diff --git a/sagemaker-pipelines/tabular/abalone_build_train_deploy/sagemaker-pipelines-preprocess-train-evaluate-batch-transform.ipynb b/sagemaker-pipelines/tabular/abalone_build_train_deploy/sagemaker-pipelines-preprocess-train-evaluate-batch-transform.ipynb
index 891f939a6a..d3b68fff80 100644
--- a/sagemaker-pipelines/tabular/abalone_build_train_deploy/sagemaker-pipelines-preprocess-train-evaluate-batch-transform.ipynb
+++ b/sagemaker-pipelines/tabular/abalone_build_train_deploy/sagemaker-pipelines-preprocess-train-evaluate-batch-transform.ipynb
@@ -110,20 +110,25 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "import sys\n",
     "\n",
-    "!{sys.executable} -m pip install \"sagemaker==2.91.1\"\n",
+    "!{sys.executable} -m pip install \"sagemaker>=2.99.0\"\n",
     "\n",
     "import boto3\n",
     "import sagemaker\n",
-    "\n",
+    "from sagemaker.workflow.pipeline_context import PipelineSession\n",
     "\n",
     "sagemaker_session = sagemaker.session.Session()\n",
     "region = sagemaker_session.boto_region_name\n",
     "role = sagemaker.get_execution_role()\n",
+    "pipeline_session = PipelineSession()\n",
     "default_bucket = sagemaker_session.default_bucket()\n",
     "model_package_group_name = f\"AbaloneModelPackageGroupName\""
    ]
@@ -138,7 +143,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "!mkdir -p data"
@@ -147,7 +156,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "local_path = \"data/abalone-dataset.csv\"\n",
@@ -175,7 +188,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "local_path = \"data/abalone-dataset-batch\"\n",
@@ -222,7 +239,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "from sagemaker.workflow.parameters import (\n",
@@ -231,7 +252,6 @@
     "    ParameterFloat,\n",
     ")\n",
     "\n",
-    "\n",
     "processing_instance_count = ParameterInteger(name=\"ProcessingInstanceCount\", default_value=1)\n",
     "instance_type = ParameterString(name=\"TrainingInstanceType\", default_value=\"ml.m5.xlarge\")\n",
     "model_approval_status = ParameterString(\n",
@@ -275,19 +295,27 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
-    "!mkdir -p abalone"
+    "!mkdir -p code"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
-    "%%writefile abalone/preprocessing.py\n",
+    "%%writefile code/preprocessing.py\n",
     "import argparse\n",
     "import os\n",
     "import requests\n",
@@ -394,7 +422,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "from sagemaker.sklearn.processing import SKLearnProcessor\n",
@@ -408,6 +440,7 @@
     "    instance_count=processing_instance_count,\n",
     "    base_job_name=\"sklearn-abalone-process\",\n",
     "    role=role,\n",
+    "    sagemaker_session=pipeline_session,\n",
     ")"
    ]
   },
@@ -415,26 +448,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Finally, use the processor instance to construct a `ProcessingStep`, along with the input and output channels, and the code that runs when the pipeline invokes pipeline execution. This is similar to a processor instance's `run()` method in the Python SDK.\n",
-    "\n",
-    "Note the `input_data` parameters passed into `ProcessingStep` is the input data used in the step. This input data is used by the processor instance when it is run.\n",
+    "Finally, we take the output of the processor's `run` method and pass that as arguments to the `ProcessingStep`. By passing the `pipeline_session` to the `sagemaker_session`, calling `.run()` does not launch the processing job, it returns the arguments needed to run the job as a step in the pipeline.\n",
     "\n",
-    "Also, note the `\"train_data\"` and `\"test_data\"` named channels specified in the output configuration for the processing job. Step `Properties` can be used in subsequent steps and resolve to their runtime values at execution. Specifically, this usage is called out when you define the training step."
+    "Note the `\"train_data\"` and `\"test_data\"` named channels specified in the output configuration for the processing job. Step `Properties` can be used in subsequent steps and resolve to their runtime values at execution. Specifically, this usage is called out when you define the training step."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "from sagemaker.processing import ProcessingInput, ProcessingOutput\n",
     "from sagemaker.workflow.steps import ProcessingStep\n",
     "\n",
-    "\n",
-    "step_process = ProcessingStep(\n",
-    "    name=\"AbaloneProcess\",\n",
-    "    processor=sklearn_processor,\n",
+    "processor_args = sklearn_processor.run(\n",
     "    inputs=[\n",
     "        ProcessingInput(source=input_data, destination=\"/opt/ml/processing/input\"),\n",
     "    ],\n",
@@ -443,8 +475,10 @@
     "        ProcessingOutput(output_name=\"validation\", source=\"/opt/ml/processing/validation\"),\n",
     "        ProcessingOutput(output_name=\"test\", source=\"/opt/ml/processing/test\"),\n",
     "    ],\n",
-    "    code=\"abalone/preprocessing.py\",\n",
-    ")"
+    "    code=\"code/preprocessing.py\",\n",
+    ")\n",
+    "\n",
+    "step_process = ProcessingStep(name=\"AbaloneProcess\", step_args=processor_args)"
    ]
   },
   {
@@ -470,11 +504,15 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "from sagemaker.estimator import Estimator\n",
-    "\n",
+    "from sagemaker.inputs import TrainingInput\n",
     "\n",
     "model_path = f\"s3://{default_bucket}/AbaloneTrain\"\n",
     "image_uri = sagemaker.image_uris.retrieve(\n",
@@ -490,6 +528,7 @@
     "    instance_count=1,\n",
     "    output_path=model_path,\n",
     "    role=role,\n",
+    "    sagemaker_session=pipeline_session,\n",
     ")\n",
     "xgb_train.set_hyperparameters(\n",
     "    objective=\"reg:linear\",\n",
@@ -499,6 +538,21 @@
     "    gamma=4,\n",
     "    min_child_weight=6,\n",
     "    subsample=0.7,\n",
+    ")\n",
+    "\n",
+    "train_args = xgb_train.fit(\n",
+    "    inputs={\n",
+    "        \"train\": TrainingInput(\n",
+    "            s3_data=step_process.properties.ProcessingOutputConfig.Outputs[\"train\"].S3Output.S3Uri,\n",
+    "            content_type=\"text/csv\",\n",
+    "        ),\n",
+    "        \"validation\": TrainingInput(\n",
+    "            s3_data=step_process.properties.ProcessingOutputConfig.Outputs[\n",
+    "                \"validation\"\n",
+    "            ].S3Output.S3Uri,\n",
+    "            content_type=\"text/csv\",\n",
+    "        ),\n",
+    "    }\n",
     ")"
    ]
   },
@@ -506,15 +560,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Finally, use the estimator instance to construct a `TrainingStep` as well as the `properties` of the prior `ProcessingStep` used as input in the `TrainingStep` inputs and the code that's executed when the pipeline invokes the pipeline execution. This is similar to an estimator's `fit` method in the Python SDK.\n",
+    "Finally, we use the output of the estimator's `.fit()` method as arguments to the `TrainingStep`. By passing the `pipeline_session` to the `sagemaker_session`, calling `.fit()` does not launch the training job, it returns the arguments needed to run the job as a step in the pipeline.\n",
     "\n",
-    "Pass in the `S3Uri` of the `\"train_data\"` output channel to the `TrainingStep`. Also, use the other `\"test_data\"` output channel for model evaluation in the pipeline. The `properties` attribute of a Pipeline step matches the object model of the corresponding response of a describe call. These properties can be referenced as placeholder values and are resolved at runtime. For example, the `ProcessingStep` `properties` attribute matches the object model of the [DescribeProcessingJob](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeProcessingJob.html) response object."
+    "Pass in the `S3Uri` of the `\"train_data\"` output channel to the `.fit()` method. Also, use the other `\"test_data\"` output channel for model evaluation in the pipeline. The `properties` attribute of a Pipeline step matches the object model of the corresponding response of a describe call. These properties can be referenced as placeholder values and are resolved at runtime. For example, the `ProcessingStep` `properties` attribute matches the object model of the [DescribeProcessingJob](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeProcessingJob.html) response object."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "from sagemaker.inputs import TrainingInput\n",
@@ -523,19 +581,7 @@
     "\n",
     "step_train = TrainingStep(\n",
     "    name=\"AbaloneTrain\",\n",
-    "    estimator=xgb_train,\n",
-    "    inputs={\n",
-    "        \"train\": TrainingInput(\n",
-    "            s3_data=step_process.properties.ProcessingOutputConfig.Outputs[\"train\"].S3Output.S3Uri,\n",
-    "            content_type=\"text/csv\",\n",
-    "        ),\n",
-    "        \"validation\": TrainingInput(\n",
-    "            s3_data=step_process.properties.ProcessingOutputConfig.Outputs[\n",
-    "                \"validation\"\n",
-    "            ].S3Output.S3Uri,\n",
-    "            content_type=\"text/csv\",\n",
-    "        ),\n",
-    "    },\n",
+    "    step_args=train_args,\n",
     ")"
    ]
   },
@@ -568,10 +614,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
-    "%%writefile abalone/evaluation.py\n",
+    "%%writefile code/evaluation.py\n",
     "import json\n",
     "import pathlib\n",
     "import pickle\n",
@@ -628,7 +678,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "from sagemaker.processing import ScriptProcessor\n",
@@ -641,6 +695,24 @@
     "    instance_count=1,\n",
     "    base_job_name=\"script-abalone-eval\",\n",
     "    role=role,\n",
+    "    sagemaker_session=pipeline_session,\n",
+    ")\n",
+    "\n",
+    "eval_args = script_eval.run(\n",
+    "    inputs=[\n",
+    "        ProcessingInput(\n",
+    "            source=step_train.properties.ModelArtifacts.S3ModelArtifacts,\n",
+    "            destination=\"/opt/ml/processing/model\",\n",
+    "        ),\n",
+    "        ProcessingInput(\n",
+    "            source=step_process.properties.ProcessingOutputConfig.Outputs[\"test\"].S3Output.S3Uri,\n",
+    "            destination=\"/opt/ml/processing/test\",\n",
+    "        ),\n",
+    "    ],\n",
+    "    outputs=[\n",
+    "        ProcessingOutput(output_name=\"evaluation\", source=\"/opt/ml/processing/evaluation\"),\n",
+    "    ],\n",
+    "    code=\"code/evaluation.py\",\n",
     ")"
    ]
   },
@@ -648,7 +720,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Use the processor instance to construct a `ProcessingStep`, along with the input and output channels and the code that runs when the pipeline invokes pipeline execution. This is similar to a processor instance's `run` method in the Python SDK.\n",
+    "Use the processor's arguments returned by `.run()` to construct a `ProcessingStep`, along with the input and output channels and the code that will be executed when the pipeline invokes pipeline execution.\n",
     "\n",
     "Specifically, the `S3ModelArtifacts` from the `step_train` `properties` and the `S3Uri` of the `\"test_data\"` output channel of the `step_process` `properties` are passed as inputs. The `TrainingStep` and `ProcessingStep` `properties` attribute matches the object model of the [DescribeTrainingJob](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeTrainingJob.html) and [DescribeProcessingJob](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeProcessingJob.html) response objects, respectively."
    ]
@@ -656,7 +728,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "from sagemaker.workflow.properties import PropertyFile\n",
@@ -667,21 +743,7 @@
     ")\n",
     "step_eval = ProcessingStep(\n",
     "    name=\"AbaloneEval\",\n",
-    "    processor=script_eval,\n",
-    "    inputs=[\n",
-    "        ProcessingInput(\n",
-    "            source=step_train.properties.ModelArtifacts.S3ModelArtifacts,\n",
-    "            destination=\"/opt/ml/processing/model\",\n",
-    "        ),\n",
-    "        ProcessingInput(\n",
-    "            source=step_process.properties.ProcessingOutputConfig.Outputs[\"test\"].S3Output.S3Uri,\n",
-    "            destination=\"/opt/ml/processing/test\",\n",
-    "        ),\n",
-    "    ],\n",
-    "    outputs=[\n",
-    "        ProcessingOutput(output_name=\"evaluation\", source=\"/opt/ml/processing/evaluation\"),\n",
-    "    ],\n",
-    "    code=\"abalone/evaluation.py\",\n",
+    "    step_args=eval_args,\n",
     "    property_files=[evaluation_report],\n",
     ")"
    ]
@@ -699,7 +761,7 @@
    "source": [
     "## Define a Create Model Step to Create a Model\n",
     "\n",
-    "In order to perform batch transformation using the example model, create a SageMaker model. \n",
+    "In order to perform batch transformation using the example model, create a SageMaker model.\n",
     "\n",
     "Specifically, pass in the `S3ModelArtifacts` from the `TrainingStep`, `step_train` properties. The `TrainingStep` `properties` attribute matches the object model of the [DescribeTrainingJob](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeTrainingJob.html) response object."
    ]
@@ -707,16 +769,19 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "from sagemaker.model import Model\n",
     "\n",
-    "\n",
     "model = Model(\n",
     "    image_uri=image_uri,\n",
     "    model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,\n",
-    "    sagemaker_session=sagemaker_session,\n",
+    "    sagemaker_session=pipeline_session,\n",
     "    role=role,\n",
     ")"
    ]
@@ -725,27 +790,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Supply the model input (`instance_type` and `accelerator_type`) for creating the SageMaker Model, and then define the `CreateModelStep`, passing in the inputs and the model instance defined before."
