subscription_id: str, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/labelingJobs/{id}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, "str"),
+        "id": _SERIALIZER.url("id", id, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="DELETE", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_get_request(
+    resource_group_name: str,
+    workspace_name: str,
+    id: str,
+    subscription_id: str,
+    *,
+    include_job_instructions: bool = False,
+    include_label_categories: bool = False,
+    **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/labelingJobs/{id}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, "str"),
+        "id": _SERIALIZER.url("id", id, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+    if include_job_instructions is not None:
+        _params["includeJobInstructions"] = _SERIALIZER.query(
+            "include_job_instructions", include_job_instructions, "bool"
+        )
+    if include_label_categories is not None:
+        _params["includeLabelCategories"] = _SERIALIZER.query(
+            "include_label_categories", include_label_categories, "bool"
+        )
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_create_or_update_request(
+    resource_group_name: str, workspace_name: str, id: str, subscription_id: str, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/labelingJobs/{id}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, "str"),
+        "id": _SERIALIZER.url("id", id, "str", pattern=r"^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    if content_type is not None:
+        _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str")
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="PUT", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_export_labels_request(
+    resource_group_name: str, workspace_name: str, id: str, subscription_id: str, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/labelingJobs/{id}/exportLabels",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, "str"),
+        "id": _SERIALIZER.url("id", id, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    if content_type is not None:
+        _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str")
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_pause_request(
+    resource_group_name: str, workspace_name: str, id: str, subscription_id: str, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/labelingJobs/{id}/pause",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, "str"),
+        "id": _SERIALIZER.url("id", id, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_resume_request(
+    resource_group_name: str, workspace_name: str, id: str, subscription_id: str, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/labelingJobs/{id}/resume",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, "str"),
+        "id": _SERIALIZER.url("id", id, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+class LabelingJobsOperations:
+    """
+    .. warning::
+        **DO NOT** instantiate this class directly.
+
+        Instead, you should access the following operations through
+        :class:`~azure.mgmt.machinelearningservices.MachineLearningServicesMgmtClient`'s
+        :attr:`labeling_jobs` attribute.
+    """
+
+    models = _models
+
+    def __init__(self, *args, **kwargs):
+        input_args = list(args)
+        self._client = input_args.pop(0) if input_args else kwargs.pop("client")
+        self._config = input_args.pop(0) if input_args else kwargs.pop("config")
+        self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer")
+        self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer")
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        skip: Optional[str] = None,
+        top: Optional[int] = None,
+        **kwargs: Any
+    ) -> Iterable["_models.LabelingJob"]:
+        """Lists labeling jobs in the workspace.
+
+        Lists labeling jobs in the workspace.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace. Required.
+        :type workspace_name: str
+        :param skip: Continuation token for pagination. Default value is None.
+        :type skip: str
+        :param top: Number of labeling jobs to return. Default value is None.
+        :type top: int
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either LabelingJob or the result of cls(response)
+        :rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.LabelingJob]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.LabelingJobResourceArmPaginatedResult] = kwargs.pop("cls", None)
+
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        def prepare_request(next_link=None):
+            if not next_link:
+
+                request = build_list_request(
+                    resource_group_name=resource_group_name,
+                    workspace_name=workspace_name,
+                    subscription_id=self._config.subscription_id,
+                    skip=skip,
+                    top=top,
+                    api_version=api_version,
+                    template_url=self.list.metadata["url"],
+                    headers=_headers,
+                    params=_params,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                # make call to next link with the client's api-version
+                _parsed_next_link = urllib.parse.urlparse(next_link)
+                _next_request_params = case_insensitive_dict(
+                    {
+                        key: [urllib.parse.quote(v) for v in value]
+                        for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items()
+                    }
+                )
+                _next_request_params["api-version"] = self._config.api_version
+                request = HttpRequest(
+                    "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("LabelingJobResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)  # type: ignore
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+                request, stream=False, **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+        return ItemPaged(get_next, extract_data)
+
+    list.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/labelingJobs"
+    }
+
+    @distributed_trace
+    def delete(  # pylint: disable=inconsistent-return-statements
+        self, resource_group_name: str, workspace_name: str, id: str, **kwargs: Any
+    ) -> None:
+        """Delete a labeling job.
+
+        Delete a labeling job.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace. Required.
+        :type workspace_name: str
+        :param id: The name and identifier for the LabelingJob. Required.
+        :type id: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None or the result of cls(response)
+        :rtype: None
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+
+        request = build_delete_request(
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            id=id,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self.delete.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    delete.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/labelingJobs/{id}"
+    }
+
+    @distributed_trace
+    def get(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        id: str,
+        include_job_instructions: bool = False,
+        include_label_categories: bool = False,
+        **kwargs: Any
+    ) -> _models.LabelingJob:
+        """Gets a labeling job by name/id.
+
+        Gets a labeling job by name/id.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace. Required.
+        :type workspace_name: str
+        :param id: The name and identifier for the LabelingJob. Required.
+        :type id: str
+        :param include_job_instructions: Boolean value to indicate whether to include JobInstructions
+         in response. Default value is False.
+        :type include_job_instructions: bool
+        :param include_label_categories: Boolean value to indicate Whether to include LabelCategories
+         in response. Default value is False.
+        :type include_label_categories: bool
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: LabelingJob or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.LabelingJob
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.LabelingJob] = kwargs.pop("cls", None)
+
+        request = build_get_request(
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            id=id,
+            subscription_id=self._config.subscription_id,
+            include_job_instructions=include_job_instructions,
+            include_label_categories=include_label_categories,
+            api_version=api_version,
+            template_url=self.get.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize("LabelingJob", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/labelingJobs/{id}"
+    }
+
+    def _create_or_update_initial(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        id: str,
+        body: Union[_models.LabelingJob, IO],
+        **kwargs: Any
+    ) -> _models.LabelingJob:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.LabelingJob] = kwargs.pop("cls", None)
+
+        content_type = content_type or "application/json"
+        _json = None
+        _content = None
+        if isinstance(body, (IO, bytes)):
+            _content = body
+        else:
+            _json = self._serialize.body(body, "LabelingJob")
+
+        request = build_create_or_update_request(
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            id=id,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            content=_content,
+            template_url=self._create_or_update_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize("LabelingJob", pipeline_response)
+
+        if response.status_code == 201:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Azure-AsyncOperation"] = self._deserialize(
+                "str", response.headers.get("Azure-AsyncOperation")
+            )
+
+            deserialized = self._deserialize("LabelingJob", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)  # type: ignore
+
+        return deserialized  # type: ignore
+
+    _create_or_update_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/labelingJobs/{id}"
+    }
+
+    @overload
+    def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        id: str,
+        body: _models.LabelingJob,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> LROPoller[_models.LabelingJob]:
+        """Creates or updates a labeling job (asynchronous).
+
+        Creates or updates a labeling job (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace. Required.
+        :type workspace_name: str
+        :param id: The name and identifier for the LabelingJob. Required.
+        :type id: str
+        :param body: LabelingJob definition object. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.LabelingJob
+        :keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either LabelingJob or the result of
+         cls(response)
+        :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.LabelingJob]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @overload
+    def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        id: str,
+        body: IO,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> LROPoller[_models.LabelingJob]:
+        """Creates or updates a labeling job (asynchronous).
+
+        Creates or updates a labeling job (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace. Required.
+        :type workspace_name: str
+        :param id: The name and identifier for the LabelingJob. Required.
+        :type id: str
+        :param body: LabelingJob definition object. Required.
+        :type body: IO
+        :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either LabelingJob or the result of
+         cls(response)
+        :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.LabelingJob]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @distributed_trace
+    def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        id: str,
+        body: Union[_models.LabelingJob, IO],
+        **kwargs: Any
+    ) -> LROPoller[_models.LabelingJob]:
+        """Creates or updates a labeling job (asynchronous).
+
+        Creates or updates a labeling job (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace. Required.
+        :type workspace_name: str
+        :param id: The name and identifier for the LabelingJob. Required.
+        :type id: str
+        :param body: LabelingJob definition object. Is either a model type or a IO type. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.LabelingJob or IO
+        :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
+         Default value is None.
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either LabelingJob or the result of
+         cls(response)
+        :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.LabelingJob]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.LabelingJob] = kwargs.pop("cls", None)
+        polling: Union[bool, PollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                id=id,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):
+            deserialized = self._deserialize("LabelingJob", pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+        if polling is True:
+            polling_method: PollingMethod = cast(PollingMethod, ARMPolling(lro_delay, **kwargs))
+        elif polling is False:
+            polling_method = cast(PollingMethod, NoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_create_or_update.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/labelingJobs/{id}"
+    }
+
+    def _export_labels_initial(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        id: str,
+        body: Union[_models.ExportSummary, IO],
+        **kwargs: Any
+    ) -> Optional[_models.ExportSummary]:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[Optional[_models.ExportSummary]] = kwargs.pop("cls", None)
+
+        content_type = content_type or "application/json"
+        _json = None
+        _content = None
+        if isinstance(body, (IO, bytes)):
+            _content = body
+        else:
+            _json = self._serialize.body(body, "ExportSummary")
+
+        request = build_export_labels_request(
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            id=id,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            content=_content,
+            template_url=self._export_labels_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = None
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize("ExportSummary", pipeline_response)
+
+        if response.status_code == 202:
+            response_headers["Location"] = self._deserialize("str", response.headers.get("Location"))
+            response_headers["Retry-After"] = self._deserialize("int", response.headers.get("Retry-After"))
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)
+
+        return deserialized
+
+    _export_labels_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/labelingJobs/{id}/exportLabels"
+    }
+
+    @overload
+    def begin_export_labels(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        id: str,
+        body: _models.ExportSummary,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> LROPoller[_models.ExportSummary]:
+        """Export labels from a labeling job (asynchronous).
+
+        Export labels from a labeling job (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace. Required.
+        :type workspace_name: str
+        :param id: The name and identifier for the LabelingJob. Required.
+        :type id: str
+        :param body: The export summary. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.ExportSummary
+        :keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either ExportSummary or the result of
+         cls(response)
+        :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.ExportSummary]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @overload
+    def begin_export_labels(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        id: str,
+        body: IO,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> LROPoller[_models.ExportSummary]:
+        """Export labels from a labeling job (asynchronous).
+
+        Export labels from a labeling job (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace. Required.
+        :type workspace_name: str
+        :param id: The name and identifier for the LabelingJob. Required.
+        :type id: str
+        :param body: The export summary. Required.
+        :type body: IO
+        :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either ExportSummary or the result of
+         cls(response)
+        :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.ExportSummary]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @distributed_trace
+    def begin_export_labels(
+        self,
+        resource_group_name: str,
+        workspace_name: str,
+        id: str,
+        body: Union[_models.ExportSummary, IO],
+        **kwargs: Any
+    ) -> LROPoller[_models.ExportSummary]:
+        """Export labels from a labeling job (asynchronous).
+
+        Export labels from a labeling job (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace. Required.
+        :type workspace_name: str
+        :param id: The name and identifier for the LabelingJob. Required.
+        :type id: str
+        :param body: The export summary. Is either a model type or a IO type. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.ExportSummary or IO
+        :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
+         Default value is None.
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either ExportSummary or the result of
+         cls(response)
+        :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.ExportSummary]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.ExportSummary] = kwargs.pop("cls", None)
+        polling: Union[bool, PollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = self._export_labels_initial(
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                id=id,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):
+            deserialized = self._deserialize("ExportSummary", pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+        if polling is True:
+            polling_method: PollingMethod = cast(
+                PollingMethod, ARMPolling(lro_delay, lro_options={"final-state-via": "location"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(PollingMethod, NoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_export_labels.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/labelingJobs/{id}/exportLabels"
+    }
+
+    @distributed_trace
+    def pause(  # pylint: disable=inconsistent-return-statements
+        self, resource_group_name: str, workspace_name: str, id: str, **kwargs: Any
+    ) -> None:
+        """Pause a labeling job.
+
+        Pause a labeling job.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace. Required.
+        :type workspace_name: str
+        :param id: The name and identifier for the LabelingJob. Required.
+        :type id: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: None or the result of cls(response)
+        :rtype: None
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+
+        request = build_pause_request(
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            id=id,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self.pause.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        if cls:
+            return cls(pipeline_response, None, {})
+
+    pause.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/labelingJobs/{id}/pause"
+    }
+
+    def _resume_initial(  # pylint: disable=inconsistent-return-statements
+        self, resource_group_name: str, workspace_name: str, id: str, **kwargs: Any
+    ) -> None:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+
+        request = build_resume_request(
+            resource_group_name=resource_group_name,
+            workspace_name=workspace_name,
+            id=id,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self._resume_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers["Location"] = self._deserialize("str", response.headers.get("Location"))
+            response_headers["Retry-After"] = self._deserialize("int", response.headers.get("Retry-After"))
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _resume_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/labelingJobs/{id}/resume"
+    }
+
+    @distributed_trace
+    def begin_resume(self, resource_group_name: str, workspace_name: str, id: str, **kwargs: Any) -> LROPoller[None]:
+        """Resume a labeling job (asynchronous).
+
+        Resume a labeling job (asynchronous).
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param workspace_name: Name of Azure Machine Learning workspace. Required.
+        :type workspace_name: str
+        :param id: The name and identifier for the LabelingJob. Required.
+        :type id: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[None]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+        polling: Union[bool, PollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = self._resume_initial(  # type: ignore
+                resource_group_name=resource_group_name,
+                workspace_name=workspace_name,
+                id=id,
+                api_version=api_version,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):  # pylint: disable=inconsistent-return-statements
+            if cls:
+                return cls(pipeline_response, None, {})
+
+        if polling is True:
+            polling_method: PollingMethod = cast(
+                PollingMethod, ARMPolling(lro_delay, lro_options={"final-state-via": "location"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(PollingMethod, NoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_resume.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/labelingJobs/{id}/resume"
+    }
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_model_containers_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_model_containers_operations.py
index 2c416fba27722..bd1f00ee4e53d 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_model_containers_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_model_containers_operations.py
@@ -54,7 +54,9 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -93,7 +95,9 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -127,7 +131,9 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -161,7 +167,9 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -245,7 +253,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ModelContainerResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -353,7 +361,7 @@ def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -418,7 +426,7 @@ def get(self, resource_group_name: str, workspace_name: str, name: str, **kwargs
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ModelContainer] = kwargs.pop("cls", None)
@@ -566,7 +574,7 @@ def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_model_versions_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_model_versions_operations.py
index 775841fe3ae4f..ac27b0d0b551c 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_model_versions_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_model_versions_operations.py
@@ -62,7 +62,9 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -116,7 +118,9 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -151,7 +155,9 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -186,7 +192,9 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -297,7 +305,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ModelVersionResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -415,7 +423,7 @@ def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -485,7 +493,7 @@ def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ModelVersion] = kwargs.pop("cls", None)
@@ -642,7 +650,7 @@ def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_online_deployments_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_online_deployments_operations.py
index cf048b3e29939..8c85ffe32dcdf 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_online_deployments_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_online_deployments_operations.py
@@ -57,7 +57,9 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -102,7 +104,9 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -142,7 +146,9 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -182,7 +188,9 @@ def build_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -229,7 +237,9 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -276,7 +286,9 @@ def build_get_logs_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -322,7 +334,9 @@ def build_list_skus_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -411,7 +425,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.OnlineDeploymentTrackedResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -503,7 +517,7 @@ def _delete_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -580,7 +594,7 @@ def begin_delete(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -657,7 +671,7 @@ def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.OnlineDeployment] = kwargs.pop("cls", None)
@@ -718,7 +732,7 @@ def _update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -918,7 +932,7 @@ def begin_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -987,7 +1001,7 @@ def _create_or_update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1188,7 +1202,7 @@ def begin_create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1354,7 +1368,7 @@ def get_logs(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1443,7 +1457,7 @@ def list_skus(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.SkuResourceArmPaginatedResult] = kwargs.pop("cls", None)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_online_endpoints_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_online_endpoints_operations.py
index 9d9051dce1f71..e9094b4ddbfab 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_online_endpoints_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_online_endpoints_operations.py
@@ -60,7 +60,9 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -107,7 +109,9 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -141,7 +145,9 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -175,7 +181,9 @@ def build_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -212,7 +220,9 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -251,7 +261,9 @@ def build_list_keys_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -285,7 +297,9 @@ def build_regenerate_keys_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -322,7 +336,9 @@ def build_get_token_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -420,7 +436,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.OnlineEndpointTrackedResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -515,7 +531,7 @@ def _delete_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -589,7 +605,7 @@ def begin_delete(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -663,7 +679,7 @@ def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.OnlineEndpoint] = kwargs.pop("cls", None)
@@ -722,7 +738,7 @@ def _update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -913,7 +929,7 @@ def begin_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -980,7 +996,7 @@ def _create_or_update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1171,7 +1187,7 @@ def begin_create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1250,7 +1266,7 @@ def list_keys(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.EndpointAuthKeys] = kwargs.pop("cls", None)
@@ -1309,7 +1325,7 @@ def _regenerate_keys_initial(  # pylint: disable=inconsistent-return-statements
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1482,7 +1498,7 @@ def begin_regenerate_keys(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1561,7 +1577,7 @@ def get_token(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.EndpointAuthToken] = kwargs.pop("cls", None)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_operations.py
index 369a4a8166c93..e2dd286f1f7bc 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_operations.py
@@ -45,7 +45,9 @@ def build_list_request(**kwargs: Any) -> HttpRequest:
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -81,7 +83,7 @@ def __init__(self, *args, **kwargs):
 
     @distributed_trace
     def list(self, **kwargs: Any) -> Iterable["_models.AmlOperation"]:
-        """Lists all of the available Azure Machine Learning Workspaces REST API operations.
+        """Lists all of the available Azure Machine Learning Services REST API operations.
