feat: add model_monitoring_config to BatchPredictionJob in aiplatform v1beta1 batch_prediction_job.proto

google/cloud/aiplatform_v1beta1/__init__.py

@@ -344,6 +344,7 @@
 from .types.model_evaluation import ModelEvaluation
 from .types.model_evaluation_slice import ModelEvaluationSlice
 from .types.model_monitoring import ModelMonitoringAlertConfig
+from .types.model_monitoring import ModelMonitoringConfig
 from .types.model_monitoring import ModelMonitoringObjectiveConfig
 from .types.model_monitoring import SamplingStrategy
 from .types.model_monitoring import ThresholdConfig
@@ -841,6 +842,7 @@
     "ModelEvaluationSlice",
     "ModelExplanation",
     "ModelMonitoringAlertConfig",
+    "ModelMonitoringConfig",
     "ModelMonitoringObjectiveConfig",
     "ModelMonitoringStatsAnomalies",
     "ModelServiceClient",

google/cloud/aiplatform_v1beta1/types/__init__.py

@@ -395,6 +395,7 @@
 )
 from .model_monitoring import (
     ModelMonitoringAlertConfig,
+    ModelMonitoringConfig,
     ModelMonitoringObjectiveConfig,
     SamplingStrategy,
     ThresholdConfig,
@@ -878,6 +879,7 @@
     "ModelEvaluation",
     "ModelEvaluationSlice",
     "ModelMonitoringAlertConfig",
+    "ModelMonitoringConfig",
     "ModelMonitoringObjectiveConfig",
     "SamplingStrategy",
     "ThresholdConfig",

google/cloud/aiplatform_v1beta1/types/batch_prediction_job.py

@@ -26,6 +26,7 @@
 from google.cloud.aiplatform_v1beta1.types import (
     manual_batch_tuning_parameters as gca_manual_batch_tuning_parameters,
 )
+from google.cloud.aiplatform_v1beta1.types import model_monitoring
 from google.cloud.aiplatform_v1beta1.types import (
     unmanaged_container_model as gca_unmanaged_container_model,
 )
@@ -214,6 +215,11 @@ class BatchPredictionJob(proto.Message):
             BatchPredictionJob. If this is set, then all
             resources created by the BatchPredictionJob will
             be encrypted with the provided encryption key.
+        model_monitoring_config (google.cloud.aiplatform_v1beta1.types.ModelMonitoringConfig):
+            Model monitoring config will be used for
+            analysis model behaviors, based on the input and
+            output to the batch prediction job, as well as
+            the provided training dataset.
     """
 
     class InputConfig(proto.Message):
@@ -528,6 +534,11 @@ class OutputInfo(proto.Message):
         number=24,
         message=gca_encryption_spec.EncryptionSpec,
     )
+    model_monitoring_config = proto.Field(
+        proto.MESSAGE,
+        number=26,
+        message=model_monitoring.ModelMonitoringConfig,
+    )
 
 
 __all__ = tuple(sorted(__protobuf__.manifest))

google/cloud/aiplatform_v1beta1/types/model_monitoring.py

@@ -21,6 +21,7 @@
 __protobuf__ = proto.module(
     package="google.cloud.aiplatform.v1beta1",
     manifest={
+        "ModelMonitoringConfig",
         "ModelMonitoringObjectiveConfig",
         "ModelMonitoringAlertConfig",
         "ThresholdConfig",
@@ -29,6 +30,43 @@
 )
 
 
+class ModelMonitoringConfig(proto.Message):
+    r"""Next ID: 5
+
+    Attributes:
+        objective_configs (Sequence[google.cloud.aiplatform_v1beta1.types.ModelMonitoringObjectiveConfig]):
+            Model monitoring objective config.
+        alert_config (google.cloud.aiplatform_v1beta1.types.ModelMonitoringAlertConfig):
+            Model monitoring alert config.
+        analysis_instance_schema_uri (str):
+            YAML schema file uri in Cloud Storage
+            describing the format of a single instance that
+            you want Tensorflow Data Validation (TFDV) to
+            analyze.
+            If there are any data type differences between
+            predict instance and TFDV instance, this field
+            can be used to override the schema. For models
+            trained with Vertex AI, this field must be set
+            as all the fields in predict instance formatted
+            as string.
+    """
+
+    objective_configs = proto.RepeatedField(
+        proto.MESSAGE,
+        number=3,
+        message="ModelMonitoringObjectiveConfig",
+    )
+    alert_config = proto.Field(
+        proto.MESSAGE,
+        number=2,
+        message="ModelMonitoringAlertConfig",
+    )
+    analysis_instance_schema_uri = proto.Field(
+        proto.STRING,
+        number=4,
+    )
+
+
 class ModelMonitoringObjectiveConfig(proto.Message):
     r"""Next ID: 8