+    "Define the `ModelStep` by providing the return values from `model.create()` as the step arguments."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "from sagemaker.inputs import CreateModelInput\n",
-    "from sagemaker.workflow.steps import CreateModelStep\n",
-    "\n",
+    "from sagemaker.workflow.model_step import ModelStep\n",
     "\n",
-    "inputs = CreateModelInput(\n",
-    "    instance_type=\"ml.m5.large\",\n",
-    "    accelerator_type=\"ml.eia1.medium\",\n",
-    ")\n",
-    "step_create_model = CreateModelStep(\n",
+    "step_create_model = ModelStep(\n",
     "    name=\"AbaloneCreateModel\",\n",
-    "    model=model,\n",
-    "    inputs=inputs,\n",
+    "    step_args=model.create(instance_type=\"ml.m5.large\", accelerator_type=\"ml.eia1.medium\"),\n",
     ")"
    ]
   },
@@ -763,7 +826,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "from sagemaker.transformer import Transformer\n",
@@ -787,7 +854,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "from sagemaker.inputs import TransformInput\n",
@@ -805,26 +876,35 @@
    "source": [
     "## Define a Register Model Step to Create a Model Package\n",
     "\n",
-    "Use the estimator instance specified in the training step to construct an instance of `RegisterModel`. The result of executing `RegisterModel` in a pipeline is a model package. A model package is an abstraction of reusable model artifacts that packages all ingredients required for inference. Primarily, it consists of an inference specification that defines the inference image to use along with an optional model weights location.\n",
+    "A model package is an abstraction of reusable model artifacts that packages all ingredients required for inference. Primarily, it consists of an inference specification that defines the inference image to use along with an optional model weights location.\n",
     "\n",
     "A model package group is a collection of model packages. A model package group can be created for a specific ML business problem, and new versions of the model packages can be added to it. Typically, customers are expected to create a ModelPackageGroup for a SageMaker pipeline so that model package versions can be added to the group for every SageMaker Pipeline run.\n",
     "\n",
-    "The construction of `RegisterModel` is similar to an estimator instance's `register` method in the Python SDK.\n",
-    "\n",
-    "Specifically, pass in the `S3ModelArtifacts` from the `TrainingStep`, `step_train` properties. The `TrainingStep` `properties` attribute matches the object model of the [DescribeTrainingJob](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeTrainingJob.html) response object.\n",
+    "To register a model in the Model Registry, we take the model created in the previous steps\n",
+    "```\n",
+    "model = Model(\n",
+    "    image_uri=image_uri,\n",
+    "    model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,\n",
+    "    sagemaker_session=pipeline_session,\n",
+    "    role=role,\n",
+    ")\n",
+    "```\n",
+    "and call the `.register()` function on it while passing all the parameters needed for registering the model.\n",
     "\n",
-    "Note that the specific model package group name provided in this notebook can be used in the model registry and CI/CD work with SageMaker Projects."
+    "We take the outputs of the `.register()` call and pass that to the `ModelStep` as step arguments."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "from sagemaker.model_metrics import MetricsSource, ModelMetrics\n",
-    "from sagemaker.workflow.step_collections import RegisterModel\n",
-    "\n",
     "\n",
     "model_metrics = ModelMetrics(\n",
     "    model_statistics=MetricsSource(\n",
@@ -834,10 +914,8 @@
     "        content_type=\"application/json\",\n",
     "    )\n",
     ")\n",
-    "step_register = RegisterModel(\n",
-    "    name=\"AbaloneRegisterModel\",\n",
-    "    estimator=xgb_train,\n",
-    "    model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,\n",
+    "\n",
+    "register_args = model.register(\n",
     "    content_types=[\"text/csv\"],\n",
     "    response_types=[\"text/csv\"],\n",
     "    inference_instances=[\"ml.t2.medium\", \"ml.m5.xlarge\"],\n",
@@ -845,7 +923,8 @@
     "    model_package_group_name=model_package_group_name,\n",
     "    approval_status=model_approval_status,\n",
     "    model_metrics=model_metrics,\n",
-    ")"
+    ")\n",
+    "step_register = ModelStep(name=\"AbaloneRegisterModel\", step_args=register_args)"
    ]
   },
   {
@@ -871,9 +950,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "jupyter": {
-     "outputs_hidden": false
-    },
     "pycharm": {
      "name": "#%%\n"
     }
@@ -915,7 +991,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "from sagemaker.workflow.conditions import ConditionLessThanOrEqualTo\n",
@@ -967,7 +1047,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "from sagemaker.workflow.pipeline import Pipeline\n",
@@ -1007,7 +1091,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "import json\n",
@@ -1029,7 +1117,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "pipeline.upsert(role_arn=role)"
@@ -1045,7 +1137,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "execution = pipeline.start()"
@@ -1063,7 +1159,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "execution.describe()"
@@ -1079,7 +1179,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "execution.wait()"
@@ -1095,7 +1199,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "execution.list_steps()"
@@ -1113,7 +1221,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "from pprint import pprint\n",
@@ -1139,7 +1251,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "import time\n",
@@ -1167,7 +1283,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "execution = pipeline.start(\n",
@@ -1180,7 +1300,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "execution.wait()"
@@ -1189,7 +1313,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "execution.list_steps()"
@@ -1206,9 +1334,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "jupyter": {
-     "outputs_hidden": false
-    },
     "pycharm": {
      "name": "#%%\n"
     }
@@ -1229,9 +1354,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "jupyter": {
-     "outputs_hidden": false
-    },
     "pycharm": {
      "name": "#%%\n"
     }
@@ -1248,9 +1370,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "jupyter": {
-     "outputs_hidden": false
-    },
     "pycharm": {
      "name": "#%%\n"
     }
@@ -1283,4 +1402,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/sagemaker-pipelines/tabular/abalone_build_train_deploy/sagemaker-pipelines-preprocess-train-evaluate-batch-transform_outputs.ipynb b/sagemaker-pipelines/tabular/abalone_build_train_deploy/sagemaker-pipelines-preprocess-train-evaluate-batch-transform_outputs.ipynb
index bda9ea6fc2..d20c0f6d83 100644
--- a/sagemaker-pipelines/tabular/abalone_build_train_deploy/sagemaker-pipelines-preprocess-train-evaluate-batch-transform_outputs.ipynb
+++ b/sagemaker-pipelines/tabular/abalone_build_train_deploy/sagemaker-pipelines-preprocess-train-evaluate-batch-transform_outputs.ipynb
@@ -1,14 +1,42 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3905c65f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-07-13T16:07:43.979421Z",
+     "iopub.status.busy": "2022-07-13T16:07:43.978612Z",
+     "iopub.status.idle": "2022-07-13T16:07:43.980773Z",
+     "shell.execute_reply": "2022-07-13T16:07:43.980271Z"
+    },
+    "papermill": {
+     "duration": 0.036422,
+     "end_time": "2022-07-13T16:07:43.980902",
+     "exception": false,
+     "start_time": "2022-07-13T16:07:43.944480",
+     "status": "completed"
+    },
+    "tags": [
+     "injected-parameters"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Parameters\n",
+    "kms_key = \"arn:aws:kms:us-west-2:000000000000:1234abcd-12ab-34cd-56ef-1234567890ab\""
+   ]
+  },
   {
    "cell_type": "markdown",
-   "id": "3a9f8b86",
+   "id": "f207c2b8",
    "metadata": {
     "papermill": {
-     "duration": 0.031042,
-     "end_time": "2022-04-18T00:26:38.898215",
+     "duration": 0.029403,
+     "end_time": "2022-07-13T16:07:44.040408",
      "exception": false,
-     "start_time": "2022-04-18T00:26:38.867173",
+     "start_time": "2022-07-13T16:07:44.011005",
      "status": "completed"
     },
     "tags": []
@@ -49,13 +77,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c052c2e3",
+   "id": "57abefb0",
    "metadata": {
     "papermill": {
-     "duration": 0.031989,
-     "end_time": "2022-04-18T00:26:38.961142",
+     "duration": 0.029368,
+     "end_time": "2022-07-13T16:07:44.099261",
      "exception": false,
-     "start_time": "2022-04-18T00:26:38.929153",
+     "start_time": "2022-07-13T16:07:44.069893",
      "status": "completed"
     },
     "tags": []
@@ -78,13 +106,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "37efa33f",
+   "id": "f8fd6f33",
    "metadata": {
     "papermill": {
-     "duration": 0.034621,
-     "end_time": "2022-04-18T00:26:39.027923",
+     "duration": 0.029618,
+     "end_time": "2022-07-13T16:07:44.158330",
      "exception": false,
-     "start_time": "2022-04-18T00:26:38.993302",
+     "start_time": "2022-07-13T16:07:44.128712",
      "status": "completed"
     },
     "tags": []
@@ -111,13 +139,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b6ae16a6",
+   "id": "38772d5a",
    "metadata": {
     "papermill": {
-     "duration": 0.031533,
-     "end_time": "2022-04-18T00:26:39.090351",
+     "duration": 0.029536,
+     "end_time": "2022-07-13T16:07:44.217483",
      "exception": false,
-     "start_time": "2022-04-18T00:26:39.058818",
+     "start_time": "2022-07-13T16:07:44.187947",
      "status": "completed"
     },
     "tags": []
@@ -132,13 +160,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "171887b6",
+   "id": "9607841c",
    "metadata": {
     "papermill": {
-     "duration": 0.03037,
-     "end_time": "2022-04-18T00:26:39.151023",
+     "duration": 0.029684,
+     "end_time": "2022-07-13T16:07:44.276589",
      "exception": false,
-     "start_time": "2022-04-18T00:26:39.120653",
+     "start_time": "2022-07-13T16:07:44.246905",
      "status": "completed"
     },
     "tags": []
@@ -152,7 +180,7 @@
     "\n",
     "The number of rings turns out to be a good approximation for age (age is rings + 1.5). However, to obtain this number requires cutting the shell through the cone, staining the section, and counting the number of rings through a microscope, which is a time-consuming task. However, the other physical measurements are easier to determine. You use the dataset to build a predictive model of the variable rings through these other physical measurements.\n",
     "\n",
-    "Before you upload the data to an S3 bucket, upgrade the [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/) to the latest version and gather some constants you can use later in this notebook.\n",
+    "Before you upload the data to an S3 bucket, install the SageMaker Python SDK and gather some constants you can use later in this notebook.\n",
     "\n",
     "> [1] Dua, D. and Graff, C. (2019). [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml). Irvine, CA: University of California, School of Information and Computer Science."
    ]
@@ -160,21 +188,24 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "017a6796",
+   "id": "ef441354",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:26:39.217502Z",
-     "iopub.status.busy": "2022-04-18T00:26:39.216724Z",
-     "iopub.status.idle": "2022-04-18T00:26:58.137345Z",
-     "shell.execute_reply": "2022-04-18T00:26:58.136909Z"
+     "iopub.execute_input": "2022-07-13T16:07:44.382810Z",
+     "iopub.status.busy": "2022-07-13T16:07:44.382069Z",
+     "iopub.status.idle": "2022-07-13T16:07:48.212159Z",
+     "shell.execute_reply": "2022-07-13T16:07:48.211713Z"
     },
     "papermill": {
-     "duration": 18.956378,
-     "end_time": "2022-04-18T00:26:58.137468",
+     "duration": 3.905415,
+     "end_time": "2022-07-13T16:07:48.212278",
      "exception": false,
-     "start_time": "2022-04-18T00:26:39.181090",
+     "start_time": "2022-07-13T16:07:44.306863",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [
@@ -192,1261 +223,87 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Requirement already satisfied: sagemaker in /opt/conda/lib/python3.7/site-packages (2.69.1.dev0)\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Collecting sagemaker\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  Downloading sagemaker-2.86.2.tar.gz (521 kB)\r\n",
-      "\u001b[?25l\r",
-      "\u001b[K     |▋                               | 10 kB 29.8 MB/s eta 0:00:01\r",
-      "\u001b[K     |█▎                              | 20 kB 22.8 MB/s eta 0:00:01\r",
-      "\u001b[K     |█▉                              | 30 kB 16.7 MB/s eta 0:00:01\r",
-      "\u001b[K     |██▌                             | 40 kB 7.1 MB/s eta 0:00:01\r",
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-      "\u001b[K     |███▊                            | 61 kB 7.3 MB/s eta 0:00:01"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "\u001b[K     |████▍                           | 71 kB 7.4 MB/s eta 0:00:01\r",
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-      "\u001b[?25h"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Collecting attrs==20.3.0\r\n",
-      "  Downloading attrs-20.3.0-py2.py3-none-any.whl (49 kB)\r\n",
-      "\u001b[?25l\r",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "\u001b[K     |████████████████████████████████| 49 kB 8.4 MB/s \r\n",
-      "\u001b[?25h"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Collecting boto3>=1.20.21\r\n",
-      "  Downloading boto3-1.21.42-py3-none-any.whl (132 kB)\r\n",
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-     ]
-    },
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-     "name": "stdout",
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-     ]
-    },
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-      "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /opt/conda/lib/python3.7/site-packages (from boto3>=1.20.21->sagemaker) (0.10.0)\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Collecting botocore<1.25.0,>=1.24.42\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  Downloading botocore-1.24.42-py3-none-any.whl (8.7 MB)\r\n",