 
         :keyword callable cls: A custom type or function that will be passed the direct response
         :return: An iterator like instance of either AmlOperation or the result of cls(response)
@@ -91,7 +93,7 @@ def list(self, **kwargs: Any) -> Iterable["_models.AmlOperation"]:
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.AmlOperationListResult] = kwargs.pop("cls", None)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_private_endpoint_connections_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_private_endpoint_connections_operations.py
index 6be9a15c5c601..72fe910a28e8a 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_private_endpoint_connections_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_private_endpoint_connections_operations.py
@@ -47,7 +47,9 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -84,7 +86,9 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -124,7 +128,9 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -167,7 +173,9 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -237,7 +245,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.PrivateEndpointConnectionListResult] = kwargs.pop("cls", None)
@@ -341,7 +349,7 @@ def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.PrivateEndpointConnection] = kwargs.pop("cls", None)
@@ -486,7 +494,7 @@ def create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -568,7 +576,7 @@ def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_private_link_resources_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_private_link_resources_operations.py
index 330afa1b89407..2eadc84452b7b 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_private_link_resources_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_private_link_resources_operations.py
@@ -45,7 +45,9 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -118,7 +120,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.PrivateLinkResourceListResult] = kwargs.pop("cls", None)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_quotas_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_quotas_operations.py
index 926e7f8d9baa3..8b8a0b613f169 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_quotas_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_quotas_operations.py
@@ -45,7 +45,9 @@ def build_update_request(location: str, subscription_id: str, **kwargs: Any) ->
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -76,7 +78,9 @@ def build_list_request(location: str, subscription_id: str, **kwargs: Any) -> Ht
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -191,7 +195,7 @@ def update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -255,7 +259,7 @@ def list(self, location: str, **kwargs: Any) -> Iterable["_models.ResourceQuota"
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ListWorkspaceQuotas] = kwargs.pop("cls", None)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_registries_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_registries_operations.py
new file mode 100644
index 0000000000000..d0d7931327218
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_registries_operations.py
@@ -0,0 +1,1122 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+import sys
+from typing import Any, Callable, Dict, IO, Iterable, Optional, TypeVar, Union, cast, overload
+import urllib.parse
+
+from azure.core.exceptions import (
+    ClientAuthenticationError,
+    HttpResponseError,
+    ResourceExistsError,
+    ResourceNotFoundError,
+    ResourceNotModifiedError,
+    map_error,
+)
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.polling import LROPoller, NoPolling, PollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.utils import case_insensitive_dict
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.arm_polling import ARMPolling
+
+from .. import models as _models
+from .._serialization import Serializer
+from .._vendor import _convert_request, _format_url_section
+
+if sys.version_info >= (3, 8):
+    from typing import Literal  # pylint: disable=no-name-in-module, ungrouped-imports
+else:
+    from typing_extensions import Literal  # type: ignore  # pylint: disable=ungrouped-imports
+T = TypeVar("T")
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+
+
+def build_list_by_subscription_request(
+    subscription_id: str, *, skip: Optional[str] = None, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url", "/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearningServices/registries"
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+    if skip is not None:
+        _params["$skip"] = _SERIALIZER.query("skip", skip, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_list_request(
+    resource_group_name: str, subscription_id: str, *, skip: Optional[str] = None, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+    if skip is not None:
+        _params["$skip"] = _SERIALIZER.query("skip", skip, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_delete_request(
+    resource_group_name: str, registry_name: str, subscription_id: str, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url(
+            "registry_name", registry_name, "str", pattern=r"^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$"
+        ),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="DELETE", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_get_request(resource_group_name: str, registry_name: str, subscription_id: str, **kwargs: Any) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url("registry_name", registry_name, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_update_request(
+    resource_group_name: str, registry_name: str, subscription_id: str, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url(
+            "registry_name", registry_name, "str", pattern=r"^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$"
+        ),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    if content_type is not None:
+        _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str")
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="PATCH", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_create_or_update_request(
+    resource_group_name: str, registry_name: str, subscription_id: str, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url(
+            "registry_name", registry_name, "str", pattern=r"^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$"
+        ),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    if content_type is not None:
+        _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str")
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="PUT", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+class RegistriesOperations:
+    """
+    .. warning::
+        **DO NOT** instantiate this class directly.
+
+        Instead, you should access the following operations through
+        :class:`~azure.mgmt.machinelearningservices.MachineLearningServicesMgmtClient`'s
+        :attr:`registries` attribute.
+    """
+
+    models = _models
+
+    def __init__(self, *args, **kwargs):
+        input_args = list(args)
+        self._client = input_args.pop(0) if input_args else kwargs.pop("client")
+        self._config = input_args.pop(0) if input_args else kwargs.pop("config")
+        self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer")
+        self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer")
+
+    @distributed_trace
+    def list_by_subscription(self, skip: Optional[str] = None, **kwargs: Any) -> Iterable["_models.Registry"]:
+        """List registries by subscription.
+
+        List registries by subscription.
+
+        :param skip: Continuation token for pagination. Default value is None.
+        :type skip: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either Registry or the result of cls(response)
+        :rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.Registry]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.RegistryTrackedResourceArmPaginatedResult] = kwargs.pop("cls", None)
+
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        def prepare_request(next_link=None):
+            if not next_link:
+
+                request = build_list_by_subscription_request(
+                    subscription_id=self._config.subscription_id,
+                    skip=skip,
+                    api_version=api_version,
+                    template_url=self.list_by_subscription.metadata["url"],
+                    headers=_headers,
+                    params=_params,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                # make call to next link with the client's api-version
+                _parsed_next_link = urllib.parse.urlparse(next_link)
+                _next_request_params = case_insensitive_dict(
+                    {
+                        key: [urllib.parse.quote(v) for v in value]
+                        for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items()
+                    }
+                )
+                _next_request_params["api-version"] = self._config.api_version
+                request = HttpRequest(
+                    "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("RegistryTrackedResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)  # type: ignore
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+                request, stream=False, **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+        return ItemPaged(get_next, extract_data)
+
+    list_by_subscription.metadata = {
+        "url": "/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearningServices/registries"
+    }
+
+    @distributed_trace
+    def list(self, resource_group_name: str, skip: Optional[str] = None, **kwargs: Any) -> Iterable["_models.Registry"]:
+        """List registries.
+
+        List registries.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param skip: Continuation token for pagination. Default value is None.
+        :type skip: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either Registry or the result of cls(response)
+        :rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.Registry]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.RegistryTrackedResourceArmPaginatedResult] = kwargs.pop("cls", None)
+
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        def prepare_request(next_link=None):
+            if not next_link:
+
+                request = build_list_request(
+                    resource_group_name=resource_group_name,
+                    subscription_id=self._config.subscription_id,
+                    skip=skip,
+                    api_version=api_version,
+                    template_url=self.list.metadata["url"],
+                    headers=_headers,
+                    params=_params,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                # make call to next link with the client's api-version
+                _parsed_next_link = urllib.parse.urlparse(next_link)
+                _next_request_params = case_insensitive_dict(
+                    {
+                        key: [urllib.parse.quote(v) for v in value]
+                        for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items()
+                    }
+                )
+                _next_request_params["api-version"] = self._config.api_version
+                request = HttpRequest(
+                    "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("RegistryTrackedResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)  # type: ignore
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+                request, stream=False, **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+        return ItemPaged(get_next, extract_data)
+
+    list.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries"
+    }
+
+    def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self, resource_group_name: str, registry_name: str, **kwargs: Any
+    ) -> None:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+
+        request = build_delete_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self._delete_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Location"] = self._deserialize("str", response.headers.get("Location"))
+            response_headers["Retry-After"] = self._deserialize("int", response.headers.get("Retry-After"))
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _delete_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}"
+    }
+
+    @distributed_trace
+    def begin_delete(self, resource_group_name: str, registry_name: str, **kwargs: Any) -> LROPoller[None]:
+        """Delete registry.
+
+        Delete registry.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of registry. This is case-insensitive. Required.
+        :type registry_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[None]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+        polling: Union[bool, PollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = self._delete_initial(  # type: ignore
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                api_version=api_version,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):  # pylint: disable=inconsistent-return-statements
+            if cls:
+                return cls(pipeline_response, None, {})
+
+        if polling is True:
+            polling_method: PollingMethod = cast(
+                PollingMethod, ARMPolling(lro_delay, lro_options={"final-state-via": "location"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(PollingMethod, NoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_delete.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}"
+    }
+
+    @distributed_trace
+    def get(self, resource_group_name: str, registry_name: str, **kwargs: Any) -> _models.Registry:
+        """Get registry.
+
+        Get registry.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of registry. This is case-insensitive. Required.
+        :type registry_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: Registry or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.Registry
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.Registry] = kwargs.pop("cls", None)
+
+        request = build_get_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self.get.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize("Registry", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}"
+    }
+
+    def _update_initial(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        body: Union[_models.PartialRegistryPartialTrackedResource, IO],
+        **kwargs: Any
+    ) -> _models.Registry:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.Registry] = kwargs.pop("cls", None)
+
+        content_type = content_type or "application/json"
+        _json = None
+        _content = None
+        if isinstance(body, (IO, bytes)):
+            _content = body
+        else:
+            _json = self._serialize.body(body, "PartialRegistryPartialTrackedResource")
+
+        request = build_update_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            content=_content,
+            template_url=self._update_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize("Registry", pipeline_response)
+
+        if response.status_code == 202:
+            response_headers["Location"] = self._deserialize("str", response.headers.get("Location"))
+            response_headers["Retry-After"] = self._deserialize("int", response.headers.get("Retry-After"))
+
+            deserialized = self._deserialize("Registry", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)  # type: ignore
+
+        return deserialized  # type: ignore
+
+    _update_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}"
+    }
+
+    @overload
+    def begin_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        body: _models.PartialRegistryPartialTrackedResource,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> LROPoller[_models.Registry]:
+        """Update tags.
+
+        Update tags.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of registry. This is case-insensitive. Required.
+        :type registry_name: str
+        :param body: Details required to create the registry. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.PartialRegistryPartialTrackedResource
+        :keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either Registry or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.Registry]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @overload
+    def begin_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        body: IO,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> LROPoller[_models.Registry]:
+        """Update tags.
+
+        Update tags.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of registry. This is case-insensitive. Required.
+        :type registry_name: str
+        :param body: Details required to create the registry. Required.
+        :type body: IO
+        :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either Registry or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.Registry]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @distributed_trace
+    def begin_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        body: Union[_models.PartialRegistryPartialTrackedResource, IO],
+        **kwargs: Any
+    ) -> LROPoller[_models.Registry]:
+        """Update tags.
+
+        Update tags.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of registry. This is case-insensitive. Required.
+        :type registry_name: str
+        :param body: Details required to create the registry. Is either a model type or a IO type.
+         Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.PartialRegistryPartialTrackedResource or
+         IO
+        :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
+         Default value is None.
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either Registry or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.Registry]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.Registry] = kwargs.pop("cls", None)
+        polling: Union[bool, PollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = self._update_initial(
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):
+            deserialized = self._deserialize("Registry", pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+        if polling is True:
+            polling_method: PollingMethod = cast(
+                PollingMethod, ARMPolling(lro_delay, lro_options={"final-state-via": "location"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(PollingMethod, NoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_update.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}"
+    }
+
+    def _create_or_update_initial(
+        self, resource_group_name: str, registry_name: str, body: Union[_models.Registry, IO], **kwargs: Any
+    ) -> Optional[_models.Registry]:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[Optional[_models.Registry]] = kwargs.pop("cls", None)
+
+        content_type = content_type or "application/json"
+        _json = None
+        _content = None
+        if isinstance(body, (IO, bytes)):
+            _content = body
+        else:
+            _json = self._serialize.body(body, "Registry")
+
+        request = build_create_or_update_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            content=_content,
+            template_url=self._create_or_update_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201, 202]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = None
+        if response.status_code == 200:
+            deserialized = self._deserialize("Registry", pipeline_response)
+
+        if response.status_code == 201:
+            deserialized = self._deserialize("Registry", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    _create_or_update_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}"
+    }
+
+    @overload
+    def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        body: _models.Registry,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> LROPoller[_models.Registry]:
+        """Create or update registry.
+
+        Create or update registry.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of registry. This is case-insensitive. Required.
+        :type registry_name: str
+        :param body: Details required to create the registry. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.Registry
+        :keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either Registry or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.Registry]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @overload
+    def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        body: IO,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> LROPoller[_models.Registry]:
+        """Create or update registry.
+
+        Create or update registry.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of registry. This is case-insensitive. Required.
+        :type registry_name: str
+        :param body: Details required to create the registry. Required.
+        :type body: IO
+        :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either Registry or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.Registry]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @distributed_trace
+    def begin_create_or_update(
+        self, resource_group_name: str, registry_name: str, body: Union[_models.Registry, IO], **kwargs: Any
+    ) -> LROPoller[_models.Registry]:
+        """Create or update registry.
+
+        Create or update registry.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of registry. This is case-insensitive. Required.
+        :type registry_name: str
+        :param body: Details required to create the registry. Is either a model type or a IO type.
+         Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.Registry or IO
+        :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
+         Default value is None.
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either Registry or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.Registry]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.Registry] = kwargs.pop("cls", None)
+        polling: Union[bool, PollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):
+            deserialized = self._deserialize("Registry", pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+        if polling is True:
+            polling_method: PollingMethod = cast(
+                PollingMethod, ARMPolling(lro_delay, lro_options={"final-state-via": "azure-async-operation"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(PollingMethod, NoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_create_or_update.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}"
+    }
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_registry_code_containers_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_registry_code_containers_operations.py
new file mode 100644
index 0000000000000..ace30d21cd2bc
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_registry_code_containers_operations.py
@@ -0,0 +1,768 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+import sys
+from typing import Any, Callable, Dict, IO, Iterable, Optional, TypeVar, Union, cast, overload
+import urllib.parse
+
+from azure.core.exceptions import (
+    ClientAuthenticationError,
+    HttpResponseError,
+    ResourceExistsError,
+    ResourceNotFoundError,
+    ResourceNotModifiedError,
+    map_error,
+)
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.polling import LROPoller, NoPolling, PollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.utils import case_insensitive_dict
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.arm_polling import ARMPolling
+
+from .. import models as _models
+from .._serialization import Serializer
+from .._vendor import _convert_request, _format_url_section
+
+if sys.version_info >= (3, 8):
+    from typing import Literal  # pylint: disable=no-name-in-module, ungrouped-imports
+else:
+    from typing_extensions import Literal  # type: ignore  # pylint: disable=ungrouped-imports
+T = TypeVar("T")
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+
+
+def build_list_request(
+    resource_group_name: str, registry_name: str, subscription_id: str, *, skip: Optional[str] = None, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/codes",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url("registry_name", registry_name, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+    if skip is not None:
+        _params["$skip"] = _SERIALIZER.query("skip", skip, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_delete_request(
+    resource_group_name: str, registry_name: str, code_name: str, subscription_id: str, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/codes/{codeName}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url("registry_name", registry_name, "str"),
+        "codeName": _SERIALIZER.url("code_name", code_name, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="DELETE", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_get_request(
+    resource_group_name: str, registry_name: str, code_name: str, subscription_id: str, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/codes/{codeName}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url("registry_name", registry_name, "str"),
+        "codeName": _SERIALIZER.url("code_name", code_name, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_create_or_update_request(
+    resource_group_name: str, registry_name: str, code_name: str, subscription_id: str, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/codes/{codeName}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url("registry_name", registry_name, "str"),
+        "codeName": _SERIALIZER.url("code_name", code_name, "str", pattern=r"^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    if content_type is not None:
+        _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str")
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="PUT", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+class RegistryCodeContainersOperations:
+    """
+    .. warning::
+        **DO NOT** instantiate this class directly.
+
+        Instead, you should access the following operations through
+        :class:`~azure.mgmt.machinelearningservices.MachineLearningServicesMgmtClient`'s
+        :attr:`registry_code_containers` attribute.
+    """
+
+    models = _models
+
+    def __init__(self, *args, **kwargs):
+        input_args = list(args)
+        self._client = input_args.pop(0) if input_args else kwargs.pop("client")
+        self._config = input_args.pop(0) if input_args else kwargs.pop("config")
+        self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer")
+        self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer")
+
+    @distributed_trace
+    def list(
+        self, resource_group_name: str, registry_name: str, skip: Optional[str] = None, **kwargs: Any
+    ) -> Iterable["_models.CodeContainer"]:
+        """List containers.
+
+        List containers.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param skip: Continuation token for pagination. Default value is None.
+        :type skip: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either CodeContainer or the result of cls(response)
+        :rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.CodeContainer]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.CodeContainerResourceArmPaginatedResult] = kwargs.pop("cls", None)
+
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        def prepare_request(next_link=None):
+            if not next_link:
+
+                request = build_list_request(
+                    resource_group_name=resource_group_name,
+                    registry_name=registry_name,
+                    subscription_id=self._config.subscription_id,
+                    skip=skip,
+                    api_version=api_version,
+                    template_url=self.list.metadata["url"],
+                    headers=_headers,
+                    params=_params,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                # make call to next link with the client's api-version
+                _parsed_next_link = urllib.parse.urlparse(next_link)
+                _next_request_params = case_insensitive_dict(
+                    {
+                        key: [urllib.parse.quote(v) for v in value]
+                        for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items()
+                    }
+                )
+                _next_request_params["api-version"] = self._config.api_version
+                request = HttpRequest(
+                    "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("CodeContainerResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)  # type: ignore
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+                request, stream=False, **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+        return ItemPaged(get_next, extract_data)
+
+    list.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/codes"
+    }
+
+    def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self, resource_group_name: str, registry_name: str, code_name: str, **kwargs: Any
+    ) -> None:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+
+        request = build_delete_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            code_name=code_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self._delete_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Location"] = self._deserialize("str", response.headers.get("Location"))
+            response_headers["Retry-After"] = self._deserialize("int", response.headers.get("Retry-After"))
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _delete_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/codes/{codeName}"
+    }
+
+    @distributed_trace
+    def begin_delete(
+        self, resource_group_name: str, registry_name: str, code_name: str, **kwargs: Any
+    ) -> LROPoller[None]:
+        """Delete container.
+
+        Delete container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param code_name: Container name. Required.
+        :type code_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[None]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+        polling: Union[bool, PollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = self._delete_initial(  # type: ignore
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                code_name=code_name,
+                api_version=api_version,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):  # pylint: disable=inconsistent-return-statements
+            if cls:
+                return cls(pipeline_response, None, {})
+
+        if polling is True:
+            polling_method: PollingMethod = cast(
+                PollingMethod, ARMPolling(lro_delay, lro_options={"final-state-via": "location"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(PollingMethod, NoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_delete.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/codes/{codeName}"
+    }
+
+    @distributed_trace
+    def get(self, resource_group_name: str, registry_name: str, code_name: str, **kwargs: Any) -> _models.CodeContainer:
+        """Get container.
+
+        Get container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param code_name: Container name. Required.