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      "output_type": "stream",
      "text": [
-      "\b \b\\"
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-      "\b \b|"
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-      "\b \b/"
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-      "\b \b-"
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-      "\b \b|"
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-      "\b \b/"
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-      "\b \bdone\r\n",
-      "\u001b[?25h"
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-     "text": [
-      "  Created wheel for sagemaker: filename=sagemaker-2.86.2-py2.py3-none-any.whl size=720870 sha256=30eb3c1cca0f6a5480b9654912669d9e879aee733c91bd63d8a9addf75998a4d\r\n",
-      "  Stored in directory: /root/.cache/pip/wheels/ea/94/35/1b815c5bdf63f8947160a8e6a4eb12f4bb05bd6f9cc773176a\r\n",
-      "Successfully built sagemaker\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Installing collected packages: botocore, boto3, attrs, sagemaker\r\n",
-      "  Attempting uninstall: botocore\r\n",
-      "    Found existing installation: botocore 1.23.7\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    Uninstalling botocore-1.23.7:\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "      Successfully uninstalled botocore-1.23.7\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  Attempting uninstall: boto3\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    Found existing installation: boto3 1.20.7\r\n",
-      "    Uninstalling boto3-1.20.7:\r\n",
-      "      Successfully uninstalled boto3-1.20.7\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  Attempting uninstall: attrs\r\n",
-      "    Found existing installation: attrs 19.3.0\r\n",
-      "    Uninstalling attrs-19.3.0:\r\n",
-      "      Successfully uninstalled attrs-19.3.0\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  Attempting uninstall: sagemaker\r\n",
-      "    Found existing installation: sagemaker 2.69.1.dev0\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    Uninstalling sagemaker-2.69.1.dev0:\r\n",
-      "      Successfully uninstalled sagemaker-2.69.1.dev0\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n",
-      "pytest-astropy 0.8.0 requires pytest-cov>=2.0, which is not installed.\r\n",
-      "pytest-astropy 0.8.0 requires pytest-filter-subpackage>=0.1, which is not installed.\r\n",
-      "awscli 1.22.7 requires botocore==1.23.7, but you have botocore 1.24.42 which is incompatible.\r\n",
-      "aiobotocore 1.3.3 requires botocore<1.20.107,>=1.20.106, but you have botocore 1.24.42 which is incompatible.\u001b[0m\r\n",
-      "Successfully installed attrs-20.3.0 boto3-1.21.42 botocore-1.24.42 sagemaker-2.86.2\r\n",
       "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n",
-      "\u001b[33mWARNING: You are using pip version 21.1.3; however, version 22.0.4 is available.\r\n",
+      "\u001b[33mWARNING: You are using pip version 21.1.3; however, version 22.1.2 is available.\r\n",
       "You should consider upgrading via the '/opt/conda/bin/python -m pip install --upgrade pip' command.\u001b[0m\r\n"
      ]
     }
    ],
    "source": [
-    "!pip install --upgrade sagemaker\n",
+    "import sys\n",
+    "\n",
+    "!{sys.executable} -m pip install \"sagemaker>=2.99.0\"\n",
     "\n",
     "import boto3\n",
     "import sagemaker\n",
-    "\n",
+    "from sagemaker.workflow.pipeline_context import PipelineSession\n",
     "\n",
     "sagemaker_session = sagemaker.session.Session()\n",
     "region = sagemaker_session.boto_region_name\n",
     "role = sagemaker.get_execution_role()\n",
+    "pipeline_session = PipelineSession()\n",
     "default_bucket = sagemaker_session.default_bucket()\n",
     "model_package_group_name = f\"AbaloneModelPackageGroupName\""
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0e125406",
+   "id": "c03f70f4",
    "metadata": {
     "papermill": {
-     "duration": 0.04072,
-     "end_time": "2022-04-18T00:26:58.219587",
+     "duration": 0.031185,
+     "end_time": "2022-07-13T16:07:48.274459",
      "exception": false,
-     "start_time": "2022-04-18T00:26:58.178867",
+     "start_time": "2022-07-13T16:07:48.243274",
      "status": "completed"
     },
     "tags": []
@@ -1458,21 +315,24 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "9ba7d402",
+   "id": "f15c8059",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:26:58.444676Z",
-     "iopub.status.busy": "2022-04-18T00:26:58.443960Z",
-     "iopub.status.idle": "2022-04-18T00:26:58.740397Z",
-     "shell.execute_reply": "2022-04-18T00:26:58.740762Z"
+     "iopub.execute_input": "2022-07-13T16:07:48.346589Z",
+     "iopub.status.busy": "2022-07-13T16:07:48.341539Z",
+     "iopub.status.idle": "2022-07-13T16:07:48.497993Z",
+     "shell.execute_reply": "2022-07-13T16:07:48.497482Z"
     },
     "papermill": {
-     "duration": 0.478884,
-     "end_time": "2022-04-18T00:26:58.740911",
+     "duration": 0.19199,
+     "end_time": "2022-07-13T16:07:48.498111",
      "exception": false,
-     "start_time": "2022-04-18T00:26:58.262027",
+     "start_time": "2022-07-13T16:07:48.306121",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [],
@@ -1483,21 +343,24 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "deb34a1d",
+   "id": "8ff00b12",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:26:58.932598Z",
-     "iopub.status.busy": "2022-04-18T00:26:58.931487Z",
-     "iopub.status.idle": "2022-04-18T00:26:59.983116Z",
-     "shell.execute_reply": "2022-04-18T00:26:59.983495Z"
+     "iopub.execute_input": "2022-07-13T16:07:48.575181Z",
+     "iopub.status.busy": "2022-07-13T16:07:48.574347Z",
+     "iopub.status.idle": "2022-07-13T16:07:49.570833Z",
+     "shell.execute_reply": "2022-07-13T16:07:49.571230Z"
     },
     "papermill": {
-     "duration": 1.20106,
-     "end_time": "2022-04-18T00:26:59.983639",
+     "duration": 1.041888,
+     "end_time": "2022-07-13T16:07:49.571370",
      "exception": false,
-     "start_time": "2022-04-18T00:26:58.782579",
+     "start_time": "2022-07-13T16:07:48.529482",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [
@@ -1527,13 +390,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f19a382b",
+   "id": "68c6ec51",
    "metadata": {
     "papermill": {
-     "duration": 0.042061,
-     "end_time": "2022-04-18T00:27:00.067912",
+     "duration": 0.033529,
+     "end_time": "2022-07-13T16:07:49.637761",
      "exception": false,
-     "start_time": "2022-04-18T00:27:00.025851",
+     "start_time": "2022-07-13T16:07:49.604232",
      "status": "completed"
     },
     "tags": []
@@ -1545,21 +408,24 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "3ac709e8",
+   "id": "f671e801",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:27:00.165097Z",
-     "iopub.status.busy": "2022-04-18T00:27:00.164270Z",
-     "iopub.status.idle": "2022-04-18T00:27:00.440584Z",
-     "shell.execute_reply": "2022-04-18T00:27:00.444404Z"
+     "iopub.execute_input": "2022-07-13T16:07:49.722364Z",
+     "iopub.status.busy": "2022-07-13T16:07:49.719824Z",
+     "iopub.status.idle": "2022-07-13T16:07:50.203802Z",
+     "shell.execute_reply": "2022-07-13T16:07:50.208095Z"
     },
     "papermill": {
-     "duration": 0.332017,
-     "end_time": "2022-04-18T00:27:00.444577",
+     "duration": 0.530874,
+     "end_time": "2022-07-13T16:07:50.208271",
      "exception": false,
-     "start_time": "2022-04-18T00:27:00.112560",
+     "start_time": "2022-07-13T16:07:49.677397",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [
@@ -1589,13 +455,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cdb20305",
+   "id": "345b427e",
    "metadata": {
     "papermill": {
-     "duration": 0.05191,
-     "end_time": "2022-04-18T00:27:00.557073",
+     "duration": 0.054012,
+     "end_time": "2022-07-13T16:07:50.311578",
      "exception": false,
-     "start_time": "2022-04-18T00:27:00.505163",
+     "start_time": "2022-07-13T16:07:50.257566",
      "status": "completed"
     },
     "tags": []
@@ -1615,7 +481,6 @@
     "\n",
     "The parameters defined in this workflow include:\n",
     "\n",
-    "* `processing_instance_type` - The `ml.*` instance type of the processing job.\n",
     "* `processing_instance_count` - The instance count of the processing job.\n",
     "* `instance_type` - The `ml.*` instance type of the training job.\n",
     "* `model_approval_status` - The approval status to register with the trained model for CI/CD purposes (\"PendingManualApproval\" is the default).\n",
@@ -1627,21 +492,24 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "177ff1bd",
+   "id": "b730dc01",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:27:00.760136Z",
-     "iopub.status.busy": "2022-04-18T00:27:00.759034Z",
-     "iopub.status.idle": "2022-04-18T00:27:00.761022Z",
-     "shell.execute_reply": "2022-04-18T00:27:00.761444Z"
+     "iopub.execute_input": "2022-07-13T16:07:50.422392Z",
+     "iopub.status.busy": "2022-07-13T16:07:50.421597Z",
+     "iopub.status.idle": "2022-07-13T16:07:50.423907Z",
+     "shell.execute_reply": "2022-07-13T16:07:50.423467Z"
     },
     "papermill": {
-     "duration": 0.118215,
-     "end_time": "2022-04-18T00:27:00.761626",
+     "duration": 0.058807,
+     "end_time": "2022-07-13T16:07:50.424032",
      "exception": false,
-     "start_time": "2022-04-18T00:27:00.643411",
+     "start_time": "2022-07-13T16:07:50.365225",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [],
@@ -1652,11 +520,7 @@
     "    ParameterFloat,\n",
     ")\n",
     "\n",
-    "\n",
     "processing_instance_count = ParameterInteger(name=\"ProcessingInstanceCount\", default_value=1)\n",
-    "processing_instance_type = ParameterString(\n",
-    "    name=\"ProcessingInstanceType\", default_value=\"ml.m5.xlarge\"\n",
-    ")\n",
     "instance_type = ParameterString(name=\"TrainingInstanceType\", default_value=\"ml.m5.xlarge\")\n",
     "model_approval_status = ParameterString(\n",
     "    name=\"ModelApprovalStatus\", default_value=\"PendingManualApproval\"\n",
@@ -1674,13 +538,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7db46ee8",
+   "id": "2113cab9",
    "metadata": {
     "papermill": {
-     "duration": 0.104152,
-     "end_time": "2022-04-18T00:27:01.035968",
+     "duration": 0.061565,
+     "end_time": "2022-07-13T16:07:50.532413",
      "exception": false,
-     "start_time": "2022-04-18T00:27:00.931816",
+     "start_time": "2022-07-13T16:07:50.470848",
      "status": "completed"
     },
     "tags": []
@@ -1691,13 +555,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cd4a939f",
+   "id": "0279c19d",
    "metadata": {
     "papermill": {
-     "duration": 0.178766,
-     "end_time": "2022-04-18T00:27:01.330422",
+     "duration": 0.034918,
+     "end_time": "2022-07-13T16:07:50.601674",
      "exception": false,
-     "start_time": "2022-04-18T00:27:01.151656",
+     "start_time": "2022-07-13T16:07:50.566756",
      "status": "completed"
     },
     "tags": []
@@ -1719,46 +583,52 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "e84a2b1c",
+   "id": "8b07a322",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:27:01.631001Z",
-     "iopub.status.busy": "2022-04-18T00:27:01.630119Z",
-     "iopub.status.idle": "2022-04-18T00:27:01.854003Z",
-     "shell.execute_reply": "2022-04-18T00:27:01.850909Z"
+     "iopub.execute_input": "2022-07-13T16:07:50.679648Z",
+     "iopub.status.busy": "2022-07-13T16:07:50.678977Z",
+     "iopub.status.idle": "2022-07-13T16:07:50.827765Z",
+     "shell.execute_reply": "2022-07-13T16:07:50.827361Z"
     },
     "papermill": {
-     "duration": 0.399913,
-     "end_time": "2022-04-18T00:27:01.854610",
+     "duration": 0.19357,
+     "end_time": "2022-07-13T16:07:50.827884",
      "exception": false,
-     "start_time": "2022-04-18T00:27:01.454697",
+     "start_time": "2022-07-13T16:07:50.634314",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [],
    "source": [
-    "!mkdir -p abalone"
+    "!mkdir -p code"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "6e1a780d",
+   "id": "3901891e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:27:02.136534Z",
-     "iopub.status.busy": "2022-04-18T00:27:02.135485Z",
-     "iopub.status.idle": "2022-04-18T00:27:02.139256Z",
-     "shell.execute_reply": "2022-04-18T00:27:02.139664Z"
+     "iopub.execute_input": "2022-07-13T16:07:50.900839Z",
+     "iopub.status.busy": "2022-07-13T16:07:50.899665Z",
+     "iopub.status.idle": "2022-07-13T16:07:50.902508Z",
+     "shell.execute_reply": "2022-07-13T16:07:50.902925Z"
     },
     "papermill": {
-     "duration": 0.107493,
-     "end_time": "2022-04-18T00:27:02.139838",
+     "duration": 0.043198,
+     "end_time": "2022-07-13T16:07:50.903065",
      "exception": false,
-     "start_time": "2022-04-18T00:27:02.032345",
+     "start_time": "2022-07-13T16:07:50.859867",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [
@@ -1766,12 +636,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Writing abalone/preprocessing.py\n"
+      "Writing code/preprocessing.py\n"
      ]
     }
    ],
    "source": [
-    "%%writefile abalone/preprocessing.py\n",
+    "%%writefile code/preprocessing.py\n",
     "import argparse\n",
     "import os\n",
     "import requests\n",
@@ -1866,13 +736,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e529d9c0",
+   "id": "753400dc",
    "metadata": {
     "papermill": {
-     "duration": 0.158348,
-     "end_time": "2022-04-18T00:27:02.341041",
+     "duration": 0.03212,
+     "end_time": "2022-07-13T16:07:50.967490",
      "exception": false,
-     "start_time": "2022-04-18T00:27:02.182693",
+     "start_time": "2022-07-13T16:07:50.935370",
      "status": "completed"
     },
     "tags": []
@@ -1882,27 +752,30 @@
     "\n",
     "You also specify the `framework_version` to use throughout this notebook.\n",
     "\n",
-    "Note the `processing_instance_type` and `processing_instance_count` parameters used by the processor instance."