+        :type code_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: CodeContainer or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.CodeContainer
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.CodeContainer] = kwargs.pop("cls", None)
+
+        request = build_get_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            code_name=code_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self.get.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize("CodeContainer", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/codes/{codeName}"
+    }
+
+    def _create_or_update_initial(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        code_name: str,
+        body: Union[_models.CodeContainer, IO],
+        **kwargs: Any
+    ) -> _models.CodeContainer:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.CodeContainer] = kwargs.pop("cls", None)
+
+        content_type = content_type or "application/json"
+        _json = None
+        _content = None
+        if isinstance(body, (IO, bytes)):
+            _content = body
+        else:
+            _json = self._serialize.body(body, "CodeContainer")
+
+        request = build_create_or_update_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            code_name=code_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            content=_content,
+            template_url=self._create_or_update_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize("CodeContainer", pipeline_response)
+
+        if response.status_code == 201:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Azure-AsyncOperation"] = self._deserialize(
+                "str", response.headers.get("Azure-AsyncOperation")
+            )
+
+            deserialized = self._deserialize("CodeContainer", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)  # type: ignore
+
+        return deserialized  # type: ignore
+
+    _create_or_update_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/codes/{codeName}"
+    }
+
+    @overload
+    def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        code_name: str,
+        body: _models.CodeContainer,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> LROPoller[_models.CodeContainer]:
+        """Create or update container.
+
+        Create or update container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param code_name: Container name. Required.
+        :type code_name: str
+        :param body: Container entity to create or update. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.CodeContainer
+        :keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either CodeContainer or the result of
+         cls(response)
+        :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.CodeContainer]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @overload
+    def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        code_name: str,
+        body: IO,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> LROPoller[_models.CodeContainer]:
+        """Create or update container.
+
+        Create or update container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param code_name: Container name. Required.
+        :type code_name: str
+        :param body: Container entity to create or update. Required.
+        :type body: IO
+        :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either CodeContainer or the result of
+         cls(response)
+        :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.CodeContainer]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @distributed_trace
+    def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        code_name: str,
+        body: Union[_models.CodeContainer, IO],
+        **kwargs: Any
+    ) -> LROPoller[_models.CodeContainer]:
+        """Create or update container.
+
+        Create or update container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param code_name: Container name. Required.
+        :type code_name: str
+        :param body: Container entity to create or update. Is either a model type or a IO type.
+         Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.CodeContainer or IO
+        :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
+         Default value is None.
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either CodeContainer or the result of
+         cls(response)
+        :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.CodeContainer]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.CodeContainer] = kwargs.pop("cls", None)
+        polling: Union[bool, PollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                code_name=code_name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):
+            deserialized = self._deserialize("CodeContainer", pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+        if polling is True:
+            polling_method: PollingMethod = cast(
+                PollingMethod, ARMPolling(lro_delay, lro_options={"final-state-via": "original-uri"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(PollingMethod, NoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_create_or_update.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/codes/{codeName}"
+    }
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_registry_code_versions_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_registry_code_versions_operations.py
new file mode 100644
index 0000000000000..cc0d2cf6c3348
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_registry_code_versions_operations.py
@@ -0,0 +1,820 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+import sys
+from typing import Any, Callable, Dict, IO, Iterable, Optional, TypeVar, Union, cast, overload
+import urllib.parse
+
+from azure.core.exceptions import (
+    ClientAuthenticationError,
+    HttpResponseError,
+    ResourceExistsError,
+    ResourceNotFoundError,
+    ResourceNotModifiedError,
+    map_error,
+)
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.polling import LROPoller, NoPolling, PollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.utils import case_insensitive_dict
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.arm_polling import ARMPolling
+
+from .. import models as _models
+from .._serialization import Serializer
+from .._vendor import _convert_request, _format_url_section
+
+if sys.version_info >= (3, 8):
+    from typing import Literal  # pylint: disable=no-name-in-module, ungrouped-imports
+else:
+    from typing_extensions import Literal  # type: ignore  # pylint: disable=ungrouped-imports
+T = TypeVar("T")
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+
+
+def build_list_request(
+    resource_group_name: str,
+    registry_name: str,
+    code_name: str,
+    subscription_id: str,
+    *,
+    order_by: Optional[str] = None,
+    top: Optional[int] = None,
+    skip: Optional[str] = None,
+    **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/codes/{codeName}/versions",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url("registry_name", registry_name, "str"),
+        "codeName": _SERIALIZER.url("code_name", code_name, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+    if order_by is not None:
+        _params["$orderBy"] = _SERIALIZER.query("order_by", order_by, "str")
+    if top is not None:
+        _params["$top"] = _SERIALIZER.query("top", top, "int")
+    if skip is not None:
+        _params["$skip"] = _SERIALIZER.query("skip", skip, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_delete_request(
+    resource_group_name: str, registry_name: str, code_name: str, version: str, subscription_id: str, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/codes/{codeName}/versions/{version}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url("registry_name", registry_name, "str"),
+        "codeName": _SERIALIZER.url("code_name", code_name, "str"),
+        "version": _SERIALIZER.url("version", version, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="DELETE", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_get_request(
+    resource_group_name: str, registry_name: str, code_name: str, version: str, subscription_id: str, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/codes/{codeName}/versions/{version}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url("registry_name", registry_name, "str"),
+        "codeName": _SERIALIZER.url("code_name", code_name, "str"),
+        "version": _SERIALIZER.url("version", version, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_create_or_update_request(
+    resource_group_name: str, registry_name: str, code_name: str, version: str, subscription_id: str, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/codes/{codeName}/versions/{version}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url("registry_name", registry_name, "str"),
+        "codeName": _SERIALIZER.url("code_name", code_name, "str", pattern=r"^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$"),
+        "version": _SERIALIZER.url("version", version, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    if content_type is not None:
+        _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str")
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="PUT", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+class RegistryCodeVersionsOperations:
+    """
+    .. warning::
+        **DO NOT** instantiate this class directly.
+
+        Instead, you should access the following operations through
+        :class:`~azure.mgmt.machinelearningservices.MachineLearningServicesMgmtClient`'s
+        :attr:`registry_code_versions` attribute.
+    """
+
+    models = _models
+
+    def __init__(self, *args, **kwargs):
+        input_args = list(args)
+        self._client = input_args.pop(0) if input_args else kwargs.pop("client")
+        self._config = input_args.pop(0) if input_args else kwargs.pop("config")
+        self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer")
+        self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer")
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        code_name: str,
+        order_by: Optional[str] = None,
+        top: Optional[int] = None,
+        skip: Optional[str] = None,
+        **kwargs: Any
+    ) -> Iterable["_models.CodeVersion"]:
+        """List versions.
+
+        List versions.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param code_name: Container name. Required.
+        :type code_name: str
+        :param order_by: Ordering of list. Default value is None.
+        :type order_by: str
+        :param top: Maximum number of records to return. Default value is None.
+        :type top: int
+        :param skip: Continuation token for pagination. Default value is None.
+        :type skip: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either CodeVersion or the result of cls(response)
+        :rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.CodeVersion]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.CodeVersionResourceArmPaginatedResult] = kwargs.pop("cls", None)
+
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        def prepare_request(next_link=None):
+            if not next_link:
+
+                request = build_list_request(
+                    resource_group_name=resource_group_name,
+                    registry_name=registry_name,
+                    code_name=code_name,
+                    subscription_id=self._config.subscription_id,
+                    order_by=order_by,
+                    top=top,
+                    skip=skip,
+                    api_version=api_version,
+                    template_url=self.list.metadata["url"],
+                    headers=_headers,
+                    params=_params,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                # make call to next link with the client's api-version
+                _parsed_next_link = urllib.parse.urlparse(next_link)
+                _next_request_params = case_insensitive_dict(
+                    {
+                        key: [urllib.parse.quote(v) for v in value]
+                        for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items()
+                    }
+                )
+                _next_request_params["api-version"] = self._config.api_version
+                request = HttpRequest(
+                    "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("CodeVersionResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)  # type: ignore
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+                request, stream=False, **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+        return ItemPaged(get_next, extract_data)
+
+    list.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/codes/{codeName}/versions"
+    }
+
+    def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self, resource_group_name: str, registry_name: str, code_name: str, version: str, **kwargs: Any
+    ) -> None:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+
+        request = build_delete_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            code_name=code_name,
+            version=version,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self._delete_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Location"] = self._deserialize("str", response.headers.get("Location"))
+            response_headers["Retry-After"] = self._deserialize("int", response.headers.get("Retry-After"))
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _delete_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/codes/{codeName}/versions/{version}"
+    }
+
+    @distributed_trace
+    def begin_delete(
+        self, resource_group_name: str, registry_name: str, code_name: str, version: str, **kwargs: Any
+    ) -> LROPoller[None]:
+        """Delete version.
+
+        Delete version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param code_name: Container name. Required.
+        :type code_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[None]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+        polling: Union[bool, PollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = self._delete_initial(  # type: ignore
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                code_name=code_name,
+                version=version,
+                api_version=api_version,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):  # pylint: disable=inconsistent-return-statements
+            if cls:
+                return cls(pipeline_response, None, {})
+
+        if polling is True:
+            polling_method: PollingMethod = cast(
+                PollingMethod, ARMPolling(lro_delay, lro_options={"final-state-via": "location"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(PollingMethod, NoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_delete.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/codes/{codeName}/versions/{version}"
+    }
+
+    @distributed_trace
+    def get(
+        self, resource_group_name: str, registry_name: str, code_name: str, version: str, **kwargs: Any
+    ) -> _models.CodeVersion:
+        """Get version.
+
+        Get version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param code_name: Container name. Required.
+        :type code_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: CodeVersion or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.CodeVersion
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.CodeVersion] = kwargs.pop("cls", None)
+
+        request = build_get_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            code_name=code_name,
+            version=version,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self.get.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize("CodeVersion", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/codes/{codeName}/versions/{version}"
+    }
+
+    def _create_or_update_initial(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        code_name: str,
+        version: str,
+        body: Union[_models.CodeVersion, IO],
+        **kwargs: Any
+    ) -> _models.CodeVersion:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.CodeVersion] = kwargs.pop("cls", None)
+
+        content_type = content_type or "application/json"
+        _json = None
+        _content = None
+        if isinstance(body, (IO, bytes)):
+            _content = body
+        else:
+            _json = self._serialize.body(body, "CodeVersion")
+
+        request = build_create_or_update_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            code_name=code_name,
+            version=version,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            content=_content,
+            template_url=self._create_or_update_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize("CodeVersion", pipeline_response)
+
+        if response.status_code == 201:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Azure-AsyncOperation"] = self._deserialize(
+                "str", response.headers.get("Azure-AsyncOperation")
+            )
+
+            deserialized = self._deserialize("CodeVersion", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)  # type: ignore
+
+        return deserialized  # type: ignore
+
+    _create_or_update_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/codes/{codeName}/versions/{version}"
+    }
+
+    @overload
+    def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        code_name: str,
+        version: str,
+        body: _models.CodeVersion,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> LROPoller[_models.CodeVersion]:
+        """Create or update version.
+
+        Create or update version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param code_name: Container name. Required.
+        :type code_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :param body: Version entity to create or update. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.CodeVersion
+        :keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either CodeVersion or the result of
+         cls(response)
+        :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.CodeVersion]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @overload
+    def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        code_name: str,
+        version: str,
+        body: IO,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> LROPoller[_models.CodeVersion]:
+        """Create or update version.
+
+        Create or update version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param code_name: Container name. Required.
+        :type code_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :param body: Version entity to create or update. Required.
+        :type body: IO
+        :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either CodeVersion or the result of
+         cls(response)
+        :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.CodeVersion]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @distributed_trace
+    def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        code_name: str,
+        version: str,
+        body: Union[_models.CodeVersion, IO],
+        **kwargs: Any
+    ) -> LROPoller[_models.CodeVersion]:
+        """Create or update version.
+
+        Create or update version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param code_name: Container name. Required.
+        :type code_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :param body: Version entity to create or update. Is either a model type or a IO type. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.CodeVersion or IO
+        :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
+         Default value is None.
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either CodeVersion or the result of
+         cls(response)
+        :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.CodeVersion]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.CodeVersion] = kwargs.pop("cls", None)
+        polling: Union[bool, PollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                code_name=code_name,
+                version=version,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):
+            deserialized = self._deserialize("CodeVersion", pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+        if polling is True:
+            polling_method: PollingMethod = cast(
+                PollingMethod, ARMPolling(lro_delay, lro_options={"final-state-via": "original-uri"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(PollingMethod, NoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_create_or_update.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/codes/{codeName}/versions/{version}"
+    }
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_registry_component_containers_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_registry_component_containers_operations.py
new file mode 100644
index 0000000000000..8a94810396865
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_registry_component_containers_operations.py
@@ -0,0 +1,776 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+import sys
+from typing import Any, Callable, Dict, IO, Iterable, Optional, TypeVar, Union, cast, overload
+import urllib.parse
+
+from azure.core.exceptions import (
+    ClientAuthenticationError,
+    HttpResponseError,
+    ResourceExistsError,
+    ResourceNotFoundError,
+    ResourceNotModifiedError,
+    map_error,
+)
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.polling import LROPoller, NoPolling, PollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.utils import case_insensitive_dict
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.arm_polling import ARMPolling
+
+from .. import models as _models
+from .._serialization import Serializer
+from .._vendor import _convert_request, _format_url_section
+
+if sys.version_info >= (3, 8):
+    from typing import Literal  # pylint: disable=no-name-in-module, ungrouped-imports
+else:
+    from typing_extensions import Literal  # type: ignore  # pylint: disable=ungrouped-imports
+T = TypeVar("T")
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+
+
+def build_list_request(
+    resource_group_name: str, registry_name: str, subscription_id: str, *, skip: Optional[str] = None, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/components",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url("registry_name", registry_name, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+    if skip is not None:
+        _params["$skip"] = _SERIALIZER.query("skip", skip, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_delete_request(
+    resource_group_name: str, registry_name: str, component_name: str, subscription_id: str, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/components/{componentName}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url("registry_name", registry_name, "str"),
+        "componentName": _SERIALIZER.url("component_name", component_name, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="DELETE", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_get_request(
+    resource_group_name: str, registry_name: str, component_name: str, subscription_id: str, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/components/{componentName}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url("registry_name", registry_name, "str"),
+        "componentName": _SERIALIZER.url("component_name", component_name, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_create_or_update_request(
+    resource_group_name: str, registry_name: str, component_name: str, subscription_id: str, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/components/{componentName}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url("registry_name", registry_name, "str"),
+        "componentName": _SERIALIZER.url(
+            "component_name", component_name, "str", pattern=r"^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$"
+        ),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    if content_type is not None:
+        _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str")
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="PUT", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+class RegistryComponentContainersOperations:
+    """
+    .. warning::
+        **DO NOT** instantiate this class directly.
+
+        Instead, you should access the following operations through
+        :class:`~azure.mgmt.machinelearningservices.MachineLearningServicesMgmtClient`'s
+        :attr:`registry_component_containers` attribute.
+    """
+
+    models = _models
+
+    def __init__(self, *args, **kwargs):
+        input_args = list(args)
+        self._client = input_args.pop(0) if input_args else kwargs.pop("client")
+        self._config = input_args.pop(0) if input_args else kwargs.pop("config")
+        self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer")
+        self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer")
+
+    @distributed_trace
+    def list(
+        self, resource_group_name: str, registry_name: str, skip: Optional[str] = None, **kwargs: Any
+    ) -> Iterable["_models.ComponentContainer"]:
+        """List containers.
+
+        List containers.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param skip: Continuation token for pagination. Default value is None.
+        :type skip: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either ComponentContainer or the result of cls(response)
+        :rtype:
+         ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.ComponentContainer]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.ComponentContainerResourceArmPaginatedResult] = kwargs.pop("cls", None)
+
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        def prepare_request(next_link=None):
+            if not next_link:
+
+                request = build_list_request(
+                    resource_group_name=resource_group_name,
+                    registry_name=registry_name,
+                    subscription_id=self._config.subscription_id,
+                    skip=skip,
+                    api_version=api_version,
+                    template_url=self.list.metadata["url"],
+                    headers=_headers,
+                    params=_params,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                # make call to next link with the client's api-version
+                _parsed_next_link = urllib.parse.urlparse(next_link)
+                _next_request_params = case_insensitive_dict(
+                    {
+                        key: [urllib.parse.quote(v) for v in value]
+                        for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items()
+                    }
+                )
+                _next_request_params["api-version"] = self._config.api_version
+                request = HttpRequest(
+                    "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("ComponentContainerResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)  # type: ignore
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+                request, stream=False, **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+        return ItemPaged(get_next, extract_data)
+
+    list.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/components"
+    }
+
+    def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self, resource_group_name: str, registry_name: str, component_name: str, **kwargs: Any
+    ) -> None:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+
+        request = build_delete_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            component_name=component_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self._delete_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Location"] = self._deserialize("str", response.headers.get("Location"))
+            response_headers["Retry-After"] = self._deserialize("int", response.headers.get("Retry-After"))
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _delete_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/components/{componentName}"
+    }
+
+    @distributed_trace
+    def begin_delete(
+        self, resource_group_name: str, registry_name: str, component_name: str, **kwargs: Any
+    ) -> LROPoller[None]:
+        """Delete container.
+
+        Delete container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param component_name: Container name. Required.
+        :type component_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[None]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+        polling: Union[bool, PollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = self._delete_initial(  # type: ignore
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                component_name=component_name,
+                api_version=api_version,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):  # pylint: disable=inconsistent-return-statements
+            if cls:
+                return cls(pipeline_response, None, {})
+
+        if polling is True:
+            polling_method: PollingMethod = cast(
+                PollingMethod, ARMPolling(lro_delay, lro_options={"final-state-via": "location"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(PollingMethod, NoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_delete.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/components/{componentName}"
+    }
+
+    @distributed_trace
+    def get(
+        self, resource_group_name: str, registry_name: str, component_name: str, **kwargs: Any
+    ) -> _models.ComponentContainer:
+        """Get container.
+
+        Get container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param component_name: Container name. Required.