+    "Note the `processing_instance_count` parameter used by the processor instance."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "22bd2e33",
+   "id": "c3563172",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:27:02.542297Z",
-     "iopub.status.busy": "2022-04-18T00:27:02.541393Z",
-     "iopub.status.idle": "2022-04-18T00:27:02.646852Z",
-     "shell.execute_reply": "2022-04-18T00:27:02.646034Z"
+     "iopub.execute_input": "2022-07-13T16:07:51.037823Z",
+     "iopub.status.busy": "2022-07-13T16:07:51.037000Z",
+     "iopub.status.idle": "2022-07-13T16:07:51.055494Z",
+     "shell.execute_reply": "2022-07-13T16:07:51.055848Z"
     },
     "papermill": {
-     "duration": 0.206931,
-     "end_time": "2022-04-18T00:27:02.646979",
+     "duration": 0.056125,
+     "end_time": "2022-07-13T16:07:51.055993",
      "exception": false,
-     "start_time": "2022-04-18T00:27:02.440048",
+     "start_time": "2022-07-13T16:07:50.999868",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [],
@@ -1910,67 +783,85 @@
     "from sagemaker.sklearn.processing import SKLearnProcessor\n",
     "\n",
     "\n",
-    "framework_version = \"1.0-1\"\n",
+    "framework_version = \"0.23-1\"\n",
     "\n",
     "sklearn_processor = SKLearnProcessor(\n",
     "    framework_version=framework_version,\n",
-    "    instance_type=processing_instance_type,\n",
+    "    instance_type=\"ml.m5.xlarge\",\n",
     "    instance_count=processing_instance_count,\n",
     "    base_job_name=\"sklearn-abalone-process\",\n",
     "    role=role,\n",
+    "    sagemaker_session=pipeline_session,\n",
     ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "1757d287",
+   "id": "93a1d463",
    "metadata": {
     "papermill": {
-     "duration": 0.106575,
-     "end_time": "2022-04-18T00:27:02.859741",
+     "duration": 0.032682,
+     "end_time": "2022-07-13T16:07:51.121085",
      "exception": false,
-     "start_time": "2022-04-18T00:27:02.753166",
+     "start_time": "2022-07-13T16:07:51.088403",
      "status": "completed"
     },
     "tags": []
    },
    "source": [
-    "Finally, use the processor instance to construct a `ProcessingStep`, along with the input and output channels, and the code that runs when the pipeline invokes pipeline execution. This is similar to a processor instance's `run()` method in the Python SDK.\n",
-    "\n",
-    "Note the `input_data` parameters passed into `ProcessingStep` is the input data used in the step. This input data is used by the processor instance when it is run.\n",
+    "Finally, we take the output of the processor's `run` method and pass that as arguments to the `ProcessingStep`. By passing the `pipeline_session` to the `sagemaker_session`, calling `.run()` does not launch the processing job, it returns the arguments needed to run the job as a step in the pipeline.\n",
     "\n",
-    "Also, note the `\"train_data\"` and `\"test_data\"` named channels specified in the output configuration for the processing job. Step `Properties` can be used in subsequent steps and resolve to their runtime values at execution. Specifically, this usage is called out when you define the training step."
+    "Note the `\"train_data\"` and `\"test_data\"` named channels specified in the output configuration for the processing job. Step `Properties` can be used in subsequent steps and resolve to their runtime values at execution. Specifically, this usage is called out when you define the training step."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "7cad2493",
+   "id": "240281be",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:27:03.136272Z",
-     "iopub.status.busy": "2022-04-18T00:27:03.135400Z",
-     "iopub.status.idle": "2022-04-18T00:27:03.146315Z",
-     "shell.execute_reply": "2022-04-18T00:27:03.145802Z"
+     "iopub.execute_input": "2022-07-13T16:07:51.192383Z",
+     "iopub.status.busy": "2022-07-13T16:07:51.191558Z",
+     "iopub.status.idle": "2022-07-13T16:07:51.548107Z",
+     "shell.execute_reply": "2022-07-13T16:07:51.548453Z"
     },
     "papermill": {
-     "duration": 0.180929,
-     "end_time": "2022-04-18T00:27:03.146454",
+     "duration": 0.39512,
+     "end_time": "2022-07-13T16:07:51.548594",
      "exception": false,
-     "start_time": "2022-04-18T00:27:02.965525",
+     "start_time": "2022-07-13T16:07:51.153474",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.7/site-packages/sagemaker/workflow/pipeline_context.py:197: UserWarning: Running within a PipelineSession, there will be No Wait, No Logs, and No Job being started.\n",
+      "  UserWarning,\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Job Name:  sklearn-abalone-process-2022-07-13-16-07-51-228\n",
+      "Inputs:  [{'InputName': 'input-1', 'AppManaged': False, 'S3Input': {'S3Uri': ParameterString(name='InputData', parameter_type=<ParameterTypeEnum.STRING: 'String'>, default_value='s3://sagemaker-us-west-2-000000000000/abalone/abalone-dataset.csv'), 'LocalPath': '/opt/ml/processing/input', 'S3DataType': 'S3Prefix', 'S3InputMode': 'File', 'S3DataDistributionType': 'FullyReplicated', 'S3CompressionType': 'None'}}, {'InputName': 'code', 'AppManaged': False, 'S3Input': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/sklearn-abalone-process-2022-07-13-16-07-51-228/input/code/preprocessing.py', 'LocalPath': '/opt/ml/processing/input/code', 'S3DataType': 'S3Prefix', 'S3InputMode': 'File', 'S3DataDistributionType': 'FullyReplicated', 'S3CompressionType': 'None'}}]\n",
+      "Outputs:  [{'OutputName': 'train', 'AppManaged': False, 'S3Output': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/sklearn-abalone-process-2022-07-13-16-07-51-228/output/train', 'LocalPath': '/opt/ml/processing/train', 'S3UploadMode': 'EndOfJob'}}, {'OutputName': 'validation', 'AppManaged': False, 'S3Output': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/sklearn-abalone-process-2022-07-13-16-07-51-228/output/validation', 'LocalPath': '/opt/ml/processing/validation', 'S3UploadMode': 'EndOfJob'}}, {'OutputName': 'test', 'AppManaged': False, 'S3Output': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/sklearn-abalone-process-2022-07-13-16-07-51-228/output/test', 'LocalPath': '/opt/ml/processing/test', 'S3UploadMode': 'EndOfJob'}}]\n"
+     ]
+    }
+   ],
    "source": [
     "from sagemaker.processing import ProcessingInput, ProcessingOutput\n",
     "from sagemaker.workflow.steps import ProcessingStep\n",
     "\n",
-    "\n",
-    "step_process = ProcessingStep(\n",
-    "    name=\"AbaloneProcess\",\n",
-    "    processor=sklearn_processor,\n",
+    "processor_args = sklearn_processor.run(\n",
     "    inputs=[\n",
     "        ProcessingInput(source=input_data, destination=\"/opt/ml/processing/input\"),\n",
     "    ],\n",
@@ -1979,19 +870,21 @@
     "        ProcessingOutput(output_name=\"validation\", source=\"/opt/ml/processing/validation\"),\n",
     "        ProcessingOutput(output_name=\"test\", source=\"/opt/ml/processing/test\"),\n",
     "    ],\n",
-    "    code=\"abalone/preprocessing.py\",\n",
-    ")"
+    "    code=\"code/preprocessing.py\",\n",
+    ")\n",
+    "\n",
+    "step_process = ProcessingStep(name=\"AbaloneProcess\", step_args=processor_args)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ae886e49",
+   "id": "b42f9bb1",
    "metadata": {
     "papermill": {
-     "duration": 0.106882,
-     "end_time": "2022-04-18T00:27:03.362015",
+     "duration": 0.033806,
+     "end_time": "2022-07-13T16:07:51.617961",
      "exception": false,
-     "start_time": "2022-04-18T00:27:03.255133",
+     "start_time": "2022-07-13T16:07:51.584155",
      "status": "completed"
     },
     "tags": []
@@ -2002,13 +895,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "99a2d101",
+   "id": "dc187225",
    "metadata": {
     "papermill": {
-     "duration": 0.105673,
-     "end_time": "2022-04-18T00:27:03.636255",
+     "duration": 0.040739,
+     "end_time": "2022-07-13T16:07:51.691562",
      "exception": false,
-     "start_time": "2022-04-18T00:27:03.530582",
+     "start_time": "2022-07-13T16:07:51.650823",
      "status": "completed"
     },
     "tags": []
@@ -2026,27 +919,30 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "0da0afcf",
+   "id": "7407bef6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:27:03.930444Z",
-     "iopub.status.busy": "2022-04-18T00:27:03.856195Z",
-     "iopub.status.idle": "2022-04-18T00:27:03.943258Z",
-     "shell.execute_reply": "2022-04-18T00:27:03.942838Z"
+     "iopub.execute_input": "2022-07-13T16:07:51.766085Z",
+     "iopub.status.busy": "2022-07-13T16:07:51.765580Z",
+     "iopub.status.idle": "2022-07-13T16:07:51.897005Z",
+     "shell.execute_reply": "2022-07-13T16:07:51.896426Z"
     },
     "papermill": {
-     "duration": 0.198963,
-     "end_time": "2022-04-18T00:27:03.943382",
+     "duration": 0.171801,
+     "end_time": "2022-07-13T16:07:51.897137",
      "exception": false,
-     "start_time": "2022-04-18T00:27:03.744419",
+     "start_time": "2022-07-13T16:07:51.725336",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [],
    "source": [
     "from sagemaker.estimator import Estimator\n",
-    "\n",
+    "from sagemaker.inputs import TrainingInput\n",
     "\n",
     "model_path = f\"s3://{default_bucket}/AbaloneTrain\"\n",
     "image_uri = sagemaker.image_uris.retrieve(\n",
@@ -2054,7 +950,7 @@
     "    region=region,\n",
     "    version=\"1.0-1\",\n",
     "    py_version=\"py3\",\n",
-    "    instance_type=instance_type,\n",
+    "    instance_type=\"ml.m5.xlarge\",\n",
     ")\n",
     "xgb_train = Estimator(\n",
     "    image_uri=image_uri,\n",
@@ -2062,6 +958,7 @@
     "    instance_count=1,\n",
     "    output_path=model_path,\n",
     "    role=role,\n",
+    "    sagemaker_session=pipeline_session,\n",
     ")\n",
     "xgb_train.set_hyperparameters(\n",
     "    objective=\"reg:linear\",\n",
@@ -2071,46 +968,64 @@
     "    gamma=4,\n",
     "    min_child_weight=6,\n",
     "    subsample=0.7,\n",
+    ")\n",
+    "\n",
+    "train_args = xgb_train.fit(\n",
+    "    inputs={\n",
+    "        \"train\": TrainingInput(\n",
+    "            s3_data=step_process.properties.ProcessingOutputConfig.Outputs[\"train\"].S3Output.S3Uri,\n",
+    "            content_type=\"text/csv\",\n",
+    "        ),\n",
+    "        \"validation\": TrainingInput(\n",
+    "            s3_data=step_process.properties.ProcessingOutputConfig.Outputs[\n",
+    "                \"validation\"\n",
+    "            ].S3Output.S3Uri,\n",
+    "            content_type=\"text/csv\",\n",
+    "        ),\n",
+    "    }\n",
     ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "acb3d8d4",
+   "id": "f86374ad",
    "metadata": {
     "papermill": {
-     "duration": 0.105706,
-     "end_time": "2022-04-18T00:27:04.154522",
+     "duration": 0.064705,
+     "end_time": "2022-07-13T16:07:51.995767",
      "exception": false,
-     "start_time": "2022-04-18T00:27:04.048816",
+     "start_time": "2022-07-13T16:07:51.931062",
      "status": "completed"
     },
     "tags": []
    },
    "source": [
-    "Finally, use the estimator instance to construct a `TrainingStep` as well as the `properties` of the prior `ProcessingStep` used as input in the `TrainingStep` inputs and the code that's executed when the pipeline invokes the pipeline execution. This is similar to an estimator's `fit` method in the Python SDK.\n",
+    "Finally, we use the output of the estimator's `.fit()` method as arguments to the `TrainingStep`. By passing the `pipeline_session` to the `sagemaker_session`, calling `.fit()` does not launch the training job, it returns the arguments needed to run the job as a step in the pipeline.\n",
     "\n",
-    "Pass in the `S3Uri` of the `\"train_data\"` output channel to the `TrainingStep`. Also, use the other `\"test_data\"` output channel for model evaluation in the pipeline. The `properties` attribute of a Pipeline step matches the object model of the corresponding response of a describe call. These properties can be referenced as placeholder values and are resolved at runtime. For example, the `ProcessingStep` `properties` attribute matches the object model of the [DescribeProcessingJob](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeProcessingJob.html) response object."
+    "Pass in the `S3Uri` of the `\"train_data\"` output channel to the `.fit()` method. Also, use the other `\"test_data\"` output channel for model evaluation in the pipeline. The `properties` attribute of a Pipeline step matches the object model of the corresponding response of a describe call. These properties can be referenced as placeholder values and are resolved at runtime. For example, the `ProcessingStep` `properties` attribute matches the object model of the [DescribeProcessingJob](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeProcessingJob.html) response object."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "b58b44f8",
+   "id": "724d8eef",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:27:04.438230Z",
-     "iopub.status.busy": "2022-04-18T00:27:04.437184Z",
-     "iopub.status.idle": "2022-04-18T00:27:04.438922Z",
-     "shell.execute_reply": "2022-04-18T00:27:04.439349Z"
+     "iopub.execute_input": "2022-07-13T16:07:52.195314Z",
+     "iopub.status.busy": "2022-07-13T16:07:52.193872Z",
+     "iopub.status.idle": "2022-07-13T16:07:52.195919Z",
+     "shell.execute_reply": "2022-07-13T16:07:52.196336Z"
     },
     "papermill": {
-     "duration": 0.178944,
-     "end_time": "2022-04-18T00:27:04.439510",
+     "duration": 0.103611,
+     "end_time": "2022-07-13T16:07:52.196480",
      "exception": false,
-     "start_time": "2022-04-18T00:27:04.260566",
+     "start_time": "2022-07-13T16:07:52.092869",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [],
@@ -2121,31 +1036,19 @@
     "\n",
     "step_train = TrainingStep(\n",
     "    name=\"AbaloneTrain\",\n",
-    "    estimator=xgb_train,\n",
-    "    inputs={\n",
-    "        \"train\": TrainingInput(\n",
-    "            s3_data=step_process.properties.ProcessingOutputConfig.Outputs[\"train\"].S3Output.S3Uri,\n",
-    "            content_type=\"text/csv\",\n",
-    "        ),\n",
-    "        \"validation\": TrainingInput(\n",
-    "            s3_data=step_process.properties.ProcessingOutputConfig.Outputs[\n",
-    "                \"validation\"\n",
-    "            ].S3Output.S3Uri,\n",
-    "            content_type=\"text/csv\",\n",
-    "        ),\n",
-    "    },\n",
+    "    step_args=train_args,\n",
     ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "32529fe6",
+   "id": "61d7285f",
    "metadata": {
     "papermill": {
-     "duration": 0.10595,
-     "end_time": "2022-04-18T00:27:04.650608",
+     "duration": 0.097979,
+     "end_time": "2022-07-13T16:07:52.392020",
      "exception": false,
-     "start_time": "2022-04-18T00:27:04.544658",
+     "start_time": "2022-07-13T16:07:52.294041",
      "status": "completed"
     },
     "tags": []
@@ -2156,13 +1059,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc41fa12",
+   "id": "fd58bfa7",
    "metadata": {
     "papermill": {
-     "duration": 0.106253,
-     "end_time": "2022-04-18T00:27:04.863458",
+     "duration": 0.097153,
+     "end_time": "2022-07-13T16:07:52.586494",
      "exception": false,
-     "start_time": "2022-04-18T00:27:04.757205",
+     "start_time": "2022-07-13T16:07:52.489341",
      "status": "completed"
     },
     "tags": []
@@ -2186,21 +1089,24 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "445f1b8a",
+   "id": "a03a42f2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:27:05.145905Z",
-     "iopub.status.busy": "2022-04-18T00:27:05.144954Z",
-     "iopub.status.idle": "2022-04-18T00:27:05.149035Z",
-     "shell.execute_reply": "2022-04-18T00:27:05.148480Z"
+     "iopub.execute_input": "2022-07-13T16:07:52.867379Z",
+     "iopub.status.busy": "2022-07-13T16:07:52.866257Z",
+     "iopub.status.idle": "2022-07-13T16:07:52.869025Z",
+     "shell.execute_reply": "2022-07-13T16:07:52.869384Z"
     },
     "papermill": {
-     "duration": 0.114344,
-     "end_time": "2022-04-18T00:27:05.149184",
+     "duration": 0.184948,
+     "end_time": "2022-07-13T16:07:52.869516",
      "exception": false,
-     "start_time": "2022-04-18T00:27:05.034840",
+     "start_time": "2022-07-13T16:07:52.684568",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [
@@ -2208,12 +1114,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Writing abalone/evaluation.py\n"
+      "Writing code/evaluation.py\n"
      ]
     }
    ],
    "source": [
-    "%%writefile abalone/evaluation.py\n",
+    "%%writefile code/evaluation.py\n",
     "import json\n",
     "import pathlib\n",
     "import pickle\n",
@@ -2262,44 +1168,56 @@
   },
   {
    "cell_type": "markdown",
-   "id": "291a0f15",
+   "id": "3db80eee",
    "metadata": {
     "papermill": {
-     "duration": 0.16557,
-     "end_time": "2022-04-18T00:27:05.430895",
+     "duration": 0.033789,
+     "end_time": "2022-07-13T16:07:52.999885",
      "exception": false,
-     "start_time": "2022-04-18T00:27:05.265325",
+     "start_time": "2022-07-13T16:07:52.966096",
      "status": "completed"
     },
     "tags": []
    },
    "source": [
-    "Next, create an instance of a `ScriptProcessor` processor and use it in the `ProcessingStep`.\n",
-    "\n",
-    "Note the `processing_instance_type` parameter passed into the processor."
+    "Next, create an instance of a `ScriptProcessor` processor and use it in the `ProcessingStep`."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "c6d4c85a",
+   "id": "53230930",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:27:05.655883Z",
-     "iopub.status.busy": "2022-04-18T00:27:05.655326Z",
-     "iopub.status.idle": "2022-04-18T00:27:05.663003Z",
-     "shell.execute_reply": "2022-04-18T00:27:05.662503Z"
+     "iopub.execute_input": "2022-07-13T16:07:53.265975Z",
+     "iopub.status.busy": "2022-07-13T16:07:53.265226Z",
+     "iopub.status.idle": "2022-07-13T16:07:53.339364Z",
+     "shell.execute_reply": "2022-07-13T16:07:53.338974Z"
     },
     "papermill": {
-     "duration": 0.125324,
-     "end_time": "2022-04-18T00:27:05.663126",
+     "duration": 0.242851,
+     "end_time": "2022-07-13T16:07:53.339481",
      "exception": false,
-     "start_time": "2022-04-18T00:27:05.537802",
+     "start_time": "2022-07-13T16:07:53.096630",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Job Name:  script-abalone-eval-2022-07-13-16-07-53-265\n",
+      "Inputs:  [{'InputName': 'input-1', 'AppManaged': False, 'S3Input': {'S3Uri': <sagemaker.workflow.properties.Properties object at 0x7f17cde6cc50>, 'LocalPath': '/opt/ml/processing/model', 'S3DataType': 'S3Prefix', 'S3InputMode': 'File', 'S3DataDistributionType': 'FullyReplicated', 'S3CompressionType': 'None'}}, {'InputName': 'input-2', 'AppManaged': False, 'S3Input': {'S3Uri': <sagemaker.workflow.properties.Properties object at 0x7f17cde38f10>, 'LocalPath': '/opt/ml/processing/test', 'S3DataType': 'S3Prefix', 'S3InputMode': 'File', 'S3DataDistributionType': 'FullyReplicated', 'S3CompressionType': 'None'}}, {'InputName': 'code', 'AppManaged': False, 'S3Input': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/script-abalone-eval-2022-07-13-16-07-53-265/input/code/evaluation.py', 'LocalPath': '/opt/ml/processing/input/code', 'S3DataType': 'S3Prefix', 'S3InputMode': 'File', 'S3DataDistributionType': 'FullyReplicated', 'S3CompressionType': 'None'}}]\n",
+      "Outputs:  [{'OutputName': 'evaluation', 'AppManaged': False, 'S3Output': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/script-abalone-eval-2022-07-13-16-07-53-265/output/evaluation', 'LocalPath': '/opt/ml/processing/evaluation', 'S3UploadMode': 'EndOfJob'}}]\n"
+     ]
+    }
+   ],
    "source": [
     "from sagemaker.processing import ScriptProcessor\n",
     "\n",
@@ -2307,28 +1225,46 @@
     "script_eval = ScriptProcessor(\n",
     "    image_uri=image_uri,\n",
     "    command=[\"python3\"],\n",
-    "    instance_type=processing_instance_type,\n",
+    "    instance_type=\"ml.m5.xlarge\",\n",
     "    instance_count=1,\n",
     "    base_job_name=\"script-abalone-eval\",\n",
     "    role=role,\n",
+    "    sagemaker_session=pipeline_session,\n",
+    ")\n",
+    "\n",
+    "eval_args = script_eval.run(\n",
+    "    inputs=[\n",
+    "        ProcessingInput(\n",
+    "            source=step_train.properties.ModelArtifacts.S3ModelArtifacts,\n",
+    "            destination=\"/opt/ml/processing/model\",\n",
+    "        ),\n",
+    "        ProcessingInput(\n",
+    "            source=step_process.properties.ProcessingOutputConfig.Outputs[\"test\"].S3Output.S3Uri,\n",
+    "            destination=\"/opt/ml/processing/test\",\n",
+    "        ),\n",
+    "    ],\n",
+    "    outputs=[\n",
+    "        ProcessingOutput(output_name=\"evaluation\", source=\"/opt/ml/processing/evaluation\"),\n",
+    "    ],\n",
+    "    code=\"code/evaluation.py\",\n",
     ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b316d741",
+   "id": "858206cd",
    "metadata": {
     "papermill": {
-     "duration": 0.106669,
-     "end_time": "2022-04-18T00:27:05.939502",
+     "duration": 0.097754,
+     "end_time": "2022-07-13T16:07:53.494882",
      "exception": false,
-     "start_time": "2022-04-18T00:27:05.832833",
+     "start_time": "2022-07-13T16:07:53.397128",
      "status": "completed"
     },
     "tags": []
    },
    "source": [
-    "Use the processor instance to construct a `ProcessingStep`, along with the input and output channels and the code that runs when the pipeline invokes pipeline execution. This is similar to a processor instance's `run` method in the Python SDK.\n",
+    "Use the processor's arguments returned by `.run()` to construct a `ProcessingStep`, along with the input and output channels and the code that will be executed when the pipeline invokes pipeline execution.\n",
     "\n",
     "Specifically, the `S3ModelArtifacts` from the `step_train` `properties` and the `S3Uri` of the `\"test_data\"` output channel of the `step_process` `properties` are passed as inputs. The `TrainingStep` and `ProcessingStep` `properties` attribute matches the object model of the [DescribeTrainingJob](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeTrainingJob.html) and [DescribeProcessingJob](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeProcessingJob.html) response objects, respectively."
    ]
@@ -2336,21 +1272,24 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "8acddb48",
+   "id": "ae643d67",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:27:06.232317Z",
-     "iopub.status.busy": "2022-04-18T00:27:06.231534Z",
-     "iopub.status.idle": "2022-04-18T00:27:06.234111Z",
-     "shell.execute_reply": "2022-04-18T00:27:06.234602Z"
+     "iopub.execute_input": "2022-07-13T16:07:53.695523Z",
+     "iopub.status.busy": "2022-07-13T16:07:53.694111Z",
+     "iopub.status.idle": "2022-07-13T16:07:53.696160Z",
+     "shell.execute_reply": "2022-07-13T16:07:53.696534Z"
     },
     "papermill": {
-     "duration": 0.189298,
-     "end_time": "2022-04-18T00:27:06.234812",
+     "duration": 0.104102,
+     "end_time": "2022-07-13T16:07:53.696669",
      "exception": false,
-     "start_time": "2022-04-18T00:27:06.045514",
+     "start_time": "2022-07-13T16:07:53.592567",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [],
@@ -2363,34 +1302,20 @@
     ")\n",
     "step_eval = ProcessingStep(\n",
     "    name=\"AbaloneEval\",\n",
-    "    processor=script_eval,\n",
-    "    inputs=[\n",
-    "        ProcessingInput(\n",
-    "            source=step_train.properties.ModelArtifacts.S3ModelArtifacts,\n",
-    "            destination=\"/opt/ml/processing/model\",\n",
-    "        ),\n",
-    "        ProcessingInput(\n",
-    "            source=step_process.properties.ProcessingOutputConfig.Outputs[\"test\"].S3Output.S3Uri,\n",
-    "            destination=\"/opt/ml/processing/test\",\n",
-    "        ),\n",
-    "    ],\n",
-    "    outputs=[\n",
-    "        ProcessingOutput(output_name=\"evaluation\", source=\"/opt/ml/processing/evaluation\"),\n",
-    "    ],\n",
-    "    code=\"abalone/evaluation.py\",\n",
+    "    step_args=eval_args,\n",
     "    property_files=[evaluation_report],\n",
     ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a5540efa",
+   "id": "99f1a3c2",
    "metadata": {
     "papermill": {
-     "duration": 0.108782,
-     "end_time": "2022-04-18T00:27:06.452659",
+     "duration": 0.096611,
+     "end_time": "2022-07-13T16:07:53.966367",
      "exception": false,
-     "start_time": "2022-04-18T00:27:06.343877",
+     "start_time": "2022-07-13T16:07:53.869756",
      "status": "completed"
     },
     "tags": []
@@ -2401,13 +1326,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d772c2b1",
+   "id": "1ff0a560",
    "metadata": {
     "papermill": {
-     "duration": 0.106592,
-     "end_time": "2022-04-18T00:27:06.739719",
+     "duration": 0.097005,
+     "end_time": "2022-07-13T16:07:54.096931",
      "exception": false,
-     "start_time": "2022-04-18T00:27:06.633127",
+     "start_time": "2022-07-13T16:07:53.999926",
      "status": "completed"
     },
     "tags": []
@@ -2415,7 +1340,7 @@
    "source": [
     "## Define a Create Model Step to Create a Model\n",
     "\n",
-    "In order to perform batch transformation using the example model, create a SageMaker model. \n",
+    "In order to perform batch transformation using the example model, create a SageMaker model.\n",
     "\n",
     "Specifically, pass in the `S3ModelArtifacts` from the `TrainingStep`, `step_train` properties. The `TrainingStep` `properties` attribute matches the object model of the [DescribeTrainingJob](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeTrainingJob.html) response object."
    ]
@@ -2423,99 +1348,98 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "bfa99742",
+   "id": "6aab382f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:27:06.958939Z",
-     "iopub.status.busy": "2022-04-18T00:27:06.958044Z",
-     "iopub.status.idle": "2022-04-18T00:27:06.960510Z",
-     "shell.execute_reply": "2022-04-18T00:27:06.960931Z"
+     "iopub.execute_input": "2022-07-13T16:07:54.292477Z",
+     "iopub.status.busy": "2022-07-13T16:07:54.291118Z",
+     "iopub.status.idle": "2022-07-13T16:07:54.293086Z",
+     "shell.execute_reply": "2022-07-13T16:07:54.293444Z"
     },
     "papermill": {
-     "duration": 0.11483,
-     "end_time": "2022-04-18T00:27:06.961120",
+     "duration": 0.103791,
+     "end_time": "2022-07-13T16:07:54.293572",
      "exception": false,
-     "start_time": "2022-04-18T00:27:06.846290",
+     "start_time": "2022-07-13T16:07:54.189781",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [],
    "source": [
     "from sagemaker.model import Model\n",
     "\n",
-    "\n",
     "model = Model(\n",
     "    image_uri=image_uri,\n",
     "    model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,\n",
-    "    sagemaker_session=sagemaker_session,\n",
+    "    sagemaker_session=pipeline_session,\n",
     "    role=role,\n",
     ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "6ff36a22",
+   "id": "d9be254b",
    "metadata": {
     "papermill": {
-     "duration": 0.106018,
-     "end_time": "2022-04-18T00:27:07.236643",
+     "duration": 0.099816,
+     "end_time": "2022-07-13T16:07:54.490471",
      "exception": false,
-     "start_time": "2022-04-18T00:27:07.130625",
+     "start_time": "2022-07-13T16:07:54.390655",
      "status": "completed"
     },
     "tags": []
    },
    "source": [
-    "Supply the model input (`instance_type` and `accelerator_type`) for creating the SageMaker Model, and then define the `CreateModelStep`, passing in the inputs and the model instance defined before."