+        :type component_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ComponentContainer or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ComponentContainer
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.ComponentContainer] = kwargs.pop("cls", None)
+
+        request = build_get_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            component_name=component_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self.get.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize("ComponentContainer", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/components/{componentName}"
+    }
+
+    def _create_or_update_initial(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        component_name: str,
+        body: Union[_models.ComponentContainer, IO],
+        **kwargs: Any
+    ) -> _models.ComponentContainer:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.ComponentContainer] = kwargs.pop("cls", None)
+
+        content_type = content_type or "application/json"
+        _json = None
+        _content = None
+        if isinstance(body, (IO, bytes)):
+            _content = body
+        else:
+            _json = self._serialize.body(body, "ComponentContainer")
+
+        request = build_create_or_update_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            component_name=component_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            content=_content,
+            template_url=self._create_or_update_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize("ComponentContainer", pipeline_response)
+
+        if response.status_code == 201:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Azure-AsyncOperation"] = self._deserialize(
+                "str", response.headers.get("Azure-AsyncOperation")
+            )
+
+            deserialized = self._deserialize("ComponentContainer", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)  # type: ignore
+
+        return deserialized  # type: ignore
+
+    _create_or_update_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/components/{componentName}"
+    }
+
+    @overload
+    def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        component_name: str,
+        body: _models.ComponentContainer,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> LROPoller[_models.ComponentContainer]:
+        """Create or update container.
+
+        Create or update container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param component_name: Container name. Required.
+        :type component_name: str
+        :param body: Container entity to create or update. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.ComponentContainer
+        :keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either ComponentContainer or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.ComponentContainer]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @overload
+    def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        component_name: str,
+        body: IO,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> LROPoller[_models.ComponentContainer]:
+        """Create or update container.
+
+        Create or update container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param component_name: Container name. Required.
+        :type component_name: str
+        :param body: Container entity to create or update. Required.
+        :type body: IO
+        :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either ComponentContainer or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.ComponentContainer]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @distributed_trace
+    def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        component_name: str,
+        body: Union[_models.ComponentContainer, IO],
+        **kwargs: Any
+    ) -> LROPoller[_models.ComponentContainer]:
+        """Create or update container.
+
+        Create or update container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param component_name: Container name. Required.
+        :type component_name: str
+        :param body: Container entity to create or update. Is either a model type or a IO type.
+         Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.ComponentContainer or IO
+        :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
+         Default value is None.
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either ComponentContainer or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.ComponentContainer]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.ComponentContainer] = kwargs.pop("cls", None)
+        polling: Union[bool, PollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                component_name=component_name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):
+            deserialized = self._deserialize("ComponentContainer", pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+        if polling is True:
+            polling_method: PollingMethod = cast(
+                PollingMethod, ARMPolling(lro_delay, lro_options={"final-state-via": "original-uri"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(PollingMethod, NoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_create_or_update.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/components/{componentName}"
+    }
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_registry_component_versions_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_registry_component_versions_operations.py
new file mode 100644
index 0000000000000..68332d69f6872
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_registry_component_versions_operations.py
@@ -0,0 +1,826 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+import sys
+from typing import Any, Callable, Dict, IO, Iterable, Optional, TypeVar, Union, cast, overload
+import urllib.parse
+
+from azure.core.exceptions import (
+    ClientAuthenticationError,
+    HttpResponseError,
+    ResourceExistsError,
+    ResourceNotFoundError,
+    ResourceNotModifiedError,
+    map_error,
+)
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.polling import LROPoller, NoPolling, PollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.utils import case_insensitive_dict
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.arm_polling import ARMPolling
+
+from .. import models as _models
+from .._serialization import Serializer
+from .._vendor import _convert_request, _format_url_section
+
+if sys.version_info >= (3, 8):
+    from typing import Literal  # pylint: disable=no-name-in-module, ungrouped-imports
+else:
+    from typing_extensions import Literal  # type: ignore  # pylint: disable=ungrouped-imports
+T = TypeVar("T")
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+
+
+def build_list_request(
+    resource_group_name: str,
+    registry_name: str,
+    component_name: str,
+    subscription_id: str,
+    *,
+    order_by: Optional[str] = None,
+    top: Optional[int] = None,
+    skip: Optional[str] = None,
+    **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/components/{componentName}/versions",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url("registry_name", registry_name, "str"),
+        "componentName": _SERIALIZER.url("component_name", component_name, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+    if order_by is not None:
+        _params["$orderBy"] = _SERIALIZER.query("order_by", order_by, "str")
+    if top is not None:
+        _params["$top"] = _SERIALIZER.query("top", top, "int")
+    if skip is not None:
+        _params["$skip"] = _SERIALIZER.query("skip", skip, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_delete_request(
+    resource_group_name: str, registry_name: str, component_name: str, version: str, subscription_id: str, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/components/{componentName}/versions/{version}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url("registry_name", registry_name, "str"),
+        "componentName": _SERIALIZER.url("component_name", component_name, "str"),
+        "version": _SERIALIZER.url("version", version, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="DELETE", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_get_request(
+    resource_group_name: str, registry_name: str, component_name: str, version: str, subscription_id: str, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/components/{componentName}/versions/{version}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url("registry_name", registry_name, "str"),
+        "componentName": _SERIALIZER.url("component_name", component_name, "str"),
+        "version": _SERIALIZER.url("version", version, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_create_or_update_request(
+    resource_group_name: str, registry_name: str, component_name: str, version: str, subscription_id: str, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/components/{componentName}/versions/{version}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url("registry_name", registry_name, "str"),
+        "componentName": _SERIALIZER.url(
+            "component_name", component_name, "str", pattern=r"^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$"
+        ),
+        "version": _SERIALIZER.url("version", version, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    if content_type is not None:
+        _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str")
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="PUT", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+class RegistryComponentVersionsOperations:
+    """
+    .. warning::
+        **DO NOT** instantiate this class directly.
+
+        Instead, you should access the following operations through
+        :class:`~azure.mgmt.machinelearningservices.MachineLearningServicesMgmtClient`'s
+        :attr:`registry_component_versions` attribute.
+    """
+
+    models = _models
+
+    def __init__(self, *args, **kwargs):
+        input_args = list(args)
+        self._client = input_args.pop(0) if input_args else kwargs.pop("client")
+        self._config = input_args.pop(0) if input_args else kwargs.pop("config")
+        self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer")
+        self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer")
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        component_name: str,
+        order_by: Optional[str] = None,
+        top: Optional[int] = None,
+        skip: Optional[str] = None,
+        **kwargs: Any
+    ) -> Iterable["_models.ComponentVersion"]:
+        """List versions.
+
+        List versions.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param component_name: Container name. Required.
+        :type component_name: str
+        :param order_by: Ordering of list. Default value is None.
+        :type order_by: str
+        :param top: Maximum number of records to return. Default value is None.
+        :type top: int
+        :param skip: Continuation token for pagination. Default value is None.
+        :type skip: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either ComponentVersion or the result of cls(response)
+        :rtype:
+         ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.ComponentVersion]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.ComponentVersionResourceArmPaginatedResult] = kwargs.pop("cls", None)
+
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        def prepare_request(next_link=None):
+            if not next_link:
+
+                request = build_list_request(
+                    resource_group_name=resource_group_name,
+                    registry_name=registry_name,
+                    component_name=component_name,
+                    subscription_id=self._config.subscription_id,
+                    order_by=order_by,
+                    top=top,
+                    skip=skip,
+                    api_version=api_version,
+                    template_url=self.list.metadata["url"],
+                    headers=_headers,
+                    params=_params,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                # make call to next link with the client's api-version
+                _parsed_next_link = urllib.parse.urlparse(next_link)
+                _next_request_params = case_insensitive_dict(
+                    {
+                        key: [urllib.parse.quote(v) for v in value]
+                        for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items()
+                    }
+                )
+                _next_request_params["api-version"] = self._config.api_version
+                request = HttpRequest(
+                    "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("ComponentVersionResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)  # type: ignore
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+                request, stream=False, **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+        return ItemPaged(get_next, extract_data)
+
+    list.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/components/{componentName}/versions"
+    }
+
+    def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self, resource_group_name: str, registry_name: str, component_name: str, version: str, **kwargs: Any
+    ) -> None:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+
+        request = build_delete_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            component_name=component_name,
+            version=version,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self._delete_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Location"] = self._deserialize("str", response.headers.get("Location"))
+            response_headers["Retry-After"] = self._deserialize("int", response.headers.get("Retry-After"))
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _delete_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/components/{componentName}/versions/{version}"
+    }
+
+    @distributed_trace
+    def begin_delete(
+        self, resource_group_name: str, registry_name: str, component_name: str, version: str, **kwargs: Any
+    ) -> LROPoller[None]:
+        """Delete version.
+
+        Delete version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param component_name: Container name. Required.
+        :type component_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[None]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+        polling: Union[bool, PollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = self._delete_initial(  # type: ignore
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                component_name=component_name,
+                version=version,
+                api_version=api_version,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):  # pylint: disable=inconsistent-return-statements
+            if cls:
+                return cls(pipeline_response, None, {})
+
+        if polling is True:
+            polling_method: PollingMethod = cast(
+                PollingMethod, ARMPolling(lro_delay, lro_options={"final-state-via": "location"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(PollingMethod, NoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_delete.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/components/{componentName}/versions/{version}"
+    }
+
+    @distributed_trace
+    def get(
+        self, resource_group_name: str, registry_name: str, component_name: str, version: str, **kwargs: Any
+    ) -> _models.ComponentVersion:
+        """Get version.
+
+        Get version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param component_name: Container name. Required.
+        :type component_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ComponentVersion or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ComponentVersion
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.ComponentVersion] = kwargs.pop("cls", None)
+
+        request = build_get_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            component_name=component_name,
+            version=version,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self.get.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize("ComponentVersion", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/components/{componentName}/versions/{version}"
+    }
+
+    def _create_or_update_initial(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        component_name: str,
+        version: str,
+        body: Union[_models.ComponentVersion, IO],
+        **kwargs: Any
+    ) -> _models.ComponentVersion:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.ComponentVersion] = kwargs.pop("cls", None)
+
+        content_type = content_type or "application/json"
+        _json = None
+        _content = None
+        if isinstance(body, (IO, bytes)):
+            _content = body
+        else:
+            _json = self._serialize.body(body, "ComponentVersion")
+
+        request = build_create_or_update_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            component_name=component_name,
+            version=version,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            content=_content,
+            template_url=self._create_or_update_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize("ComponentVersion", pipeline_response)
+
+        if response.status_code == 201:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Azure-AsyncOperation"] = self._deserialize(
+                "str", response.headers.get("Azure-AsyncOperation")
+            )
+
+            deserialized = self._deserialize("ComponentVersion", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)  # type: ignore
+
+        return deserialized  # type: ignore
+
+    _create_or_update_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/components/{componentName}/versions/{version}"
+    }
+
+    @overload
+    def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        component_name: str,
+        version: str,
+        body: _models.ComponentVersion,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> LROPoller[_models.ComponentVersion]:
+        """Create or update version.
+
+        Create or update version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param component_name: Container name. Required.
+        :type component_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :param body: Version entity to create or update. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.ComponentVersion
+        :keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either ComponentVersion or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.ComponentVersion]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @overload
+    def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        component_name: str,
+        version: str,
+        body: IO,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> LROPoller[_models.ComponentVersion]:
+        """Create or update version.
+
+        Create or update version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param component_name: Container name. Required.
+        :type component_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :param body: Version entity to create or update. Required.
+        :type body: IO
+        :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either ComponentVersion or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.ComponentVersion]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @distributed_trace
+    def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        component_name: str,
+        version: str,
+        body: Union[_models.ComponentVersion, IO],
+        **kwargs: Any
+    ) -> LROPoller[_models.ComponentVersion]:
+        """Create or update version.
+
+        Create or update version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param component_name: Container name. Required.
+        :type component_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :param body: Version entity to create or update. Is either a model type or a IO type. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.ComponentVersion or IO
+        :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
+         Default value is None.
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either ComponentVersion or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.ComponentVersion]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.ComponentVersion] = kwargs.pop("cls", None)
+        polling: Union[bool, PollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                component_name=component_name,
+                version=version,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):
+            deserialized = self._deserialize("ComponentVersion", pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+        if polling is True:
+            polling_method: PollingMethod = cast(
+                PollingMethod, ARMPolling(lro_delay, lro_options={"final-state-via": "original-uri"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(PollingMethod, NoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_create_or_update.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/components/{componentName}/versions/{version}"
+    }
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_registry_environment_containers_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_registry_environment_containers_operations.py
new file mode 100644
index 0000000000000..5f5f09b9d3f9f
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_registry_environment_containers_operations.py
@@ -0,0 +1,794 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+import sys
+from typing import Any, Callable, Dict, IO, Iterable, Optional, TypeVar, Union, cast, overload
+import urllib.parse
+
+from azure.core.exceptions import (
+    ClientAuthenticationError,
+    HttpResponseError,
+    ResourceExistsError,
+    ResourceNotFoundError,
+    ResourceNotModifiedError,
+    map_error,
+)
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.polling import LROPoller, NoPolling, PollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.utils import case_insensitive_dict
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.arm_polling import ARMPolling
+
+from .. import models as _models
+from .._serialization import Serializer
+from .._vendor import _convert_request, _format_url_section
+
+if sys.version_info >= (3, 8):
+    from typing import Literal  # pylint: disable=no-name-in-module, ungrouped-imports
+else:
+    from typing_extensions import Literal  # type: ignore  # pylint: disable=ungrouped-imports
+T = TypeVar("T")
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+
+
+def build_list_request(
+    resource_group_name: str,
+    registry_name: str,
+    subscription_id: str,
+    *,
+    skip: Optional[str] = None,
+    list_view_type: Optional[Union[str, _models.ListViewType]] = None,
+    **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/environments",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url("registry_name", registry_name, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+    if skip is not None:
+        _params["$skip"] = _SERIALIZER.query("skip", skip, "str")
+    if list_view_type is not None:
+        _params["listViewType"] = _SERIALIZER.query("list_view_type", list_view_type, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_delete_request(
+    resource_group_name: str, registry_name: str, environment_name: str, subscription_id: str, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/environments/{environmentName}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url("registry_name", registry_name, "str"),
+        "environmentName": _SERIALIZER.url("environment_name", environment_name, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="DELETE", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_get_request(
+    resource_group_name: str, registry_name: str, environment_name: str, subscription_id: str, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/environments/{environmentName}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url("registry_name", registry_name, "str"),
+        "environmentName": _SERIALIZER.url("environment_name", environment_name, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_create_or_update_request(
+    resource_group_name: str, registry_name: str, environment_name: str, subscription_id: str, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/environments/{environmentName}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url("registry_name", registry_name, "str"),
+        "environmentName": _SERIALIZER.url(
+            "environment_name", environment_name, "str", pattern=r"^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$"
+        ),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    if content_type is not None:
+        _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str")
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="PUT", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+class RegistryEnvironmentContainersOperations:
+    """
+    .. warning::
+        **DO NOT** instantiate this class directly.
+
+        Instead, you should access the following operations through
+        :class:`~azure.mgmt.machinelearningservices.MachineLearningServicesMgmtClient`'s
+        :attr:`registry_environment_containers` attribute.
+    """
+
+    models = _models
+
+    def __init__(self, *args, **kwargs):
+        input_args = list(args)
+        self._client = input_args.pop(0) if input_args else kwargs.pop("client")
+        self._config = input_args.pop(0) if input_args else kwargs.pop("config")
+        self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer")
+        self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer")
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        skip: Optional[str] = None,
+        list_view_type: Optional[Union[str, _models.ListViewType]] = None,
+        **kwargs: Any
+    ) -> Iterable["_models.EnvironmentContainer"]:
+        """List environment containers.
+
+        List environment containers.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param skip: Continuation token for pagination. Default value is None.
+        :type skip: str
+        :param list_view_type: View type for including/excluding (for example) archived entities. Known
+         values are: "ActiveOnly", "ArchivedOnly", and "All". Default value is None.
+        :type list_view_type: str or ~azure.mgmt.machinelearningservices.models.ListViewType
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either EnvironmentContainer or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.EnvironmentContainer]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.EnvironmentContainerResourceArmPaginatedResult] = kwargs.pop("cls", None)
+
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        def prepare_request(next_link=None):
+            if not next_link:
+
+                request = build_list_request(
+                    resource_group_name=resource_group_name,
+                    registry_name=registry_name,
+                    subscription_id=self._config.subscription_id,
+                    skip=skip,
+                    list_view_type=list_view_type,
+                    api_version=api_version,
+                    template_url=self.list.metadata["url"],
+                    headers=_headers,
+                    params=_params,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                # make call to next link with the client's api-version
+                _parsed_next_link = urllib.parse.urlparse(next_link)
+                _next_request_params = case_insensitive_dict(
+                    {
+                        key: [urllib.parse.quote(v) for v in value]
+                        for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items()
+                    }
+                )
+                _next_request_params["api-version"] = self._config.api_version
+                request = HttpRequest(
+                    "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("EnvironmentContainerResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)  # type: ignore
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+                request, stream=False, **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+        return ItemPaged(get_next, extract_data)
+
+    list.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/environments"
+    }
+
+    def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self, resource_group_name: str, registry_name: str, environment_name: str, **kwargs: Any
+    ) -> None:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+
+        request = build_delete_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            environment_name=environment_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self._delete_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Location"] = self._deserialize("str", response.headers.get("Location"))
+            response_headers["Retry-After"] = self._deserialize("int", response.headers.get("Retry-After"))
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _delete_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/environments/{environmentName}"
+    }
+
+    @distributed_trace
+    def begin_delete(
+        self, resource_group_name: str, registry_name: str, environment_name: str, **kwargs: Any
+    ) -> LROPoller[None]:
+        """Delete container.
+
+        Delete container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param environment_name: Container name. Required.
+        :type environment_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[None]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+        polling: Union[bool, PollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = self._delete_initial(  # type: ignore
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                environment_name=environment_name,
+                api_version=api_version,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):  # pylint: disable=inconsistent-return-statements
+            if cls:
+                return cls(pipeline_response, None, {})
+
+        if polling is True:
+            polling_method: PollingMethod = cast(
+                PollingMethod, ARMPolling(lro_delay, lro_options={"final-state-via": "location"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(PollingMethod, NoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_delete.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/environments/{environmentName}"
+    }
+
+    @distributed_trace
+    def get(
+        self, resource_group_name: str, registry_name: str, environment_name: str, **kwargs: Any
+    ) -> _models.EnvironmentContainer:
+        """Get container.
+
+        Get container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param environment_name: Container name. This is case-sensitive. Required.