+    "Define the `ModelStep` by providing the return values from `model.create()` as the step arguments."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "4343f97b",
+   "id": "7a8dc222",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:27:07.455511Z",
-     "iopub.status.busy": "2022-04-18T00:27:07.454736Z",
-     "iopub.status.idle": "2022-04-18T00:27:07.457542Z",
-     "shell.execute_reply": "2022-04-18T00:27:07.457062Z"
+     "iopub.execute_input": "2022-07-13T16:07:54.690071Z",
+     "iopub.status.busy": "2022-07-13T16:07:54.689317Z",
+     "iopub.status.idle": "2022-07-13T16:07:54.694056Z",
+     "shell.execute_reply": "2022-07-13T16:07:54.694467Z"
     },
     "papermill": {
-     "duration": 0.114869,
-     "end_time": "2022-04-18T00:27:07.457675",
+     "duration": 0.106688,
+     "end_time": "2022-07-13T16:07:54.694631",
      "exception": false,
-     "start_time": "2022-04-18T00:27:07.342806",
+     "start_time": "2022-07-13T16:07:54.587943",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [],
    "source": [
     "from sagemaker.inputs import CreateModelInput\n",
-    "from sagemaker.workflow.steps import CreateModelStep\n",
-    "\n",
+    "from sagemaker.workflow.model_step import ModelStep\n",
     "\n",
-    "inputs = CreateModelInput(\n",
-    "    instance_type=\"ml.m5.large\",\n",
-    "    accelerator_type=\"ml.eia1.medium\",\n",
-    ")\n",
-    "step_create_model = CreateModelStep(\n",
+    "step_create_model = ModelStep(\n",
     "    name=\"AbaloneCreateModel\",\n",
-    "    model=model,\n",
-    "    inputs=inputs,\n",
+    "    step_args=model.create(instance_type=\"ml.m5.large\", accelerator_type=\"ml.eia1.medium\"),\n",
     ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "921a78fe",
+   "id": "c26c7052",
    "metadata": {
     "papermill": {
-     "duration": 0.169956,
-     "end_time": "2022-04-18T00:27:07.734340",
+     "duration": 0.092854,
+     "end_time": "2022-07-13T16:07:54.964979",
      "exception": false,
-     "start_time": "2022-04-18T00:27:07.564384",
+     "start_time": "2022-07-13T16:07:54.872125",
      "status": "completed"
     },
     "tags": []
@@ -2531,21 +1455,24 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "1c5e6294",
+   "id": "f55318d8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:27:07.956628Z",
-     "iopub.status.busy": "2022-04-18T00:27:07.955691Z",
-     "iopub.status.idle": "2022-04-18T00:27:08.033592Z",
-     "shell.execute_reply": "2022-04-18T00:27:08.033113Z"
+     "iopub.execute_input": "2022-07-13T16:07:55.168074Z",
+     "iopub.status.busy": "2022-07-13T16:07:55.167333Z",
+     "iopub.status.idle": "2022-07-13T16:07:55.175645Z",
+     "shell.execute_reply": "2022-07-13T16:07:55.176011Z"
     },
     "papermill": {
-     "duration": 0.192424,
-     "end_time": "2022-04-18T00:27:08.033724",
+     "duration": 0.177212,
+     "end_time": "2022-07-13T16:07:55.176173",
      "exception": false,
-     "start_time": "2022-04-18T00:27:07.841300",
+     "start_time": "2022-07-13T16:07:54.998961",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [],
@@ -2563,13 +1490,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "08c9bd75",
+   "id": "6227a29a",
    "metadata": {
     "papermill": {
-     "duration": 0.10677,
-     "end_time": "2022-04-18T00:27:08.246016",
+     "duration": 0.102855,
+     "end_time": "2022-07-13T16:07:55.376256",
      "exception": false,
-     "start_time": "2022-04-18T00:27:08.139246",
+     "start_time": "2022-07-13T16:07:55.273401",
      "status": "completed"
     },
     "tags": []
@@ -2581,21 +1508,24 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "0086570b",
+   "id": "6e1aa0ad",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:27:08.455589Z",
-     "iopub.status.busy": "2022-04-18T00:27:08.454984Z",
-     "iopub.status.idle": "2022-04-18T00:27:08.532736Z",
-     "shell.execute_reply": "2022-04-18T00:27:08.532252Z"
+     "iopub.execute_input": "2022-07-13T16:07:55.578902Z",
+     "iopub.status.busy": "2022-07-13T16:07:55.577537Z",
+     "iopub.status.idle": "2022-07-13T16:07:55.579522Z",
+     "shell.execute_reply": "2022-07-13T16:07:55.579879Z"
     },
     "papermill": {
-     "duration": 0.190304,
-     "end_time": "2022-04-18T00:27:08.532863",
+     "duration": 0.110305,
+     "end_time": "2022-07-13T16:07:55.580011",
      "exception": false,
-     "start_time": "2022-04-18T00:27:08.342559",
+     "start_time": "2022-07-13T16:07:55.469706",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [],
@@ -2611,13 +1541,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d1db8c49",
+   "id": "e400b0f0",
    "metadata": {
     "papermill": {
-     "duration": 0.162352,
-     "end_time": "2022-04-18T00:27:08.738517",
+     "duration": 0.097293,
+     "end_time": "2022-07-13T16:07:55.779132",
      "exception": false,
-     "start_time": "2022-04-18T00:27:08.576165",
+     "start_time": "2022-07-13T16:07:55.681839",
      "status": "completed"
     },
     "tags": []
@@ -2625,42 +1555,50 @@
    "source": [
     "## Define a Register Model Step to Create a Model Package\n",
     "\n",
-    "Use the estimator instance specified in the training step to construct an instance of `RegisterModel`. The result of executing `RegisterModel` in a pipeline is a model package. A model package is an abstraction of reusable model artifacts that packages all ingredients required for inference. Primarily, it consists of an inference specification that defines the inference image to use along with an optional model weights location.\n",
+    "A model package is an abstraction of reusable model artifacts that packages all ingredients required for inference. Primarily, it consists of an inference specification that defines the inference image to use along with an optional model weights location.\n",
     "\n",
     "A model package group is a collection of model packages. A model package group can be created for a specific ML business problem, and new versions of the model packages can be added to it. Typically, customers are expected to create a ModelPackageGroup for a SageMaker pipeline so that model package versions can be added to the group for every SageMaker Pipeline run.\n",
     "\n",
-    "The construction of `RegisterModel` is similar to an estimator instance's `register` method in the Python SDK.\n",
-    "\n",
-    "Specifically, pass in the `S3ModelArtifacts` from the `TrainingStep`, `step_train` properties. The `TrainingStep` `properties` attribute matches the object model of the [DescribeTrainingJob](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeTrainingJob.html) response object.\n",
+    "To register a model in the Model Registry, we take the model created in the previous steps\n",
+    "```\n",
+    "model = Model(\n",
+    "    image_uri=image_uri,\n",
+    "    model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,\n",
+    "    sagemaker_session=pipeline_session,\n",
+    "    role=role,\n",
+    ")\n",
+    "```\n",
+    "and call the `.register()` function on it while passing all the parameters needed for registering the model.\n",
     "\n",
-    "Note that the specific model package group name provided in this notebook can be used in the model registry and CI/CD work with SageMaker Projects."
+    "We take the outputs of the `.register()` call and pass that to the `ModelStep` as step arguments."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "5763c5cf",
+   "id": "49268979",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:27:09.035696Z",
-     "iopub.status.busy": "2022-04-18T00:27:09.034604Z",
-     "iopub.status.idle": "2022-04-18T00:27:09.334548Z",
-     "shell.execute_reply": "2022-04-18T00:27:09.334010Z"
+     "iopub.execute_input": "2022-07-13T16:07:55.990666Z",
+     "iopub.status.busy": "2022-07-13T16:07:55.989233Z",
+     "iopub.status.idle": "2022-07-13T16:07:55.991258Z",
+     "shell.execute_reply": "2022-07-13T16:07:55.991663Z"
     },
     "papermill": {
-     "duration": 0.477925,
-     "end_time": "2022-04-18T00:27:09.334667",
+     "duration": 0.106521,
+     "end_time": "2022-07-13T16:07:55.991800",
      "exception": false,
-     "start_time": "2022-04-18T00:27:08.856742",
+     "start_time": "2022-07-13T16:07:55.885279",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [],
    "source": [
     "from sagemaker.model_metrics import MetricsSource, ModelMetrics\n",
-    "from sagemaker.workflow.step_collections import RegisterModel\n",
-    "\n",
     "\n",
     "model_metrics = ModelMetrics(\n",
     "    model_statistics=MetricsSource(\n",
@@ -2670,10 +1608,8 @@
     "        content_type=\"application/json\",\n",
     "    )\n",
     ")\n",
-    "step_register = RegisterModel(\n",
-    "    name=\"AbaloneRegisterModel\",\n",
-    "    estimator=xgb_train,\n",
-    "    model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,\n",
+    "\n",
+    "register_args = model.register(\n",
     "    content_types=[\"text/csv\"],\n",
     "    response_types=[\"text/csv\"],\n",
     "    inference_instances=[\"ml.t2.medium\", \"ml.m5.xlarge\"],\n",
@@ -2681,18 +1617,19 @@
     "    model_package_group_name=model_package_group_name,\n",
     "    approval_status=model_approval_status,\n",
     "    model_metrics=model_metrics,\n",
-    ")"
+    ")\n",
+    "step_register = ModelStep(name=\"AbaloneRegisterModel\", step_args=register_args)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b8b852cf",
+   "id": "d1622708",
    "metadata": {
     "papermill": {
-     "duration": 0.078046,
-     "end_time": "2022-04-18T00:27:09.455768",
+     "duration": 0.093149,
+     "end_time": "2022-07-13T16:07:56.186199",
      "exception": false,
-     "start_time": "2022-04-18T00:27:09.377722",
+     "start_time": "2022-07-13T16:07:56.093050",
      "status": "completed"
     },
     "tags": []
@@ -2703,13 +1640,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1a1cec12",
+   "id": "756157c0",
    "metadata": {
     "papermill": {
-     "duration": 0.169798,
-     "end_time": "2022-04-18T00:27:09.731822",
+     "duration": 0.097569,
+     "end_time": "2022-07-13T16:07:56.381372",
      "exception": false,
-     "start_time": "2022-04-18T00:27:09.562024",
+     "start_time": "2022-07-13T16:07:56.283803",
      "status": "completed"
     },
     "tags": []
@@ -2726,22 +1663,19 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "8948e14a",
+   "id": "f041c8a8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:27:09.951031Z",
-     "iopub.status.busy": "2022-04-18T00:27:09.949925Z",
-     "iopub.status.idle": "2022-04-18T00:27:09.953764Z",
-     "shell.execute_reply": "2022-04-18T00:27:09.953320Z"
-    },
-    "jupyter": {
-     "outputs_hidden": false
+     "iopub.execute_input": "2022-07-13T16:07:56.670215Z",
+     "iopub.status.busy": "2022-07-13T16:07:56.669270Z",
+     "iopub.status.idle": "2022-07-13T16:07:56.675249Z",
+     "shell.execute_reply": "2022-07-13T16:07:56.674652Z"
     },
     "papermill": {
-     "duration": 0.115047,
-     "end_time": "2022-04-18T00:27:09.953878",
+     "duration": 0.193196,
+     "end_time": "2022-07-13T16:07:56.675378",
      "exception": false,
-     "start_time": "2022-04-18T00:27:09.838831",
+     "start_time": "2022-07-13T16:07:56.482182",
      "status": "completed"
     },
     "pycharm": {
@@ -2762,13 +1696,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "835eaf17",
+   "id": "8fcf7e9c",
    "metadata": {
     "papermill": {
-     "duration": 0.106951,
-     "end_time": "2022-04-18T00:27:10.167203",
+     "duration": 0.182821,
+     "end_time": "2022-07-13T16:07:56.967379",
      "exception": false,
-     "start_time": "2022-04-18T00:27:10.060252",
+     "start_time": "2022-07-13T16:07:56.784558",
      "status": "completed"
     },
     "tags": []
@@ -2779,13 +1713,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9e52c136",
+   "id": "fd777542",
    "metadata": {
     "papermill": {
-     "duration": 0.106965,
-     "end_time": "2022-04-18T00:27:10.444635",
+     "duration": 0.034317,
+     "end_time": "2022-07-13T16:07:57.099302",
      "exception": false,
-     "start_time": "2022-04-18T00:27:10.337670",
+     "start_time": "2022-07-13T16:07:57.064985",
      "status": "completed"
     },
     "tags": []
@@ -2806,21 +1740,24 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "454e9b4c",
+   "id": "8969e1fc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:27:10.732662Z",
-     "iopub.status.busy": "2022-04-18T00:27:10.731797Z",
-     "iopub.status.idle": "2022-04-18T00:27:10.739016Z",
-     "shell.execute_reply": "2022-04-18T00:27:10.738481Z"
+     "iopub.execute_input": "2022-07-13T16:07:57.300757Z",
+     "iopub.status.busy": "2022-07-13T16:07:57.299973Z",
+     "iopub.status.idle": "2022-07-13T16:07:57.367501Z",
+     "shell.execute_reply": "2022-07-13T16:07:57.367888Z"
     },
     "papermill": {
-     "duration": 0.185973,
-     "end_time": "2022-04-18T00:27:10.739143",
+     "duration": 0.171087,
+     "end_time": "2022-07-13T16:07:57.368032",
      "exception": false,
-     "start_time": "2022-04-18T00:27:10.553170",
+     "start_time": "2022-07-13T16:07:57.196945",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [],
@@ -2849,13 +1786,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1849f21f",