+        :type environment_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: EnvironmentContainer or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.EnvironmentContainer
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.EnvironmentContainer] = kwargs.pop("cls", None)
+
+        request = build_get_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            environment_name=environment_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self.get.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize("EnvironmentContainer", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/environments/{environmentName}"
+    }
+
+    def _create_or_update_initial(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        environment_name: str,
+        body: Union[_models.EnvironmentContainer, IO],
+        **kwargs: Any
+    ) -> _models.EnvironmentContainer:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.EnvironmentContainer] = kwargs.pop("cls", None)
+
+        content_type = content_type or "application/json"
+        _json = None
+        _content = None
+        if isinstance(body, (IO, bytes)):
+            _content = body
+        else:
+            _json = self._serialize.body(body, "EnvironmentContainer")
+
+        request = build_create_or_update_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            environment_name=environment_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            content=_content,
+            template_url=self._create_or_update_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize("EnvironmentContainer", pipeline_response)
+
+        if response.status_code == 201:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Azure-AsyncOperation"] = self._deserialize(
+                "str", response.headers.get("Azure-AsyncOperation")
+            )
+
+            deserialized = self._deserialize("EnvironmentContainer", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)  # type: ignore
+
+        return deserialized  # type: ignore
+
+    _create_or_update_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/environments/{environmentName}"
+    }
+
+    @overload
+    def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        environment_name: str,
+        body: _models.EnvironmentContainer,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> LROPoller[_models.EnvironmentContainer]:
+        """Create or update container.
+
+        Create or update container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param environment_name: Container name. Required.
+        :type environment_name: str
+        :param body: Container entity to create or update. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.EnvironmentContainer
+        :keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either EnvironmentContainer or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.EnvironmentContainer]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @overload
+    def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        environment_name: str,
+        body: IO,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> LROPoller[_models.EnvironmentContainer]:
+        """Create or update container.
+
+        Create or update container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param environment_name: Container name. Required.
+        :type environment_name: str
+        :param body: Container entity to create or update. Required.
+        :type body: IO
+        :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either EnvironmentContainer or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.EnvironmentContainer]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @distributed_trace
+    def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        environment_name: str,
+        body: Union[_models.EnvironmentContainer, IO],
+        **kwargs: Any
+    ) -> LROPoller[_models.EnvironmentContainer]:
+        """Create or update container.
+
+        Create or update container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param environment_name: Container name. Required.
+        :type environment_name: str
+        :param body: Container entity to create or update. Is either a model type or a IO type.
+         Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.EnvironmentContainer or IO
+        :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
+         Default value is None.
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either EnvironmentContainer or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.EnvironmentContainer]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.EnvironmentContainer] = kwargs.pop("cls", None)
+        polling: Union[bool, PollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                environment_name=environment_name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):
+            deserialized = self._deserialize("EnvironmentContainer", pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+        if polling is True:
+            polling_method: PollingMethod = cast(
+                PollingMethod, ARMPolling(lro_delay, lro_options={"final-state-via": "original-uri"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(PollingMethod, NoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_create_or_update.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/environments/{environmentName}"
+    }
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_registry_environment_versions_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_registry_environment_versions_operations.py
new file mode 100644
index 0000000000000..51ea7fbf62d5a
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_registry_environment_versions_operations.py
@@ -0,0 +1,849 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+import sys
+from typing import Any, Callable, Dict, IO, Iterable, Optional, TypeVar, Union, cast, overload
+import urllib.parse
+
+from azure.core.exceptions import (
+    ClientAuthenticationError,
+    HttpResponseError,
+    ResourceExistsError,
+    ResourceNotFoundError,
+    ResourceNotModifiedError,
+    map_error,
+)
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.polling import LROPoller, NoPolling, PollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.utils import case_insensitive_dict
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.arm_polling import ARMPolling
+
+from .. import models as _models
+from .._serialization import Serializer
+from .._vendor import _convert_request, _format_url_section
+
+if sys.version_info >= (3, 8):
+    from typing import Literal  # pylint: disable=no-name-in-module, ungrouped-imports
+else:
+    from typing_extensions import Literal  # type: ignore  # pylint: disable=ungrouped-imports
+T = TypeVar("T")
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+
+
+def build_list_request(
+    resource_group_name: str,
+    registry_name: str,
+    environment_name: str,
+    subscription_id: str,
+    *,
+    order_by: Optional[str] = None,
+    top: Optional[int] = None,
+    skip: Optional[str] = None,
+    list_view_type: Optional[Union[str, _models.ListViewType]] = None,
+    **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/environments/{environmentName}/versions",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url("registry_name", registry_name, "str"),
+        "environmentName": _SERIALIZER.url("environment_name", environment_name, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+    if order_by is not None:
+        _params["$orderBy"] = _SERIALIZER.query("order_by", order_by, "str")
+    if top is not None:
+        _params["$top"] = _SERIALIZER.query("top", top, "int")
+    if skip is not None:
+        _params["$skip"] = _SERIALIZER.query("skip", skip, "str")
+    if list_view_type is not None:
+        _params["listViewType"] = _SERIALIZER.query("list_view_type", list_view_type, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_delete_request(
+    resource_group_name: str,
+    registry_name: str,
+    environment_name: str,
+    version: str,
+    subscription_id: str,
+    **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/environments/{environmentName}/versions/{version}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url("registry_name", registry_name, "str"),
+        "environmentName": _SERIALIZER.url("environment_name", environment_name, "str"),
+        "version": _SERIALIZER.url("version", version, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="DELETE", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_get_request(
+    resource_group_name: str,
+    registry_name: str,
+    environment_name: str,
+    version: str,
+    subscription_id: str,
+    **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/environments/{environmentName}/versions/{version}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url("registry_name", registry_name, "str"),
+        "environmentName": _SERIALIZER.url("environment_name", environment_name, "str"),
+        "version": _SERIALIZER.url("version", version, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_create_or_update_request(
+    resource_group_name: str,
+    registry_name: str,
+    environment_name: str,
+    version: str,
+    subscription_id: str,
+    **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/environments/{environmentName}/versions/{version}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url("registry_name", registry_name, "str"),
+        "environmentName": _SERIALIZER.url(
+            "environment_name", environment_name, "str", pattern=r"^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$"
+        ),
+        "version": _SERIALIZER.url("version", version, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    if content_type is not None:
+        _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str")
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="PUT", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+class RegistryEnvironmentVersionsOperations:
+    """
+    .. warning::
+        **DO NOT** instantiate this class directly.
+
+        Instead, you should access the following operations through
+        :class:`~azure.mgmt.machinelearningservices.MachineLearningServicesMgmtClient`'s
+        :attr:`registry_environment_versions` attribute.
+    """
+
+    models = _models
+
+    def __init__(self, *args, **kwargs):
+        input_args = list(args)
+        self._client = input_args.pop(0) if input_args else kwargs.pop("client")
+        self._config = input_args.pop(0) if input_args else kwargs.pop("config")
+        self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer")
+        self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer")
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        environment_name: str,
+        order_by: Optional[str] = None,
+        top: Optional[int] = None,
+        skip: Optional[str] = None,
+        list_view_type: Optional[Union[str, _models.ListViewType]] = None,
+        **kwargs: Any
+    ) -> Iterable["_models.EnvironmentVersion"]:
+        """List versions.
+
+        List versions.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param environment_name: Container name. This is case-sensitive. Required.
+        :type environment_name: str
+        :param order_by: Ordering of list. Default value is None.
+        :type order_by: str
+        :param top: Maximum number of records to return. Default value is None.
+        :type top: int
+        :param skip: Continuation token for pagination. Default value is None.
+        :type skip: str
+        :param list_view_type: View type for including/excluding (for example) archived entities. Known
+         values are: "ActiveOnly", "ArchivedOnly", and "All". Default value is None.
+        :type list_view_type: str or ~azure.mgmt.machinelearningservices.models.ListViewType
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either EnvironmentVersion or the result of cls(response)
+        :rtype:
+         ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.EnvironmentVersion]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.EnvironmentVersionResourceArmPaginatedResult] = kwargs.pop("cls", None)
+
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        def prepare_request(next_link=None):
+            if not next_link:
+
+                request = build_list_request(
+                    resource_group_name=resource_group_name,
+                    registry_name=registry_name,
+                    environment_name=environment_name,
+                    subscription_id=self._config.subscription_id,
+                    order_by=order_by,
+                    top=top,
+                    skip=skip,
+                    list_view_type=list_view_type,
+                    api_version=api_version,
+                    template_url=self.list.metadata["url"],
+                    headers=_headers,
+                    params=_params,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                # make call to next link with the client's api-version
+                _parsed_next_link = urllib.parse.urlparse(next_link)
+                _next_request_params = case_insensitive_dict(
+                    {
+                        key: [urllib.parse.quote(v) for v in value]
+                        for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items()
+                    }
+                )
+                _next_request_params["api-version"] = self._config.api_version
+                request = HttpRequest(
+                    "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("EnvironmentVersionResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)  # type: ignore
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+                request, stream=False, **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+        return ItemPaged(get_next, extract_data)
+
+    list.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/environments/{environmentName}/versions"
+    }
+
+    def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self, resource_group_name: str, registry_name: str, environment_name: str, version: str, **kwargs: Any
+    ) -> None:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+
+        request = build_delete_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            environment_name=environment_name,
+            version=version,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self._delete_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Location"] = self._deserialize("str", response.headers.get("Location"))
+            response_headers["Retry-After"] = self._deserialize("int", response.headers.get("Retry-After"))
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _delete_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/environments/{environmentName}/versions/{version}"
+    }
+
+    @distributed_trace
+    def begin_delete(
+        self, resource_group_name: str, registry_name: str, environment_name: str, version: str, **kwargs: Any
+    ) -> LROPoller[None]:
+        """Delete version.
+
+        Delete version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param environment_name: Container name. Required.
+        :type environment_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[None]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+        polling: Union[bool, PollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = self._delete_initial(  # type: ignore
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                environment_name=environment_name,
+                version=version,
+                api_version=api_version,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):  # pylint: disable=inconsistent-return-statements
+            if cls:
+                return cls(pipeline_response, None, {})
+
+        if polling is True:
+            polling_method: PollingMethod = cast(
+                PollingMethod, ARMPolling(lro_delay, lro_options={"final-state-via": "location"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(PollingMethod, NoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_delete.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/environments/{environmentName}/versions/{version}"
+    }
+
+    @distributed_trace
+    def get(
+        self, resource_group_name: str, registry_name: str, environment_name: str, version: str, **kwargs: Any
+    ) -> _models.EnvironmentVersion:
+        """Get version.
+
+        Get version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param environment_name: Container name. This is case-sensitive. Required.
+        :type environment_name: str
+        :param version: Version identifier. This is case-sensitive. Required.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: EnvironmentVersion or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.EnvironmentVersion
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.EnvironmentVersion] = kwargs.pop("cls", None)
+
+        request = build_get_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            environment_name=environment_name,
+            version=version,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self.get.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize("EnvironmentVersion", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/environments/{environmentName}/versions/{version}"
+    }
+
+    def _create_or_update_initial(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        environment_name: str,
+        version: str,
+        body: Union[_models.EnvironmentVersion, IO],
+        **kwargs: Any
+    ) -> _models.EnvironmentVersion:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.EnvironmentVersion] = kwargs.pop("cls", None)
+
+        content_type = content_type or "application/json"
+        _json = None
+        _content = None
+        if isinstance(body, (IO, bytes)):
+            _content = body
+        else:
+            _json = self._serialize.body(body, "EnvironmentVersion")
+
+        request = build_create_or_update_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            environment_name=environment_name,
+            version=version,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            content=_content,
+            template_url=self._create_or_update_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize("EnvironmentVersion", pipeline_response)
+
+        if response.status_code == 201:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Azure-AsyncOperation"] = self._deserialize(
+                "str", response.headers.get("Azure-AsyncOperation")
+            )
+
+            deserialized = self._deserialize("EnvironmentVersion", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)  # type: ignore
+
+        return deserialized  # type: ignore
+
+    _create_or_update_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/environments/{environmentName}/versions/{version}"
+    }
+
+    @overload
+    def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        environment_name: str,
+        version: str,
+        body: _models.EnvironmentVersion,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> LROPoller[_models.EnvironmentVersion]:
+        """Create or update version.
+
+        Create or update version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param environment_name: Container name. Required.
+        :type environment_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :param body: Version entity to create or update. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.EnvironmentVersion
+        :keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either EnvironmentVersion or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.EnvironmentVersion]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @overload
+    def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        environment_name: str,
+        version: str,
+        body: IO,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> LROPoller[_models.EnvironmentVersion]:
+        """Create or update version.
+
+        Create or update version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param environment_name: Container name. Required.
+        :type environment_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :param body: Version entity to create or update. Required.
+        :type body: IO
+        :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either EnvironmentVersion or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.EnvironmentVersion]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @distributed_trace
+    def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        environment_name: str,
+        version: str,
+        body: Union[_models.EnvironmentVersion, IO],
+        **kwargs: Any
+    ) -> LROPoller[_models.EnvironmentVersion]:
+        """Create or update version.
+
+        Create or update version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param environment_name: Container name. Required.
+        :type environment_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :param body: Version entity to create or update. Is either a model type or a IO type. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.EnvironmentVersion or IO
+        :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
+         Default value is None.
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either EnvironmentVersion or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.EnvironmentVersion]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.EnvironmentVersion] = kwargs.pop("cls", None)
+        polling: Union[bool, PollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                environment_name=environment_name,
+                version=version,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):
+            deserialized = self._deserialize("EnvironmentVersion", pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+        if polling is True:
+            polling_method: PollingMethod = cast(
+                PollingMethod, ARMPolling(lro_delay, lro_options={"final-state-via": "original-uri"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(PollingMethod, NoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_create_or_update.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/environments/{environmentName}/versions/{version}"
+    }
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_registry_model_containers_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_registry_model_containers_operations.py
new file mode 100644
index 0000000000000..dc85a3c7ce70c
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_registry_model_containers_operations.py
@@ -0,0 +1,790 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+import sys
+from typing import Any, Callable, Dict, IO, Iterable, Optional, TypeVar, Union, cast, overload
+import urllib.parse
+
+from azure.core.exceptions import (
+    ClientAuthenticationError,
+    HttpResponseError,
+    ResourceExistsError,
+    ResourceNotFoundError,
+    ResourceNotModifiedError,
+    map_error,
+)
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.polling import LROPoller, NoPolling, PollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.utils import case_insensitive_dict
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.arm_polling import ARMPolling
+
+from .. import models as _models
+from .._serialization import Serializer
+from .._vendor import _convert_request, _format_url_section
+
+if sys.version_info >= (3, 8):
+    from typing import Literal  # pylint: disable=no-name-in-module, ungrouped-imports
+else:
+    from typing_extensions import Literal  # type: ignore  # pylint: disable=ungrouped-imports
+T = TypeVar("T")
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+
+
+def build_list_request(
+    resource_group_name: str,
+    registry_name: str,
+    subscription_id: str,
+    *,
+    skip: Optional[str] = None,
+    list_view_type: Optional[Union[str, _models.ListViewType]] = None,
+    **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/models",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url("registry_name", registry_name, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+    if skip is not None:
+        _params["$skip"] = _SERIALIZER.query("skip", skip, "str")
+    if list_view_type is not None:
+        _params["listViewType"] = _SERIALIZER.query("list_view_type", list_view_type, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_delete_request(
+    resource_group_name: str, registry_name: str, model_name: str, subscription_id: str, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/models/{modelName}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url("registry_name", registry_name, "str"),
+        "modelName": _SERIALIZER.url("model_name", model_name, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="DELETE", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_get_request(
+    resource_group_name: str, registry_name: str, model_name: str, subscription_id: str, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/models/{modelName}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url("registry_name", registry_name, "str"),
+        "modelName": _SERIALIZER.url("model_name", model_name, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_create_or_update_request(
+    resource_group_name: str, registry_name: str, model_name: str, subscription_id: str, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/models/{modelName}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url("registry_name", registry_name, "str"),
+        "modelName": _SERIALIZER.url("model_name", model_name, "str", pattern=r"^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    if content_type is not None:
+        _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str")
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="PUT", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+class RegistryModelContainersOperations:
+    """
+    .. warning::
+        **DO NOT** instantiate this class directly.
+
+        Instead, you should access the following operations through
+        :class:`~azure.mgmt.machinelearningservices.MachineLearningServicesMgmtClient`'s
+        :attr:`registry_model_containers` attribute.
+    """
+
+    models = _models
+
+    def __init__(self, *args, **kwargs):
+        input_args = list(args)
+        self._client = input_args.pop(0) if input_args else kwargs.pop("client")
+        self._config = input_args.pop(0) if input_args else kwargs.pop("config")
+        self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer")
+        self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer")
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        skip: Optional[str] = None,
+        list_view_type: Optional[Union[str, _models.ListViewType]] = None,
+        **kwargs: Any
+    ) -> Iterable["_models.ModelContainer"]:
+        """List model containers.
+
+        List model containers.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param skip: Continuation token for pagination. Default value is None.
+        :type skip: str
+        :param list_view_type: View type for including/excluding (for example) archived entities. Known
+         values are: "ActiveOnly", "ArchivedOnly", and "All". Default value is None.
+        :type list_view_type: str or ~azure.mgmt.machinelearningservices.models.ListViewType
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either ModelContainer or the result of cls(response)
+        :rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.ModelContainer]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.ModelContainerResourceArmPaginatedResult] = kwargs.pop("cls", None)
+
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        def prepare_request(next_link=None):
+            if not next_link:
+
+                request = build_list_request(
+                    resource_group_name=resource_group_name,
+                    registry_name=registry_name,
+                    subscription_id=self._config.subscription_id,
+                    skip=skip,
+                    list_view_type=list_view_type,
+                    api_version=api_version,
+                    template_url=self.list.metadata["url"],
+                    headers=_headers,
+                    params=_params,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                # make call to next link with the client's api-version
+                _parsed_next_link = urllib.parse.urlparse(next_link)
+                _next_request_params = case_insensitive_dict(
+                    {
+                        key: [urllib.parse.quote(v) for v in value]
+                        for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items()
+                    }
+                )
+                _next_request_params["api-version"] = self._config.api_version
+                request = HttpRequest(
+                    "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("ModelContainerResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)  # type: ignore
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+                request, stream=False, **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+        return ItemPaged(get_next, extract_data)
+
+    list.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/models"
+    }
+
+    def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self, resource_group_name: str, registry_name: str, model_name: str, **kwargs: Any
+    ) -> None:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+
+        request = build_delete_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            model_name=model_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self._delete_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Location"] = self._deserialize("str", response.headers.get("Location"))
+            response_headers["Retry-After"] = self._deserialize("int", response.headers.get("Retry-After"))
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _delete_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/models/{modelName}"
+    }
+
+    @distributed_trace
+    def begin_delete(
+        self, resource_group_name: str, registry_name: str, model_name: str, **kwargs: Any
+    ) -> LROPoller[None]:
+        """Delete container.
+
+        Delete container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param model_name: Container name. Required.
+        :type model_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[None]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+        polling: Union[bool, PollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = self._delete_initial(  # type: ignore
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                model_name=model_name,
+                api_version=api_version,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):  # pylint: disable=inconsistent-return-statements
+            if cls:
+                return cls(pipeline_response, None, {})
+
+        if polling is True:
+            polling_method: PollingMethod = cast(
+                PollingMethod, ARMPolling(lro_delay, lro_options={"final-state-via": "location"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(PollingMethod, NoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_delete.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/models/{modelName}"
+    }
+
+    @distributed_trace
+    def get(
+        self, resource_group_name: str, registry_name: str, model_name: str, **kwargs: Any
+    ) -> _models.ModelContainer:
+        """Get container.
+
+        Get container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param model_name: Container name. This is case-sensitive. Required.
+        :type model_name: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ModelContainer or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ModelContainer
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.ModelContainer] = kwargs.pop("cls", None)
+
+        request = build_get_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            model_name=model_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self.get.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize("ModelContainer", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/models/{modelName}"
+    }
+
+    def _create_or_update_initial(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        model_name: str,
+        body: Union[_models.ModelContainer, IO],
+        **kwargs: Any
+    ) -> _models.ModelContainer:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.ModelContainer] = kwargs.pop("cls", None)
+
+        content_type = content_type or "application/json"
+        _json = None
+        _content = None
+        if isinstance(body, (IO, bytes)):
+            _content = body
+        else:
+            _json = self._serialize.body(body, "ModelContainer")
+
+        request = build_create_or_update_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            model_name=model_name,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            content=_content,
+            template_url=self._create_or_update_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize("ModelContainer", pipeline_response)
+
+        if response.status_code == 201:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Azure-AsyncOperation"] = self._deserialize(
+                "str", response.headers.get("Azure-AsyncOperation")
+            )
+
+            deserialized = self._deserialize("ModelContainer", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)  # type: ignore
+
+        return deserialized  # type: ignore
+
+    _create_or_update_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/models/{modelName}"
+    }
+
+    @overload
+    def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        model_name: str,
+        body: _models.ModelContainer,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> LROPoller[_models.ModelContainer]:
+        """Create or update model container.
+
+        Create or update model container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param model_name: Container name. Required.
+        :type model_name: str
+        :param body: Container entity to create or update. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.ModelContainer
+        :keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either ModelContainer or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.ModelContainer]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @overload
+    def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        model_name: str,
+        body: IO,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> LROPoller[_models.ModelContainer]:
+        """Create or update model container.
+
+        Create or update model container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param model_name: Container name. Required.
+        :type model_name: str
+        :param body: Container entity to create or update. Required.
+        :type body: IO
+        :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either ModelContainer or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.ModelContainer]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @distributed_trace
+    def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        model_name: str,
+        body: Union[_models.ModelContainer, IO],
+        **kwargs: Any
+    ) -> LROPoller[_models.ModelContainer]:
+        """Create or update model container.
+
+        Create or update model container.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param model_name: Container name. Required.
+        :type model_name: str
+        :param body: Container entity to create or update. Is either a model type or a IO type.
+         Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.ModelContainer or IO
+        :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
+         Default value is None.
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either ModelContainer or the result of
+         cls(response)
+        :rtype:
+         ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.ModelContainer]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.ModelContainer] = kwargs.pop("cls", None)
+        polling: Union[bool, PollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                model_name=model_name,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):
+            deserialized = self._deserialize("ModelContainer", pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+        if polling is True:
+            polling_method: PollingMethod = cast(
+                PollingMethod, ARMPolling(lro_delay, lro_options={"final-state-via": "original-uri"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(PollingMethod, NoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_create_or_update.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/models/{modelName}"
+    }
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_registry_model_versions_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_registry_model_versions_operations.py
new file mode 100644
index 0000000000000..ddff22e501762
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_registry_model_versions_operations.py
@@ -0,0 +1,858 @@
+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+import sys
+from typing import Any, Callable, Dict, IO, Iterable, Optional, TypeVar, Union, cast, overload
+import urllib.parse
+
+from azure.core.exceptions import (
+    ClientAuthenticationError,
+    HttpResponseError,
+    ResourceExistsError,
+    ResourceNotFoundError,
+    ResourceNotModifiedError,
+    map_error,
+)
+from azure.core.paging import ItemPaged
+from azure.core.pipeline import PipelineResponse
+from azure.core.pipeline.transport import HttpResponse
+from azure.core.polling import LROPoller, NoPolling, PollingMethod
+from azure.core.rest import HttpRequest
+from azure.core.tracing.decorator import distributed_trace
+from azure.core.utils import case_insensitive_dict
+from azure.mgmt.core.exceptions import ARMErrorFormat
+from azure.mgmt.core.polling.arm_polling import ARMPolling
+
+from .. import models as _models
+from .._serialization import Serializer
+from .._vendor import _convert_request, _format_url_section
+
+if sys.version_info >= (3, 8):
+    from typing import Literal  # pylint: disable=no-name-in-module, ungrouped-imports
+else:
+    from typing_extensions import Literal  # type: ignore  # pylint: disable=ungrouped-imports
+T = TypeVar("T")
+ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
+
+_SERIALIZER = Serializer()
+_SERIALIZER.client_side_validation = False
+
+
+def build_list_request(
+    resource_group_name: str,
+    registry_name: str,
+    model_name: str,
+    subscription_id: str,
+    *,
+    skip: Optional[str] = None,
+    order_by: Optional[str] = None,
+    top: Optional[int] = None,
+    version: Optional[str] = None,
+    description: Optional[str] = None,
+    tags: Optional[str] = None,
+    properties: Optional[str] = None,
+    list_view_type: Optional[Union[str, _models.ListViewType]] = None,
+    **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/models/{modelName}/versions",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url("registry_name", registry_name, "str"),
+        "modelName": _SERIALIZER.url("model_name", model_name, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+    if skip is not None:
+        _params["$skip"] = _SERIALIZER.query("skip", skip, "str")
+    if order_by is not None:
+        _params["$orderBy"] = _SERIALIZER.query("order_by", order_by, "str")
+    if top is not None:
+        _params["$top"] = _SERIALIZER.query("top", top, "int")
+    if version is not None:
+        _params["version"] = _SERIALIZER.query("version", version, "str")
+    if description is not None:
+        _params["description"] = _SERIALIZER.query("description", description, "str")
+    if tags is not None:
+        _params["tags"] = _SERIALIZER.query("tags", tags, "str")
+    if properties is not None:
+        _params["properties"] = _SERIALIZER.query("properties", properties, "str")
+    if list_view_type is not None:
+        _params["listViewType"] = _SERIALIZER.query("list_view_type", list_view_type, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_delete_request(
+    resource_group_name: str, registry_name: str, model_name: str, version: str, subscription_id: str, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/models/{modelName}/versions/{version}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url("registry_name", registry_name, "str"),
+        "modelName": _SERIALIZER.url("model_name", model_name, "str"),
+        "version": _SERIALIZER.url("version", version, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="DELETE", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_get_request(
+    resource_group_name: str, registry_name: str, model_name: str, version: str, subscription_id: str, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/models/{modelName}/versions/{version}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url("registry_name", registry_name, "str"),
+        "modelName": _SERIALIZER.url("model_name", model_name, "str"),
+        "version": _SERIALIZER.url("version", version, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+def build_create_or_update_request(
+    resource_group_name: str, registry_name: str, model_name: str, version: str, subscription_id: str, **kwargs: Any
+) -> HttpRequest:
+    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
+    content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+    accept = _headers.pop("Accept", "application/json")
+
+    # Construct URL
+    _url = kwargs.pop(
+        "template_url",
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/models/{modelName}/versions/{version}",
+    )  # pylint: disable=line-too-long
+    path_format_arguments = {
+        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str", min_length=1),
+        "resourceGroupName": _SERIALIZER.url(
+            "resource_group_name", resource_group_name, "str", max_length=90, min_length=1
+        ),
+        "registryName": _SERIALIZER.url("registry_name", registry_name, "str"),
+        "modelName": _SERIALIZER.url("model_name", model_name, "str", pattern=r"^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$"),
+        "version": _SERIALIZER.url("version", version, "str"),
+    }
+
+    _url: str = _format_url_section(_url, **path_format_arguments)  # type: ignore
+
+    # Construct parameters
+    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
+
+    # Construct headers
+    if content_type is not None:
+        _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str")
+    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
+
+    return HttpRequest(method="PUT", url=_url, params=_params, headers=_headers, **kwargs)
+
+
+class RegistryModelVersionsOperations:
+    """
+    .. warning::
+        **DO NOT** instantiate this class directly.
+
+        Instead, you should access the following operations through
+        :class:`~azure.mgmt.machinelearningservices.MachineLearningServicesMgmtClient`'s
+        :attr:`registry_model_versions` attribute.
+    """
+
+    models = _models
+
+    def __init__(self, *args, **kwargs):
+        input_args = list(args)
+        self._client = input_args.pop(0) if input_args else kwargs.pop("client")
+        self._config = input_args.pop(0) if input_args else kwargs.pop("config")
+        self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer")
+        self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer")
+
+    @distributed_trace
+    def list(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        model_name: str,
+        skip: Optional[str] = None,
+        order_by: Optional[str] = None,
+        top: Optional[int] = None,
+        version: Optional[str] = None,
+        description: Optional[str] = None,
+        tags: Optional[str] = None,
+        properties: Optional[str] = None,
+        list_view_type: Optional[Union[str, _models.ListViewType]] = None,
+        **kwargs: Any
+    ) -> Iterable["_models.ModelVersion"]:
+        """List versions.
+
+        List versions.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param model_name: Container name. This is case-sensitive. Required.
+        :type model_name: str
+        :param skip: Continuation token for pagination. Default value is None.
+        :type skip: str
+        :param order_by: Ordering of list. Default value is None.
+        :type order_by: str
+        :param top: Maximum number of records to return. Default value is None.
+        :type top: int
+        :param version: Version identifier. Default value is None.
+        :type version: str
+        :param description: Model description. Default value is None.
+        :type description: str
+        :param tags: Comma-separated list of tag names (and optionally values). Example:
+         tag1,tag2=value2. Default value is None.
+        :type tags: str
+        :param properties: Comma-separated list of property names (and optionally values). Example:
+         prop1,prop2=value2. Default value is None.
+        :type properties: str
+        :param list_view_type: View type for including/excluding (for example) archived entities. Known
+         values are: "ActiveOnly", "ArchivedOnly", and "All". Default value is None.
+        :type list_view_type: str or ~azure.mgmt.machinelearningservices.models.ListViewType
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: An iterator like instance of either ModelVersion or the result of cls(response)
+        :rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.ModelVersion]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.ModelVersionResourceArmPaginatedResult] = kwargs.pop("cls", None)
+
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        def prepare_request(next_link=None):
+            if not next_link:
+
+                request = build_list_request(
+                    resource_group_name=resource_group_name,
+                    registry_name=registry_name,
+                    model_name=model_name,
+                    subscription_id=self._config.subscription_id,
+                    skip=skip,
+                    order_by=order_by,
+                    top=top,
+                    version=version,
+                    description=description,
+                    tags=tags,
+                    properties=properties,
+                    list_view_type=list_view_type,
+                    api_version=api_version,
+                    template_url=self.list.metadata["url"],
+                    headers=_headers,
+                    params=_params,
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+
+            else:
+                # make call to next link with the client's api-version
+                _parsed_next_link = urllib.parse.urlparse(next_link)
+                _next_request_params = case_insensitive_dict(
+                    {
+                        key: [urllib.parse.quote(v) for v in value]
+                        for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items()
+                    }
+                )
+                _next_request_params["api-version"] = self._config.api_version
+                request = HttpRequest(
+                    "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params
+                )
+                request = _convert_request(request)
+                request.url = self._client.format_url(request.url)
+                request.method = "GET"
+            return request
+
+        def extract_data(pipeline_response):
+            deserialized = self._deserialize("ModelVersionResourceArmPaginatedResult", pipeline_response)
+            list_of_elem = deserialized.value
+            if cls:
+                list_of_elem = cls(list_of_elem)  # type: ignore
+            return deserialized.next_link or None, iter(list_of_elem)
+
+        def get_next(next_link=None):
+            request = prepare_request(next_link)
+
+            pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+                request, stream=False, **kwargs
+            )
+            response = pipeline_response.http_response
+
+            if response.status_code not in [200]:
+                map_error(status_code=response.status_code, response=response, error_map=error_map)
+                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+            return pipeline_response
+
+        return ItemPaged(get_next, extract_data)
+
+    list.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/models/{modelName}/versions"
+    }
+
+    def _delete_initial(  # pylint: disable=inconsistent-return-statements
+        self, resource_group_name: str, registry_name: str, model_name: str, version: str, **kwargs: Any
+    ) -> None:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+
+        request = build_delete_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            model_name=model_name,
+            version=version,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self._delete_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 202, 204]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 202:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Location"] = self._deserialize("str", response.headers.get("Location"))
+            response_headers["Retry-After"] = self._deserialize("int", response.headers.get("Retry-After"))
+
+        if cls:
+            return cls(pipeline_response, None, response_headers)
+
+    _delete_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/models/{modelName}/versions/{version}"
+    }
+
+    @distributed_trace
+    def begin_delete(
+        self, resource_group_name: str, registry_name: str, model_name: str, version: str, **kwargs: Any
+    ) -> LROPoller[None]:
+        """Delete version.
+
+        Delete version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param model_name: Container name. Required.
+        :type model_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either None or the result of cls(response)
+        :rtype: ~azure.core.polling.LROPoller[None]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[None] = kwargs.pop("cls", None)
+        polling: Union[bool, PollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = self._delete_initial(  # type: ignore
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                model_name=model_name,
+                version=version,
+                api_version=api_version,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):  # pylint: disable=inconsistent-return-statements
+            if cls:
+                return cls(pipeline_response, None, {})
+
+        if polling is True:
+            polling_method: PollingMethod = cast(
+                PollingMethod, ARMPolling(lro_delay, lro_options={"final-state-via": "location"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(PollingMethod, NoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_delete.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/models/{modelName}/versions/{version}"
+    }
+
+    @distributed_trace
+    def get(
+        self, resource_group_name: str, registry_name: str, model_name: str, version: str, **kwargs: Any
+    ) -> _models.ModelVersion:
+        """Get version.
+
+        Get version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param model_name: Container name. This is case-sensitive. Required.
+        :type model_name: str
+        :param version: Version identifier. This is case-sensitive. Required.
+        :type version: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :return: ModelVersion or the result of cls(response)
+        :rtype: ~azure.mgmt.machinelearningservices.models.ModelVersion
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = kwargs.pop("headers", {}) or {}
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        cls: ClsType[_models.ModelVersion] = kwargs.pop("cls", None)
+
+        request = build_get_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            model_name=model_name,
+            version=version,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            template_url=self.get.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        deserialized = self._deserialize("ModelVersion", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, {})
+
+        return deserialized
+
+    get.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/models/{modelName}/versions/{version}"
+    }
+
+    def _create_or_update_initial(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        model_name: str,
+        version: str,
+        body: Union[_models.ModelVersion, IO],
+        **kwargs: Any
+    ) -> _models.ModelVersion:
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            409: ResourceExistsError,
+            304: ResourceNotModifiedError,
+        }
+        error_map.update(kwargs.pop("error_map", {}) or {})
+
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.ModelVersion] = kwargs.pop("cls", None)
+
+        content_type = content_type or "application/json"
+        _json = None
+        _content = None
+        if isinstance(body, (IO, bytes)):
+            _content = body
+        else:
+            _json = self._serialize.body(body, "ModelVersion")
+
+        request = build_create_or_update_request(
+            resource_group_name=resource_group_name,
+            registry_name=registry_name,
+            model_name=model_name,
+            version=version,
+            subscription_id=self._config.subscription_id,
+            api_version=api_version,
+            content_type=content_type,
+            json=_json,
+            content=_content,
+            template_url=self._create_or_update_initial.metadata["url"],
+            headers=_headers,
+            params=_params,
+        )
+        request = _convert_request(request)
+        request.url = self._client.format_url(request.url)
+
+        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
+            request, stream=False, **kwargs
+        )
+
+        response = pipeline_response.http_response
+
+        if response.status_code not in [200, 201]:
+            map_error(status_code=response.status_code, response=response, error_map=error_map)
+            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
+            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
+
+        response_headers = {}
+        if response.status_code == 200:
+            deserialized = self._deserialize("ModelVersion", pipeline_response)
+
+        if response.status_code == 201:
+            response_headers["x-ms-async-operation-timeout"] = self._deserialize(
+                "duration", response.headers.get("x-ms-async-operation-timeout")
+            )
+            response_headers["Azure-AsyncOperation"] = self._deserialize(
+                "str", response.headers.get("Azure-AsyncOperation")
+            )
+
+            deserialized = self._deserialize("ModelVersion", pipeline_response)
+
+        if cls:
+            return cls(pipeline_response, deserialized, response_headers)  # type: ignore
+
+        return deserialized  # type: ignore
+
+    _create_or_update_initial.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/models/{modelName}/versions/{version}"
+    }
+
+    @overload
+    def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        model_name: str,
+        version: str,
+        body: _models.ModelVersion,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> LROPoller[_models.ModelVersion]:
+        """Create or update version.
+
+        Create or update version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param model_name: Container name. Required.
+        :type model_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :param body: Version entity to create or update. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.ModelVersion
+        :keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either ModelVersion or the result of
+         cls(response)
+        :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.ModelVersion]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @overload
+    def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        model_name: str,
+        version: str,
+        body: IO,
+        *,
+        content_type: str = "application/json",
+        **kwargs: Any
+    ) -> LROPoller[_models.ModelVersion]:
+        """Create or update version.
+
+        Create or update version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param model_name: Container name. Required.
+        :type model_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :param body: Version entity to create or update. Required.
+        :type body: IO
+        :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
+         Default value is "application/json".
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either ModelVersion or the result of
+         cls(response)
+        :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.ModelVersion]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+
+    @distributed_trace
+    def begin_create_or_update(
+        self,
+        resource_group_name: str,
+        registry_name: str,
+        model_name: str,
+        version: str,
+        body: Union[_models.ModelVersion, IO],
+        **kwargs: Any
+    ) -> LROPoller[_models.ModelVersion]:
+        """Create or update version.
+
+        Create or update version.
+
+        :param resource_group_name: The name of the resource group. The name is case insensitive.
+         Required.