+   "id": "772154ec",
    "metadata": {
     "papermill": {
-     "duration": 0.106741,
-     "end_time": "2022-04-18T00:27:10.953358",
+     "duration": 0.098709,
+     "end_time": "2022-07-13T16:07:57.564590",
      "exception": false,
-     "start_time": "2022-04-18T00:27:10.846617",
+     "start_time": "2022-07-13T16:07:57.465881",
      "status": "completed"
     },
     "tags": []
@@ -2866,13 +1803,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7e06f7da",
+   "id": "5cd8a7d1",
    "metadata": {
     "papermill": {
-     "duration": 0.169898,
-     "end_time": "2022-04-18T00:27:11.231857",
+     "duration": 0.097983,
+     "end_time": "2022-07-13T16:07:57.697262",
      "exception": false,
-     "start_time": "2022-04-18T00:27:11.061959",
+     "start_time": "2022-07-13T16:07:57.599279",
      "status": "completed"
     },
     "tags": []
@@ -2894,21 +1831,24 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "331e72f7",
+   "id": "9ed7ea0f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:27:11.453724Z",
-     "iopub.status.busy": "2022-04-18T00:27:11.452894Z",
-     "iopub.status.idle": "2022-04-18T00:27:11.548515Z",
-     "shell.execute_reply": "2022-04-18T00:27:11.548065Z"
+     "iopub.execute_input": "2022-07-13T16:07:57.977833Z",
+     "iopub.status.busy": "2022-07-13T16:07:57.977020Z",
+     "iopub.status.idle": "2022-07-13T16:07:57.996102Z",
+     "shell.execute_reply": "2022-07-13T16:07:57.995674Z"
     },
     "papermill": {
-     "duration": 0.208191,
-     "end_time": "2022-04-18T00:27:11.548633",
+     "duration": 0.201248,
+     "end_time": "2022-07-13T16:07:57.996214",
      "exception": false,
-     "start_time": "2022-04-18T00:27:11.340442",
+     "start_time": "2022-07-13T16:07:57.794966",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [],
@@ -2920,7 +1860,6 @@
     "pipeline = Pipeline(\n",
     "    name=pipeline_name,\n",
     "    parameters=[\n",
-    "        processing_instance_type,\n",
     "        processing_instance_count,\n",
     "        instance_type,\n",
     "        model_approval_status,\n",
@@ -2934,13 +1873,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c12b9f87",
+   "id": "8bd7778c",
    "metadata": {
     "papermill": {
-     "duration": 0.107549,
-     "end_time": "2022-04-18T00:27:11.763791",
+     "duration": 0.098305,
+     "end_time": "2022-07-13T16:07:58.192290",
      "exception": false,
-     "start_time": "2022-04-18T00:27:11.656242",
+     "start_time": "2022-07-13T16:07:58.093985",
      "status": "completed"
     },
     "tags": []
@@ -2951,13 +1890,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f395b678",
+   "id": "c1ae8658",
    "metadata": {
     "papermill": {
-     "duration": 0.107734,
-     "end_time": "2022-04-18T00:27:12.042259",
+     "duration": 0.097728,
+     "end_time": "2022-07-13T16:07:58.394347",
      "exception": false,
-     "start_time": "2022-04-18T00:27:11.934525",
+     "start_time": "2022-07-13T16:07:58.296619",
      "status": "completed"
     },
     "tags": []
@@ -2971,40 +1910,35 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "a5ed87c3",
+   "id": "39a836b8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:27:12.264134Z",
-     "iopub.status.busy": "2022-04-18T00:27:12.263335Z",
-     "iopub.status.idle": "2022-04-18T00:27:12.933150Z",
-     "shell.execute_reply": "2022-04-18T00:27:12.932730Z"
+     "iopub.execute_input": "2022-07-13T16:07:58.681323Z",
+     "iopub.status.busy": "2022-07-13T16:07:58.680648Z",
+     "iopub.status.idle": "2022-07-13T16:07:58.684073Z",
+     "shell.execute_reply": "2022-07-13T16:07:58.684421Z"
     },
     "papermill": {
-     "duration": 0.783967,
-     "end_time": "2022-04-18T00:27:12.933271",
+     "duration": 0.18974,
+     "end_time": "2022-07-13T16:07:58.684552",
      "exception": false,
-     "start_time": "2022-04-18T00:27:12.149304",
+     "start_time": "2022-07-13T16:07:58.494812",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "No finished training job found associated with this estimator. Please make sure this estimator is only used for building workflow config\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
        "{'Version': '2020-12-01',\n",
        " 'Metadata': {},\n",
-       " 'Parameters': [{'Name': 'ProcessingInstanceType',\n",
-       "   'Type': 'String',\n",
-       "   'DefaultValue': 'ml.m5.xlarge'},\n",
-       "  {'Name': 'ProcessingInstanceCount', 'Type': 'Integer', 'DefaultValue': 1},\n",
+       " 'Parameters': [{'Name': 'ProcessingInstanceCount',\n",
+       "   'Type': 'Integer',\n",
+       "   'DefaultValue': 1},\n",
        "  {'Name': 'TrainingInstanceType',\n",
        "   'Type': 'String',\n",
        "   'DefaultValue': 'ml.m5.xlarge'},\n",
@@ -3022,13 +1956,13 @@
        "  'TrialName': {'Get': 'Execution.PipelineExecutionId'}},\n",
        " 'Steps': [{'Name': 'AbaloneProcess',\n",
        "   'Type': 'Processing',\n",
-       "   'Arguments': {'ProcessingResources': {'ClusterConfig': {'InstanceType': {'Get': 'Parameters.ProcessingInstanceType'},\n",
+       "   'Arguments': {'ProcessingResources': {'ClusterConfig': {'InstanceType': 'ml.m5.xlarge',\n",
        "      'InstanceCount': {'Get': 'Parameters.ProcessingInstanceCount'},\n",
        "      'VolumeSizeInGB': 30}},\n",
-       "    'AppSpecification': {'ImageUri': '246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-scikit-learn:1.0-1-cpu-py3',\n",
+       "    'AppSpecification': {'ImageUri': '246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-scikit-learn:0.23-1-cpu-py3',\n",
        "     'ContainerEntrypoint': ['python3',\n",
        "      '/opt/ml/processing/input/code/preprocessing.py']},\n",
-       "    'RoleArn': 'arn:aws:iam::000000000000:role/ProdBuildSystemStack-ReleaseBuildRoleFB326D49-QK8LUA2UI1IC',\n",
+       "    'RoleArn': 'arn:aws:iam::000000000000:role/SageMakerRole',\n",
        "    'ProcessingInputs': [{'InputName': 'input-1',\n",
        "      'AppManaged': False,\n",
        "      'S3Input': {'S3Uri': {'Get': 'Parameters.InputData'},\n",
@@ -3039,7 +1973,7 @@
        "       'S3CompressionType': 'None'}},\n",
        "     {'InputName': 'code',\n",
        "      'AppManaged': False,\n",
-       "      'S3Input': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/AbaloneProcess-a55d50a0c87783b887401dc1ff1d9bf9/input/code/preprocessing.py',\n",
+       "      'S3Input': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/sklearn-abalone-process-2022-07-13-16-07-51-228/input/code/preprocessing.py',\n",
        "       'LocalPath': '/opt/ml/processing/input/code',\n",
        "       'S3DataType': 'S3Prefix',\n",
        "       'S3InputMode': 'File',\n",
@@ -3047,17 +1981,17 @@
        "       'S3CompressionType': 'None'}}],\n",
        "    'ProcessingOutputConfig': {'Outputs': [{'OutputName': 'train',\n",
        "       'AppManaged': False,\n",
-       "       'S3Output': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/AbaloneProcess-a55d50a0c87783b887401dc1ff1d9bf9/output/train',\n",
+       "       'S3Output': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/sklearn-abalone-process-2022-07-13-16-07-51-228/output/train',\n",
        "        'LocalPath': '/opt/ml/processing/train',\n",
        "        'S3UploadMode': 'EndOfJob'}},\n",
        "      {'OutputName': 'validation',\n",
        "       'AppManaged': False,\n",
-       "       'S3Output': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/AbaloneProcess-a55d50a0c87783b887401dc1ff1d9bf9/output/validation',\n",
+       "       'S3Output': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/sklearn-abalone-process-2022-07-13-16-07-51-228/output/validation',\n",
        "        'LocalPath': '/opt/ml/processing/validation',\n",
        "        'S3UploadMode': 'EndOfJob'}},\n",
        "      {'OutputName': 'test',\n",
        "       'AppManaged': False,\n",
-       "       'S3Output': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/AbaloneProcess-a55d50a0c87783b887401dc1ff1d9bf9/output/test',\n",
+       "       'S3Output': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/sklearn-abalone-process-2022-07-13-16-07-51-228/output/test',\n",
        "        'LocalPath': '/opt/ml/processing/test',\n",
        "        'S3UploadMode': 'EndOfJob'}}]}}},\n",
        "  {'Name': 'AbaloneTrain',\n",
@@ -3066,10 +2000,10 @@
        "     'TrainingImage': '246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-xgboost:1.0-1-cpu-py3'},\n",
        "    'OutputDataConfig': {'S3OutputPath': 's3://sagemaker-us-west-2-000000000000/AbaloneTrain'},\n",
        "    'StoppingCondition': {'MaxRuntimeInSeconds': 86400},\n",
-       "    'ResourceConfig': {'InstanceCount': 1,\n",
-       "     'InstanceType': {'Get': 'Parameters.TrainingInstanceType'},\n",
-       "     'VolumeSizeInGB': 30},\n",
-       "    'RoleArn': 'arn:aws:iam::000000000000:role/ProdBuildSystemStack-ReleaseBuildRoleFB326D49-QK8LUA2UI1IC',\n",
+       "    'ResourceConfig': {'VolumeSizeInGB': 30,\n",
+       "     'InstanceCount': 1,\n",
+       "     'InstanceType': {'Get': 'Parameters.TrainingInstanceType'}},\n",
+       "    'RoleArn': 'arn:aws:iam::000000000000:role/SageMakerRole',\n",
        "    'InputDataConfig': [{'DataSource': {'S3DataSource': {'S3DataType': 'S3Prefix',\n",
        "        'S3Uri': {'Get': \"Steps.AbaloneProcess.ProcessingOutputConfig.Outputs['train'].S3Output.S3Uri\"},\n",
        "        'S3DataDistributionType': 'FullyReplicated'}},\n",
@@ -3087,19 +2021,19 @@
        "     'gamma': '4',\n",
        "     'min_child_weight': '6',\n",
        "     'subsample': '0.7'},\n",
-       "    'ProfilerRuleConfigurations': [{'RuleConfigurationName': 'ProfilerReport-1650241632',\n",
+       "    'ProfilerRuleConfigurations': [{'RuleConfigurationName': 'ProfilerReport-1657728471',\n",
        "      'RuleEvaluatorImage': '895741380848.dkr.ecr.us-west-2.amazonaws.com/sagemaker-debugger-rules:latest',\n",
        "      'RuleParameters': {'rule_to_invoke': 'ProfilerReport'}}],\n",
        "    'ProfilerConfig': {'S3OutputPath': 's3://sagemaker-us-west-2-000000000000/AbaloneTrain'}}},\n",
        "  {'Name': 'AbaloneEval',\n",
        "   'Type': 'Processing',\n",
-       "   'Arguments': {'ProcessingResources': {'ClusterConfig': {'InstanceType': {'Get': 'Parameters.ProcessingInstanceType'},\n",
+       "   'Arguments': {'ProcessingResources': {'ClusterConfig': {'InstanceType': 'ml.m5.xlarge',\n",
        "      'InstanceCount': 1,\n",
        "      'VolumeSizeInGB': 30}},\n",
        "    'AppSpecification': {'ImageUri': '246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-xgboost:1.0-1-cpu-py3',\n",
        "     'ContainerEntrypoint': ['python3',\n",
        "      '/opt/ml/processing/input/code/evaluation.py']},\n",
-       "    'RoleArn': 'arn:aws:iam::000000000000:role/ProdBuildSystemStack-ReleaseBuildRoleFB326D49-QK8LUA2UI1IC',\n",
+       "    'RoleArn': 'arn:aws:iam::000000000000:role/SageMakerRole',\n",
        "    'ProcessingInputs': [{'InputName': 'input-1',\n",
        "      'AppManaged': False,\n",
        "      'S3Input': {'S3Uri': {'Get': 'Steps.AbaloneTrain.ModelArtifacts.S3ModelArtifacts'},\n",
@@ -3118,7 +2052,7 @@
        "       'S3CompressionType': 'None'}},\n",
        "     {'InputName': 'code',\n",
        "      'AppManaged': False,\n",
-       "      'S3Input': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/AbaloneEval-3419a950c93b74404394e09ce2edc51e/input/code/evaluation.py',\n",
+       "      'S3Input': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/script-abalone-eval-2022-07-13-16-07-53-265/input/code/evaluation.py',\n",
        "       'LocalPath': '/opt/ml/processing/input/code',\n",
        "       'S3DataType': 'S3Prefix',\n",
        "       'S3InputMode': 'File',\n",
@@ -3126,7 +2060,7 @@
        "       'S3CompressionType': 'None'}}],\n",
        "    'ProcessingOutputConfig': {'Outputs': [{'OutputName': 'evaluation',\n",
        "       'AppManaged': False,\n",
-       "       'S3Output': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/AbaloneEval-3419a950c93b74404394e09ce2edc51e/output/evaluation',\n",
+       "       'S3Output': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/script-abalone-eval-2022-07-13-16-07-53-265/output/evaluation',\n",
        "        'LocalPath': '/opt/ml/processing/evaluation',\n",
        "        'S3UploadMode': 'EndOfJob'}}]}},\n",
        "   'PropertyFiles': [{'PropertyFileName': 'EvaluationReport',\n",
@@ -3138,14 +2072,15 @@
        "      'LeftValue': {'Std:JsonGet': {'PropertyFile': {'Get': 'Steps.AbaloneEval.PropertyFiles.EvaluationReport'},\n",
        "        'Path': 'regression_metrics.mse.value'}},\n",
        "      'RightValue': {'Get': 'Parameters.MseThreshold'}}],\n",
-       "    'IfSteps': [{'Name': 'AbaloneRegisterModel',\n",
+       "    'IfSteps': [{'Name': 'AbaloneRegisterModel-RegisterModel',\n",
        "      'Type': 'RegisterModel',\n",
        "      'Arguments': {'ModelPackageGroupName': 'AbaloneModelPackageGroupName',\n",
        "       'ModelMetrics': {'ModelQuality': {'Statistics': {'ContentType': 'application/json',\n",
-       "          'S3Uri': 's3://sagemaker-us-west-2-000000000000/AbaloneEval-3419a950c93b74404394e09ce2edc51e/output/evaluation/evaluation.json'}},\n",