+        :type resource_group_name: str
+        :param registry_name: Name of Azure Machine Learning registry. Required.
+        :type registry_name: str
+        :param model_name: Container name. Required.
+        :type model_name: str
+        :param version: Version identifier. Required.
+        :type version: str
+        :param body: Version entity to create or update. Is either a model type or a IO type. Required.
+        :type body: ~azure.mgmt.machinelearningservices.models.ModelVersion or IO
+        :keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
+         Default value is None.
+        :paramtype content_type: str
+        :keyword callable cls: A custom type or function that will be passed the direct response
+        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
+        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
+         operation to not poll, or pass in your own initialized polling object for a personal polling
+         strategy.
+        :paramtype polling: bool or ~azure.core.polling.PollingMethod
+        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
+         Retry-After header is present.
+        :return: An instance of LROPoller that returns either ModelVersion or the result of
+         cls(response)
+        :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.ModelVersion]
+        :raises ~azure.core.exceptions.HttpResponseError:
+        """
+        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
+        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
+
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+            "api_version", _params.pop("api-version", self._config.api_version)
+        )
+        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
+        cls: ClsType[_models.ModelVersion] = kwargs.pop("cls", None)
+        polling: Union[bool, PollingMethod] = kwargs.pop("polling", True)
+        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
+        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
+        if cont_token is None:
+            raw_result = self._create_or_update_initial(
+                resource_group_name=resource_group_name,
+                registry_name=registry_name,
+                model_name=model_name,
+                version=version,
+                body=body,
+                api_version=api_version,
+                content_type=content_type,
+                cls=lambda x, y, z: x,
+                headers=_headers,
+                params=_params,
+                **kwargs
+            )
+        kwargs.pop("error_map", None)
+
+        def get_long_running_output(pipeline_response):
+            deserialized = self._deserialize("ModelVersion", pipeline_response)
+            if cls:
+                return cls(pipeline_response, deserialized, {})
+            return deserialized
+
+        if polling is True:
+            polling_method: PollingMethod = cast(
+                PollingMethod, ARMPolling(lro_delay, lro_options={"final-state-via": "original-uri"}, **kwargs)
+            )
+        elif polling is False:
+            polling_method = cast(PollingMethod, NoPolling())
+        else:
+            polling_method = polling
+        if cont_token:
+            return LROPoller.from_continuation_token(
+                polling_method=polling_method,
+                continuation_token=cont_token,
+                client=self._client,
+                deserialization_callback=get_long_running_output,
+            )
+        return LROPoller(self._client, raw_result, get_long_running_output, polling_method)  # type: ignore
+
+    begin_create_or_update.metadata = {
+        "url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/registries/{registryName}/models/{modelName}/versions/{version}"
+    }
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_schedules_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_schedules_operations.py
index ccf22fb3aed15..2c8ab3341b5c0 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_schedules_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_schedules_operations.py
@@ -55,7 +55,9 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -92,7 +94,9 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -126,7 +130,9 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -160,7 +166,9 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -241,7 +249,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ScheduleResourceArmPaginatedResult] = kwargs.pop("cls", None)
@@ -331,7 +339,7 @@ def _delete_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -403,7 +411,7 @@ def begin_delete(self, resource_group_name: str, workspace_name: str, name: str,
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -475,7 +483,7 @@ def get(self, resource_group_name: str, workspace_name: str, name: str, **kwargs
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.Schedule] = kwargs.pop("cls", None)
@@ -529,7 +537,7 @@ def _create_or_update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -708,7 +716,7 @@ def begin_create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_usages_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_usages_operations.py
index 87b60742ab08e..2b82aae135533 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_usages_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_usages_operations.py
@@ -45,7 +45,9 @@ def build_list_request(location: str, subscription_id: str, **kwargs: Any) -> Ht
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -103,7 +105,7 @@ def list(self, location: str, **kwargs: Any) -> Iterable["_models.Usage"]:
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ListUsagesResult] = kwargs.pop("cls", None)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_virtual_machine_sizes_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_virtual_machine_sizes_operations.py
index 4928f81dcedfc..dfdc25770fa82 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_virtual_machine_sizes_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_virtual_machine_sizes_operations.py
@@ -43,7 +43,9 @@ def build_list_request(location: str, subscription_id: str, **kwargs: Any) -> Ht
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -108,7 +110,7 @@ def list(self, location: str, **kwargs: Any) -> _models.VirtualMachineSizeListRe
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.VirtualMachineSizeListResult] = kwargs.pop("cls", None)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_workspace_connections_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_workspace_connections_operations.py
index c85caf24b6e80..d5076c50ca1eb 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_workspace_connections_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_workspace_connections_operations.py
@@ -47,7 +47,9 @@ def build_create_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -84,7 +86,9 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -118,7 +122,9 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -158,7 +164,9 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -312,7 +320,7 @@ def create(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -393,7 +401,7 @@ def get(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.WorkspaceConnectionPropertiesV2BasicResource] = kwargs.pop("cls", None)
@@ -462,7 +470,7 @@ def delete(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -528,7 +536,7 @@ def list(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.WorkspaceConnectionPropertiesV2BasicResourceArmPaginatedResult] = kwargs.pop("cls", None)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_workspace_features_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_workspace_features_operations.py
index 00c72c0a8b824..bc4b5c0287133 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_workspace_features_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_workspace_features_operations.py
@@ -47,7 +47,9 @@ def build_list_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -110,7 +112,7 @@ def list(self, resource_group_name: str, workspace_name: str, **kwargs: Any) ->
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ListAmlUserFeatureResult] = kwargs.pop("cls", None)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_workspaces_operations.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_workspaces_operations.py
index 3d8220b5fefaf..0469501e6ea81 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_workspaces_operations.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/azure/mgmt/machinelearningservices/operations/_workspaces_operations.py
@@ -49,7 +49,9 @@ def build_get_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -82,7 +84,9 @@ def build_create_or_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -118,7 +122,9 @@ def build_delete_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -151,7 +157,9 @@ def build_update_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -187,7 +195,9 @@ def build_list_by_resource_group_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -221,7 +231,9 @@ def build_diagnose_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
     accept = _headers.pop("Accept", "application/json")
 
@@ -257,7 +269,9 @@ def build_list_keys_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -290,7 +304,9 @@ def build_resync_keys_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -323,7 +339,9 @@ def build_list_by_subscription_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -353,7 +371,9 @@ def build_list_notebook_access_token_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -386,7 +406,9 @@ def build_prepare_notebook_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -419,7 +441,9 @@ def build_list_storage_account_keys_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -452,7 +476,9 @@ def build_list_notebook_keys_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -485,7 +511,9 @@ def build_list_outbound_network_dependencies_endpoints_request(
     _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
     _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-    api_version: Literal["2022-10-01"] = kwargs.pop("api_version", _params.pop("api-version", "2022-10-01"))
+    api_version: Literal["2022-12-01-preview"] = kwargs.pop(
+        "api_version", _params.pop("api-version", "2022-12-01-preview")
+    )
     accept = _headers.pop("Accept", "application/json")
 
     # Construct URL
@@ -556,7 +584,7 @@ def get(self, resource_group_name: str, workspace_name: str, **kwargs: Any) -> _
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.Workspace] = kwargs.pop("cls", None)
@@ -609,7 +637,7 @@ def _create_or_update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -766,7 +794,7 @@ def begin_create_or_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -827,7 +855,7 @@ def _delete_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -886,7 +914,7 @@ def begin_delete(self, resource_group_name: str, workspace_name: str, **kwargs:
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -946,7 +974,7 @@ def _update_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1105,7 +1133,7 @@ def begin_update(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1171,7 +1199,7 @@ def list_by_resource_group(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.WorkspaceListResult] = kwargs.pop("cls", None)
@@ -1263,7 +1291,7 @@ def _diagnose_initial(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1442,7 +1470,7 @@ def begin_diagnose(
         _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
@@ -1519,7 +1547,7 @@ def list_keys(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ListWorkspaceKeysResult] = kwargs.pop("cls", None)
@@ -1572,7 +1600,7 @@ def _resync_keys_initial(  # pylint: disable=inconsistent-return-statements
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -1632,7 +1660,7 @@ def begin_resync_keys(self, resource_group_name: str, workspace_name: str, **kwa
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[None] = kwargs.pop("cls", None)
@@ -1688,7 +1716,7 @@ def list_by_subscription(self, skip: Optional[str] = None, **kwargs: Any) -> Ite
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.WorkspaceListResult] = kwargs.pop("cls", None)
@@ -1788,7 +1816,7 @@ def list_notebook_access_token(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.NotebookAccessTokenResult] = kwargs.pop("cls", None)
@@ -1841,7 +1869,7 @@ def _prepare_notebook_initial(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[Optional[_models.NotebookResourceInfo]] = kwargs.pop("cls", None)
@@ -1910,7 +1938,7 @@ def begin_prepare_notebook(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.NotebookResourceInfo] = kwargs.pop("cls", None)
@@ -1983,7 +2011,7 @@ def list_storage_account_keys(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ListStorageAccountKeysResult] = kwargs.pop("cls", None)
@@ -2049,7 +2077,7 @@ def list_notebook_keys(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ListNotebookKeysResult] = kwargs.pop("cls", None)
@@ -2119,7 +2147,7 @@ def list_outbound_network_dependencies_endpoints(
         _headers = kwargs.pop("headers", {}) or {}
         _params = case_insensitive_dict(kwargs.pop("params", {}) or {})
 
-        api_version: Literal["2022-10-01"] = kwargs.pop(
+        api_version: Literal["2022-12-01-preview"] = kwargs.pop(
             "api_version", _params.pop("api-version", self._config.api_version)
         )
         cls: ClsType[_models.ExternalFQDNResponse] = kwargs.pop("cls", None)
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/aks_compute.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/aks_compute.py
index 4154ad04114ea..4076cc618bc58 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/aks_compute.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/aks_compute.py
@@ -37,6 +37,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Compute/get/AKSCompute.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Compute/get/AKSCompute.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/aml_compute.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/aml_compute.py
index 9b60010544b51..0667d985728b8 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/aml_compute.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/aml_compute.py
@@ -37,6 +37,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Compute/get/AmlCompute.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Compute/get/AmlCompute.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/basic_aks_compute.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/basic_aks_compute.py
index 7bf0144d06dc0..6407a4e58b433 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/basic_aks_compute.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/basic_aks_compute.py
@@ -38,6 +38,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Compute/createOrUpdate/BasicAKSCompute.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Compute/createOrUpdate/BasicAKSCompute.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/basic_aml_compute.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/basic_aml_compute.py
index d6cfcd9090348..67289ff39e1cb 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/basic_aml_compute.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/basic_aml_compute.py
@@ -55,6 +55,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Compute/createOrUpdate/BasicAmlCompute.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Compute/createOrUpdate/BasicAmlCompute.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/basic_data_factory_compute.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/basic_data_factory_compute.py
index c7256aaa72663..b4cf67db70613 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/basic_data_factory_compute.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/basic_data_factory_compute.py
@@ -38,6 +38,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Compute/createOrUpdate/BasicDataFactoryCompute.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Compute/createOrUpdate/BasicDataFactoryCompute.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/cancel.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/cancel.py
index 14832bc92a6c8..d554f8c0d7940 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/cancel.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/cancel.py
@@ -37,6 +37,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Job/cancel.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Job/cancel.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/compute_instance.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/compute_instance.py
index 8be35ceb91e48..3400e69d47077 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/compute_instance.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/compute_instance.py
@@ -37,6 +37,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Compute/get/ComputeInstance.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Compute/get/ComputeInstance.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/compute_instance_minimal.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/compute_instance_minimal.py
index f67029c8ca3c4..0153e7dd60a57 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/compute_instance_minimal.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/compute_instance_minimal.py
@@ -41,6 +41,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Compute/createOrUpdate/ComputeInstanceMinimal.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Compute/createOrUpdate/ComputeInstanceMinimal.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/compute_instance_with_schedules.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/compute_instance_with_schedules.py
index c5ef7c03d6b95..3bb7288bb00a9 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/compute_instance_with_schedules.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/compute_instance_with_schedules.py
@@ -54,6 +54,7 @@ def main():
                                     "expression": "0 18 * * *",
                                     "startTime": "2021-04-23T01:30:00",
                                     "timeZone": "Pacific Standard Time",
+                                    "triggerType": "Cron",
                                 },
                                 "status": "Enabled",
                                 "triggerType": "Cron",
@@ -69,6 +70,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Compute/createOrUpdate/ComputeInstanceWithSchedules.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Compute/createOrUpdate/ComputeInstanceWithSchedules.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/create.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/create.py
index 0d7e657dba306..80a6f24f6a985 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/create.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/create.py
@@ -76,6 +76,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Workspace/create.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Workspace/create.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/create_or_update.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/create_or_update.py
index d69a53bd684cd..be4ba37711cd6 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/create_or_update.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/create_or_update.py
@@ -40,6 +40,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/PrivateEndpointConnection/createOrUpdate.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/PrivateEndpointConnection/createOrUpdate.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/create_or_update_system_created.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/create_or_update_system_created.py
new file mode 100644
index 0000000000000..30906e902b1c3
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/create_or_update_system_created.py
@@ -0,0 +1,76 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+from azure.identity import DefaultAzureCredential
+from azure.mgmt.machinelearningservices import MachineLearningServicesMgmtClient
+
+"""
+# PREREQUISITES
+    pip install azure-identity
+    pip install azure-mgmt-machinelearningservices
+# USAGE
+    python create_or_update_system_created.py
+
+    Before run the sample, please set the values of the client ID, tenant ID and client secret
+    of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID,
+    AZURE_CLIENT_SECRET. For more info about how to get the value, please see:
+    https://docs.microsoft.com/azure/active-directory/develop/howto-create-service-principal-portal
+"""
+
+
+def main():
+    client = MachineLearningServicesMgmtClient(
+        credential=DefaultAzureCredential(),
+        subscription_id="00000000-1111-2222-3333-444444444444",
+    )
+
+    response = client.registries.begin_create_or_update(
+        resource_group_name="test-rg",
+        registry_name="string",
+        body={
+            "identity": {"type": "None", "userAssignedIdentities": {"string": {}}},
+            "kind": "string",
+            "location": "string",
+            "properties": {
+                "description": "string",
+                "properties": {"string": "string"},
+                "regionDetails": [
+                    {
+                        "acrDetails": [
+                            {
+                                "systemCreatedAcrAccount": {
+                                    "acrAccountSku": "string",
+                                    "armResourceId": {"resourceId": "string"},
+                                }
+                            }
+                        ],
+                        "location": "string",
+                        "storageAccountDetails": [
+                            {
+                                "systemCreatedStorageAccount": {
+                                    "allowBlobPublicAccess": False,
+                                    "armResourceId": {"resourceId": "string"},
+                                    "storageAccountHnsEnabled": False,
+                                    "storageAccountType": "string",
+                                }
+                            }
+                        ],
+                    }
+                ],
+                "tags": {"string": "string"},
+            },
+            "sku": {"capacity": 1, "family": "string", "name": "string", "size": "string", "tier": "Basic"},
+            "tags": {},
+        },
+    ).result()
+    print(response)
+
+
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Registries/createOrUpdate-SystemCreated.json
+if __name__ == "__main__":
+    main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/create_or_update_user_created.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/create_or_update_user_created.py
new file mode 100644
index 0000000000000..8aeaea152eebf
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/create_or_update_user_created.py
@@ -0,0 +1,62 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+from azure.identity import DefaultAzureCredential
+from azure.mgmt.machinelearningservices import MachineLearningServicesMgmtClient
+
+"""
+# PREREQUISITES
+    pip install azure-identity
+    pip install azure-mgmt-machinelearningservices
+# USAGE
+    python create_or_update_user_created.py
+
+    Before run the sample, please set the values of the client ID, tenant ID and client secret
+    of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID,
+    AZURE_CLIENT_SECRET. For more info about how to get the value, please see:
+    https://docs.microsoft.com/azure/active-directory/develop/howto-create-service-principal-portal
+"""
+
+
+def main():
+    client = MachineLearningServicesMgmtClient(
+        credential=DefaultAzureCredential(),
+        subscription_id="00000000-1111-2222-3333-444444444444",
+    )
+
+    response = client.registries.begin_create_or_update(
+        resource_group_name="test-rg",
+        registry_name="string",
+        body={
+            "identity": {"type": "None", "userAssignedIdentities": {"string": {}}},
+            "kind": "string",
+            "location": "string",
+            "properties": {
+                "description": "string",
+                "properties": {"string": "string"},
+                "regionDetails": [
+                    {
+                        "acrDetails": [{"userCreatedAcrAccount": {"armResourceId": {"resourceId": "string"}}}],
+                        "location": "string",
+                        "storageAccountDetails": [
+                            {"userCreatedStorageAccount": {"armResourceId": {"resourceId": "string"}}}
+                        ],
+                    }
+                ],
+                "tags": {"string": "string"},
+            },
+            "sku": {"capacity": 1, "family": "string", "name": "string", "size": "string", "tier": "Basic"},
+            "tags": {},
+        },
+    ).result()
+    print(response)
+
+
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Registries/createOrUpdate-UserCreated.json
+if __name__ == "__main__":
+    main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/delete.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/delete.py
index 1ab7cceee3e0f..f62d5399a4f1c 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/delete.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/delete.py
@@ -36,6 +36,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Workspace/delete.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Workspace/delete.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/diagnose.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/diagnose.py
index e7d53be6b0115..e20a29c715650 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/diagnose.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/diagnose.py
@@ -36,6 +36,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Workspace/diagnose.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Workspace/diagnose.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/export_labels.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/export_labels.py
new file mode 100644
index 0000000000000..d559887fb46c2
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/export_labels.py
@@ -0,0 +1,43 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+from azure.identity import DefaultAzureCredential
+from azure.mgmt.machinelearningservices import MachineLearningServicesMgmtClient
+
+"""
+# PREREQUISITES
+    pip install azure-identity
+    pip install azure-mgmt-machinelearningservices
+# USAGE
+    python export_labels.py
+
+    Before run the sample, please set the values of the client ID, tenant ID and client secret
+    of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID,
+    AZURE_CLIENT_SECRET. For more info about how to get the value, please see:
+    https://docs.microsoft.com/azure/active-directory/develop/howto-create-service-principal-portal
+"""
+
+
+def main():
+    client = MachineLearningServicesMgmtClient(
+        credential=DefaultAzureCredential(),
+        subscription_id="00000000-1111-2222-3333-444444444444",
+    )
+
+    response = client.labeling_jobs.begin_export_labels(
+        resource_group_name="workspace-1234",
+        workspace_name="testworkspace",
+        id="testLabelingJob",
+        body={"format": "Dataset"},
+    ).result()
+    print(response)
+
+
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/LabelingJob/exportLabels.json
+if __name__ == "__main__":
+    main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get.py
index ae2325915702f..41a3291a22294 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get.py
@@ -36,6 +36,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Workspace/get.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Workspace/get.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get_logs.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get_logs.py
index 08ca1c985e42e..a12ca17ec7374 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get_logs.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get_logs.py
@@ -39,6 +39,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/OnlineDeployment/getLogs.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/OnlineDeployment/getLogs.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get_system_created.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get_system_created.py
new file mode 100644
index 0000000000000..7ee1a016ee6ac
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get_system_created.py
@@ -0,0 +1,41 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+from azure.identity import DefaultAzureCredential
+from azure.mgmt.machinelearningservices import MachineLearningServicesMgmtClient
+
+"""
+# PREREQUISITES
+    pip install azure-identity
+    pip install azure-mgmt-machinelearningservices
+# USAGE
+    python get_system_created.py
+
+    Before run the sample, please set the values of the client ID, tenant ID and client secret
+    of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID,
+    AZURE_CLIENT_SECRET. For more info about how to get the value, please see:
+    https://docs.microsoft.com/azure/active-directory/develop/howto-create-service-principal-portal
+"""
+
+
+def main():
+    client = MachineLearningServicesMgmtClient(
+        credential=DefaultAzureCredential(),
+        subscription_id="00000000-1111-2222-3333-444444444444",
+    )
+
+    response = client.registries.get(
+        resource_group_name="test-rg",
+        registry_name="string",
+    )
+    print(response)
+
+
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Registries/get-SystemCreated.json
+if __name__ == "__main__":
+    main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get_token.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get_token.py
index ce3165223bbc9..7459f16a28be5 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get_token.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get_token.py
@@ -37,6 +37,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/OnlineEndpoint/getToken.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Workspace/OnlineEndpoint/getToken.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get_user_created.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get_user_created.py
new file mode 100644
index 0000000000000..aff5df16b4f05
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/get_user_created.py
@@ -0,0 +1,41 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+from azure.identity import DefaultAzureCredential
+from azure.mgmt.machinelearningservices import MachineLearningServicesMgmtClient
+
+"""
+# PREREQUISITES
+    pip install azure-identity
+    pip install azure-mgmt-machinelearningservices
+# USAGE
+    python get_user_created.py
+
+    Before run the sample, please set the values of the client ID, tenant ID and client secret
+    of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID,
+    AZURE_CLIENT_SECRET. For more info about how to get the value, please see:
+    https://docs.microsoft.com/azure/active-directory/develop/howto-create-service-principal-portal
+"""
+
+
+def main():
+    client = MachineLearningServicesMgmtClient(
+        credential=DefaultAzureCredential(),
+        subscription_id="00000000-1111-2222-3333-444444444444",
+    )
+
+    response = client.registries.get(
+        resource_group_name="test-rg",
+        registry_name="string",
+    )
+    print(response)
+
+
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Registries/get-UserCreated.json
+if __name__ == "__main__":
+    main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/kubernetes_compute.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/kubernetes_compute.py
index 1c4abcb9c9985..8fee0582bf859 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/kubernetes_compute.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/kubernetes_compute.py
@@ -37,6 +37,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Compute/get/KubernetesCompute.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Compute/get/KubernetesCompute.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list.py
index 803939df104d0..39f5756eea019 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list.py
@@ -36,6 +36,6 @@ def main():
         print(item)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Usage/list.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Usage/list.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_by_resource_group.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_by_resource_group.py
index ab77ccf41bcbe..59fef105c3c3c 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_by_resource_group.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_by_resource_group.py
@@ -36,6 +36,6 @@ def main():
         print(item)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Workspace/listByResourceGroup.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Workspace/listByResourceGroup.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_by_subscription.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_by_subscription.py
index 021a94dc65b3e..6937bf4bcb2bc 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_by_subscription.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_by_subscription.py
@@ -34,6 +34,6 @@ def main():
         print(item)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Workspace/listBySubscription.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Workspace/listBySubscription.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_keys.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_keys.py
index 4329788bf08cc..bf49d5dc50473 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_keys.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_keys.py
@@ -36,6 +36,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Workspace/listKeys.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Workspace/listKeys.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_nodes.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_nodes.py
index 08a92686eed7c..757c6f3c20c6a 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_nodes.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_nodes.py
@@ -38,6 +38,6 @@ def main():
         print(item)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Compute/listNodes.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Compute/listNodes.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_notebook_access_token.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_notebook_access_token.py
index 2231f08361b1f..3906ab4cb675d 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_notebook_access_token.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_notebook_access_token.py
@@ -36,6 +36,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Workspace/listNotebookAccessToken.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Workspace/listNotebookAccessToken.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_secrets.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_secrets.py
index 6c9f9145642c6..7beeb4d8a994f 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_secrets.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_secrets.py
@@ -37,6 +37,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Datastore/listSecrets.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Datastore/listSecrets.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_skus.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_skus.py
index a1cd89f08c224..1c16b81ebfeb5 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_skus.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_skus.py
@@ -39,6 +39,6 @@ def main():
         print(item)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/OnlineDeployment/KubernetesOnlineDeployment/listSkus.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/OnlineDeployment/KubernetesOnlineDeployment/listSkus.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_storage_account_keys.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_storage_account_keys.py
index 84b7f19567c09..2014f9dadafb4 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_storage_account_keys.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_storage_account_keys.py
@@ -36,6 +36,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Workspace/listStorageAccountKeys.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Workspace/listStorageAccountKeys.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_system_created.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_system_created.py
new file mode 100644
index 0000000000000..aca172abd0487
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_system_created.py
@@ -0,0 +1,41 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+from azure.identity import DefaultAzureCredential
+from azure.mgmt.machinelearningservices import MachineLearningServicesMgmtClient
+
+"""
+# PREREQUISITES
+    pip install azure-identity
+    pip install azure-mgmt-machinelearningservices
+# USAGE
+    python list_system_created.py
+
+    Before run the sample, please set the values of the client ID, tenant ID and client secret
+    of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID,
+    AZURE_CLIENT_SECRET. For more info about how to get the value, please see:
+    https://docs.microsoft.com/azure/active-directory/develop/howto-create-service-principal-portal
+"""
+
+
+def main():
+    client = MachineLearningServicesMgmtClient(
+        credential=DefaultAzureCredential(),
+        subscription_id="00000000-1111-2222-3333-444444444444",
+    )
+
+    response = client.registries.list(
+        resource_group_name="test-rg",
+    )
+    for item in response:
+        print(item)
+
+
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Registries/list-SystemCreated.json
+if __name__ == "__main__":
+    main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_user_created.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_user_created.py
new file mode 100644
index 0000000000000..e2a73bfe57205
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/list_user_created.py
@@ -0,0 +1,41 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+from azure.identity import DefaultAzureCredential
+from azure.mgmt.machinelearningservices import MachineLearningServicesMgmtClient
+
+"""
+# PREREQUISITES
+    pip install azure-identity
+    pip install azure-mgmt-machinelearningservices
+# USAGE
+    python list_user_created.py
+
+    Before run the sample, please set the values of the client ID, tenant ID and client secret
+    of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID,
+    AZURE_CLIENT_SECRET. For more info about how to get the value, please see:
+    https://docs.microsoft.com/azure/active-directory/develop/howto-create-service-principal-portal
+"""
+
+
+def main():
+    client = MachineLearningServicesMgmtClient(
+        credential=DefaultAzureCredential(),
+        subscription_id="00000000-1111-2222-3333-444444444444",
+    )
+
+    response = client.registries.list(
+        resource_group_name="test-rg",
+    )
+    for item in response:
+        print(item)
+
+
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Registries/list-UserCreated.json
+if __name__ == "__main__":
+    main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/operations_list.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/operations_list.py
index 1ed3dd9d1da44..4c18849aa8794 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/operations_list.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/operations_list.py
@@ -34,6 +34,6 @@ def main():
         print(item)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Workspace/operationsList.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Workspace/operationsList.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/patch.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/patch.py
index 88d3d61b00992..5eccaa8aa804b 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/patch.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/patch.py
@@ -44,6 +44,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Compute/patch.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Compute/patch.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/pause.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/pause.py
new file mode 100644
index 0000000000000..e7d5e72aad05b
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/pause.py
@@ -0,0 +1,42 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+from azure.identity import DefaultAzureCredential
+from azure.mgmt.machinelearningservices import MachineLearningServicesMgmtClient
+
+"""
+# PREREQUISITES
+    pip install azure-identity
+    pip install azure-mgmt-machinelearningservices
+# USAGE
+    python pause.py
+
+    Before run the sample, please set the values of the client ID, tenant ID and client secret
+    of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID,
+    AZURE_CLIENT_SECRET. For more info about how to get the value, please see:
+    https://docs.microsoft.com/azure/active-directory/develop/howto-create-service-principal-portal
+"""
+
+
+def main():
+    client = MachineLearningServicesMgmtClient(
+        credential=DefaultAzureCredential(),
+        subscription_id="00000000-1111-2222-3333-444444444444",
+    )
+
+    response = client.labeling_jobs.pause(
+        resource_group_name="workspace-1234",
+        workspace_name="testworkspace",
+        id="testLabelingJob",
+    )
+    print(response)
+
+
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/LabelingJob/pause.json
+if __name__ == "__main__":
+    main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/prepare.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/prepare.py
index c6038615acb6a..d7e7e6bbb2835 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/prepare.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/prepare.py
@@ -36,6 +36,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Notebook/prepare.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Notebook/prepare.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/regenerate_keys.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/regenerate_keys.py
index 7e4eb60a2634b..d6a072205d5e6 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/regenerate_keys.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/regenerate_keys.py
@@ -38,6 +38,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/OnlineEndpoint/regenerateKeys.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Workspace/OnlineEndpoint/regenerateKeys.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/restart.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/restart.py
index df42109f655d8..75055ebb57db6 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/restart.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/restart.py
@@ -37,6 +37,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Compute/restart.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Compute/restart.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/resume.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/resume.py
new file mode 100644
index 0000000000000..e269d6c66b0c0
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/resume.py
@@ -0,0 +1,42 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+from azure.identity import DefaultAzureCredential
+from azure.mgmt.machinelearningservices import MachineLearningServicesMgmtClient
+
+"""
+# PREREQUISITES
+    pip install azure-identity
+    pip install azure-mgmt-machinelearningservices
+# USAGE
+    python resume.py
+
+    Before run the sample, please set the values of the client ID, tenant ID and client secret
+    of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID,
+    AZURE_CLIENT_SECRET. For more info about how to get the value, please see:
+    https://docs.microsoft.com/azure/active-directory/develop/howto-create-service-principal-portal
+"""
+
+
+def main():
+    client = MachineLearningServicesMgmtClient(
+        credential=DefaultAzureCredential(),
+        subscription_id="00000000-1111-2222-3333-444444444444",
+    )
+
+    response = client.labeling_jobs.begin_resume(
+        resource_group_name="workspace-1234",
+        workspace_name="testworkspace",
+        id="testLabelingJob",
+    ).result()
+    print(response)
+
+
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/LabelingJob/resume.json
+if __name__ == "__main__":
+    main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/resync_keys.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/resync_keys.py
index 02b711c19636e..939d3e9e56266 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/resync_keys.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/resync_keys.py
@@ -36,6 +36,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Workspace/resyncKeys.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Workspace/resyncKeys.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/start.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/start.py
index b2754e281b74d..7f493b567b248 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/start.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/start.py
@@ -37,6 +37,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Compute/start.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Compute/start.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/stop.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/stop.py
index 4337f64aa1580..c6c37a8b975fb 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/stop.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/stop.py
@@ -37,6 +37,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Compute/stop.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Compute/stop.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/update.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/update.py
index 482c71b07c8c2..712efbfac6627 100644
--- a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/update.py
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/update.py
@@ -43,6 +43,6 @@ def main():
     print(response)
 
 
-# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2022-10-01/examples/Workspace/update.json
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Workspace/update.json
 if __name__ == "__main__":
     main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/update_custom_services.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/update_custom_services.py
new file mode 100644
index 0000000000000..b8c726b64bce4
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/update_custom_services.py
@@ -0,0 +1,58 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+from azure.identity import DefaultAzureCredential
+from azure.mgmt.machinelearningservices import MachineLearningServicesMgmtClient
+
+"""
+# PREREQUISITES
+    pip install azure-identity
+    pip install azure-mgmt-machinelearningservices
+# USAGE
+    python update_custom_services.py
+
+    Before run the sample, please set the values of the client ID, tenant ID and client secret
+    of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID,
+    AZURE_CLIENT_SECRET. For more info about how to get the value, please see:
+    https://docs.microsoft.com/azure/active-directory/develop/howto-create-service-principal-portal
+"""
+
+
+def main():
+    client = MachineLearningServicesMgmtClient(
+        credential=DefaultAzureCredential(),
+        subscription_id="34adfa4f-cedf-4dc0-ba29-b6d1a69ab345",
+    )
+
+    response = client.compute.update_custom_services(
+        resource_group_name="testrg123",
+        workspace_name="workspaces123",
+        compute_name="compute123",
+        custom_services=[
+            {
+                "docker": {"privileged": True},
+                "endpoints": [
+                    {"hostIp": None, "name": "connect", "protocol": "http", "published": 4444, "target": 8787}
+                ],
+                "environmentVariables": {
+                    "RSP_LICENSE": {"type": "local", "value": "XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX"}
+                },
+                "image": {"reference": "ghcr.io/azure/rstudio-workbench:latest", "type": "docker"},
+                "name": "rstudio-workbench",
+                "volumes": [
+                    {"readOnly": True, "source": "/mnt/azureuser/", "target": "/home/testuser/", "type": "bind"}
+                ],
+            }
+        ],
+    )
+    print(response)
+
+
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Compute/updateCustomServices.json
+if __name__ == "__main__":
+    main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/update_idle_shutdown_setting.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/update_idle_shutdown_setting.py
new file mode 100644
index 0000000000000..678a012a40b4e
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/update_idle_shutdown_setting.py
@@ -0,0 +1,43 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+from azure.identity import DefaultAzureCredential
+from azure.mgmt.machinelearningservices import MachineLearningServicesMgmtClient
+
+"""
+# PREREQUISITES
+    pip install azure-identity
+    pip install azure-mgmt-machinelearningservices
+# USAGE
+    python update_idle_shutdown_setting.py
+
+    Before run the sample, please set the values of the client ID, tenant ID and client secret
+    of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID,
+    AZURE_CLIENT_SECRET. For more info about how to get the value, please see:
+    https://docs.microsoft.com/azure/active-directory/develop/howto-create-service-principal-portal
+"""
+
+
+def main():
+    client = MachineLearningServicesMgmtClient(
+        credential=DefaultAzureCredential(),
+        subscription_id="34adfa4f-cedf-4dc0-ba29-b6d1a69ab345",
+    )
+
+    response = client.compute.update_idle_shutdown_setting(
+        resource_group_name="testrg123",
+        workspace_name="workspaces123",
+        compute_name="compute123",
+        parameters={"idleTimeBeforeShutdown": "PT120M"},
+    )
+    print(response)
+
+
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Compute/updateIdleShutdownSetting.json
+if __name__ == "__main__":
+    main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/update_system_created.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/update_system_created.py
new file mode 100644
index 0000000000000..5908d28493b28
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/update_system_created.py
@@ -0,0 +1,48 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+from azure.identity import DefaultAzureCredential
+from azure.mgmt.machinelearningservices import MachineLearningServicesMgmtClient
+
+"""
+# PREREQUISITES
+    pip install azure-identity
+    pip install azure-mgmt-machinelearningservices
+# USAGE
+    python update_system_created.py
+
+    Before run the sample, please set the values of the client ID, tenant ID and client secret
+    of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID,
+    AZURE_CLIENT_SECRET. For more info about how to get the value, please see:
+    https://docs.microsoft.com/azure/active-directory/develop/howto-create-service-principal-portal
+"""
+
+
+def main():
+    client = MachineLearningServicesMgmtClient(
+        credential=DefaultAzureCredential(),
+        subscription_id="00000000-1111-2222-3333-444444444444",
+    )
+
+    response = client.registries.begin_update(
+        resource_group_name="test-rg",
+        registry_name="string",
+        body={
+            "identity": {"type": "UserAssigned", "userAssignedIdentities": {"string": {}}},
+            "kind": "string",
+            "properties": {},
+            "sku": {"capacity": 1, "family": "string", "name": "string", "size": "string", "tier": "Premium"},
+            "tags": {},
+        },
+    ).result()
+    print(response)
+
+
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Registries/update-SystemCreated.json
+if __name__ == "__main__":
+    main()
diff --git a/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/update_user_created.py b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/update_user_created.py
new file mode 100644
index 0000000000000..7a432c8f90101
--- /dev/null
+++ b/sdk/machinelearning/azure-mgmt-machinelearningservices/generated_samples/update_user_created.py
@@ -0,0 +1,48 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) AutoRest Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+
+from azure.identity import DefaultAzureCredential
+from azure.mgmt.machinelearningservices import MachineLearningServicesMgmtClient
+
+"""
+# PREREQUISITES
+    pip install azure-identity
+    pip install azure-mgmt-machinelearningservices
+# USAGE
+    python update_user_created.py
+
+    Before run the sample, please set the values of the client ID, tenant ID and client secret
+    of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID,
+    AZURE_CLIENT_SECRET. For more info about how to get the value, please see:
+    https://docs.microsoft.com/azure/active-directory/develop/howto-create-service-principal-portal
+"""
+
+
+def main():
+    client = MachineLearningServicesMgmtClient(
+        credential=DefaultAzureCredential(),
+        subscription_id="00000000-1111-2222-3333-444444444444",
+    )
+
+    response = client.registries.begin_update(
+        resource_group_name="test-rg",
+        registry_name="string",
+        body={
+            "identity": {"type": "UserAssigned", "userAssignedIdentities": {"string": {}}},
+            "kind": "string",
+            "properties": {},
+            "sku": {"capacity": 1, "family": "string", "name": "string", "size": "string", "tier": "Premium"},
+            "tags": {},
+        },
+    ).result()
+    print(response)
+
+
+# x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/preview/2022-12-01-preview/examples/Registries/update-UserCreated.json
+if __name__ == "__main__":
+    main()