+       "          'S3Uri': 's3://sagemaker-us-west-2-000000000000/script-abalone-eval-2022-07-13-16-07-53-265/output/evaluation/evaluation.json'}},\n",
        "        'Bias': {},\n",
        "        'Explainability': {}},\n",
        "       'InferenceSpecification': {'Containers': [{'Image': '246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-xgboost:1.0-1-cpu-py3',\n",
+       "          'Environment': {},\n",
        "          'ModelDataUrl': {'Get': 'Steps.AbaloneTrain.ModelArtifacts.S3ModelArtifacts'}}],\n",
        "        'SupportedContentTypes': ['text/csv'],\n",
        "        'SupportedResponseMIMETypes': ['text/csv'],\n",
@@ -3153,15 +2088,15 @@
        "         'ml.m5.xlarge'],\n",
        "        'SupportedTransformInstanceTypes': ['ml.m5.xlarge']},\n",
        "       'ModelApprovalStatus': {'Get': 'Parameters.ModelApprovalStatus'}}},\n",
-       "     {'Name': 'AbaloneCreateModel',\n",
+       "     {'Name': 'AbaloneCreateModel-CreateModel',\n",
        "      'Type': 'Model',\n",
-       "      'Arguments': {'ExecutionRoleArn': 'arn:aws:iam::000000000000:role/ProdBuildSystemStack-ReleaseBuildRoleFB326D49-QK8LUA2UI1IC',\n",
+       "      'Arguments': {'ExecutionRoleArn': 'arn:aws:iam::000000000000:role/SageMakerRole',\n",
        "       'PrimaryContainer': {'Image': '246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-xgboost:1.0-1-cpu-py3',\n",
        "        'Environment': {},\n",
        "        'ModelDataUrl': {'Get': 'Steps.AbaloneTrain.ModelArtifacts.S3ModelArtifacts'}}}},\n",
        "     {'Name': 'AbaloneTransform',\n",
        "      'Type': 'Transform',\n",
-       "      'Arguments': {'ModelName': {'Get': 'Steps.AbaloneCreateModel.ModelName'},\n",
+       "      'Arguments': {'ModelName': {'Get': 'Steps.AbaloneCreateModel-CreateModel.ModelName'},\n",
        "       'TransformInput': {'DataSource': {'S3DataSource': {'S3DataType': 'S3Prefix',\n",
        "          'S3Uri': {'Get': 'Parameters.BatchData'}}}},\n",
        "       'TransformOutput': {'S3OutputPath': 's3://sagemaker-us-west-2-000000000000/AbaloneTransform'},\n",
@@ -3189,13 +2124,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "73ff3cee",
+   "id": "0b396c63",
    "metadata": {
     "papermill": {
-     "duration": 0.108145,
-     "end_time": "2022-04-18T00:27:13.148804",
+     "duration": 0.093902,
+     "end_time": "2022-07-13T16:07:58.880484",
      "exception": false,
-     "start_time": "2022-04-18T00:27:13.040659",
+     "start_time": "2022-07-13T16:07:58.786582",
      "status": "completed"
     },
     "tags": []
@@ -3209,48 +2144,37 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "1cb0aae9",
+   "id": "944bcafb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:27:13.358032Z",
-     "iopub.status.busy": "2022-04-18T00:27:13.357539Z",
-     "iopub.status.idle": "2022-04-18T00:27:14.732229Z",
-     "shell.execute_reply": "2022-04-18T00:27:14.732881Z"
+     "iopub.execute_input": "2022-07-13T16:07:59.093264Z",
+     "iopub.status.busy": "2022-07-13T16:07:59.092600Z",
+     "iopub.status.idle": "2022-07-13T16:08:00.011312Z",
+     "shell.execute_reply": "2022-07-13T16:08:00.011954Z"
     },
     "papermill": {
-     "duration": 1.476645,
-     "end_time": "2022-04-18T00:27:14.733040",
+     "duration": 1.022416,
+     "end_time": "2022-07-13T16:08:00.012097",
      "exception": false,
-     "start_time": "2022-04-18T00:27:13.256395",
+     "start_time": "2022-07-13T16:07:58.989681",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "No finished training job found associated with this estimator. Please make sure this estimator is only used for building workflow config\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "No finished training job found associated with this estimator. Please make sure this estimator is only used for building workflow config\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
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@@ -3265,13 +2189,13 @@
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@@ -3283,21 +2207,24 @@
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@@ -3307,13 +2234,13 @@
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@@ -3349,19 +2279,19 @@
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-       " 'PipelineExecutionDisplayName': 'execution-1650241635185',\n",
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-       "   'date': 'Mon, 18 Apr 2022 00:27:14 GMT'},\n",
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@@ -3376,13 +2306,13 @@
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@@ -3394,21 +2324,24 @@
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@@ -3418,13 +2351,13 @@
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@@ -3436,21 +2369,24 @@
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@@ -3458,47 +2394,47 @@
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        "[{'StepName': 'AbaloneTransform',\n",
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        " {'StepName': 'AbaloneTrain',\n",
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        "  'StepStatus': 'Succeeded',\n",
        "  'AttemptCount': 0,\n",
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        " {'StepName': 'AbaloneProcess',\n",
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+       "  'Metadata': {'ProcessingJob': {'Arn': 'arn:aws:sagemaker:us-west-2:000000000000:processing-job/pipelines-d84ewltjmxhe-abaloneprocess-alx6e7utl3'}}}]"
       ]
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@@ -3512,13 +2448,13 @@
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@@ -3532,21 +2468,24 @@
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+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
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    "outputs": [
@@ -3554,8 +2493,8 @@
      "name": "stdout",
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      "text": [
-      "{'regression_metrics': {'mse': {'standard_deviation': 2.1549187830947223,\n",
-      "                                'value': 4.64537577872224}}}\n"
+      "{'regression_metrics': {'mse': {'standard_deviation': 2.2157255951663437,\n",
+      "                                'value': 4.913856830660247}}}\n"
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@@ -3573,13 +2512,13 @@
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@@ -3593,21 +2532,24 @@
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@@ -3615,7 +2557,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'StepName': 'AbaloneProcess', 'StartTime': datetime.datetime(2022, 4, 18, 0, 27, 16, 184000, tzinfo=tzlocal()), 'EndTime': datetime.datetime(2022, 4, 18, 0, 31, 34, 404000, tzinfo=tzlocal()), 'StepStatus': 'Succeeded', 'AttemptCount': 0, 'Metadata': {'ProcessingJob': {'Arn': 'arn:aws:sagemaker:us-west-2:000000000000:processing-job/pipelines-3a9dqd5mmh4u-abaloneprocess-ck1tj9s93u'}}}\n"
+      "{'StepName': 'AbaloneProcess', 'StartTime': datetime.datetime(2022, 7, 13, 16, 8, 2, 216000, tzinfo=tzlocal()), 'EndTime': datetime.datetime(2022, 7, 13, 16, 13, 7, 881000, tzinfo=tzlocal()), 'StepStatus': 'Succeeded', 'AttemptCount': 0, 'Metadata': {'ProcessingJob': {'Arn': 'arn:aws:sagemaker:us-west-2:000000000000:processing-job/pipelines-d84ewltjmxhe-abaloneprocess-alx6e7utl3'}}}\n"
      ]
     },
     {
@@ -3649,7 +2591,7 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>s3://...1dc1ff1d9bf9/input/code/preprocessing.py</td>\n",
+       "      <td>s3://...16-07-51-228/input/code/preprocessing.py</td>\n",
        "      <td>Input</td>\n",
        "      <td>DataSet</td>\n",
        "      <td>ContributedTo</td>\n",
@@ -3665,7 +2607,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>24661...om/sagemaker-scikit-learn:1.0-1-cpu-py3</td>\n",
+       "      <td>24661...om/sagemaker-scikit-learn:0.23-1-cpu-py3</td>\n",
        "      <td>Input</td>\n",
        "      <td>Image</td>\n",
        "      <td>ContributedTo</td>\n",
@@ -3673,7 +2615,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>s3://...50a0c87783b887401dc1ff1d9bf9/output/test</td>\n",
+       "      <td>s3://...cess-2022-07-13-16-07-51-228/output/test</td>\n",
        "      <td>Output</td>\n",
        "      <td>DataSet</td>\n",
        "      <td>Produced</td>\n",
@@ -3681,7 +2623,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>s3://...7783b887401dc1ff1d9bf9/output/validation</td>\n",
+       "      <td>s3://...022-07-13-16-07-51-228/output/validation</td>\n",
        "      <td>Output</td>\n",
        "      <td>DataSet</td>\n",
        "      <td>Produced</td>\n",
@@ -3689,7 +2631,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>s3://...0a0c87783b887401dc1ff1d9bf9/output/train</td>\n",
+       "      <td>s3://...ess-2022-07-13-16-07-51-228/output/train</td>\n",
        "      <td>Output</td>\n",
        "      <td>DataSet</td>\n",
        "      <td>Produced</td>\n",
@@ -3701,12 +2643,12 @@
       ],
       "text/plain": [
        "                                        Name/Source Direction     Type  \\\n",
-       "0  s3://...1dc1ff1d9bf9/input/code/preprocessing.py     Input  DataSet   \n",
+       "0  s3://...16-07-51-228/input/code/preprocessing.py     Input  DataSet   \n",
        "1  s3://...000000000000/abalone/abalone-dataset.csv     Input  DataSet   \n",
-       "2  24661...om/sagemaker-scikit-learn:1.0-1-cpu-py3     Input    Image   \n",
-       "3  s3://...50a0c87783b887401dc1ff1d9bf9/output/test    Output  DataSet   \n",
-       "4  s3://...7783b887401dc1ff1d9bf9/output/validation    Output  DataSet   \n",
-       "5  s3://...0a0c87783b887401dc1ff1d9bf9/output/train    Output  DataSet   \n",
+       "2  24661...om/sagemaker-scikit-learn:0.23-1-cpu-py3     Input    Image   \n",
+       "3  s3://...cess-2022-07-13-16-07-51-228/output/test    Output  DataSet   \n",
+       "4  s3://...022-07-13-16-07-51-228/output/validation    Output  DataSet   \n",
+       "5  s3://...ess-2022-07-13-16-07-51-228/output/train    Output  DataSet   \n",
        "\n",
        "  Association Type Lineage Type  \n",
        "0    ContributedTo     artifact  \n",
@@ -3724,7 +2666,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'StepName': 'AbaloneTrain', 'StartTime': datetime.datetime(2022, 4, 18, 0, 31, 35, 376000, tzinfo=tzlocal()), 'EndTime': datetime.datetime(2022, 4, 18, 0, 34, 21, 590000, tzinfo=tzlocal()), 'StepStatus': 'Succeeded', 'AttemptCount': 0, 'Metadata': {'TrainingJob': {'Arn': 'arn:aws:sagemaker:us-west-2:000000000000:training-job/pipelines-3a9dqd5mmh4u-abalonetrain-slip45cf16'}}}\n"
+      "{'StepName': 'AbaloneTrain', 'StartTime': datetime.datetime(2022, 7, 13, 16, 13, 8, 547000, tzinfo=tzlocal()), 'EndTime': datetime.datetime(2022, 7, 13, 16, 16, 41, 447000, tzinfo=tzlocal()), 'StepStatus': 'Succeeded', 'AttemptCount': 0, 'Metadata': {'TrainingJob': {'Arn': 'arn:aws:sagemaker:us-west-2:000000000000:training-job/pipelines-d84ewltjmxhe-abalonetrain-3kyzvpyx11'}}}\n"
      ]
     },
     {
@@ -3758,7 +2700,7 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>s3://...7783b887401dc1ff1d9bf9/output/validation</td>\n",
+       "      <td>s3://...022-07-13-16-07-51-228/output/validation</td>\n",
        "      <td>Input</td>\n",
        "      <td>DataSet</td>\n",
        "      <td>ContributedTo</td>\n",
@@ -3766,7 +2708,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>s3://...0a0c87783b887401dc1ff1d9bf9/output/train</td>\n",
+       "      <td>s3://...ess-2022-07-13-16-07-51-228/output/train</td>\n",
        "      <td>Input</td>\n",
        "      <td>DataSet</td>\n",
        "      <td>ContributedTo</td>\n",
@@ -3782,7 +2724,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>s3://...loneTrain-SLIp45cF16/output/model.tar.gz</td>\n",
+       "      <td>s3://...loneTrain-3kYZvpYx11/output/model.tar.gz</td>\n",
        "      <td>Output</td>\n",
        "      <td>Model</td>\n",
        "      <td>Produced</td>\n",
@@ -3794,10 +2736,10 @@
       ],
       "text/plain": [
        "                                        Name/Source Direction     Type  \\\n",
-       "0  s3://...7783b887401dc1ff1d9bf9/output/validation     Input  DataSet   \n",
-       "1  s3://...0a0c87783b887401dc1ff1d9bf9/output/train     Input  DataSet   \n",
+       "0  s3://...022-07-13-16-07-51-228/output/validation     Input  DataSet   \n",
+       "1  s3://...ess-2022-07-13-16-07-51-228/output/train     Input  DataSet   \n",
        "2  24661...naws.com/sagemaker-xgboost:1.0-1-cpu-py3     Input    Image   \n",
-       "3  s3://...loneTrain-SLIp45cF16/output/model.tar.gz    Output    Model   \n",
+       "3  s3://...loneTrain-3kYZvpYx11/output/model.tar.gz    Output    Model   \n",
        "\n",
        "  Association Type Lineage Type  \n",
        "0    ContributedTo     artifact  \n",
@@ -3813,7 +2755,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'StepName': 'AbaloneEval', 'StartTime': datetime.datetime(2022, 4, 18, 0, 34, 22, 219000, tzinfo=tzlocal()), 'EndTime': datetime.datetime(2022, 4, 18, 0, 38, 18, 302000, tzinfo=tzlocal()), 'StepStatus': 'Succeeded', 'AttemptCount': 0, 'Metadata': {'ProcessingJob': {'Arn': 'arn:aws:sagemaker:us-west-2:000000000000:processing-job/pipelines-3a9dqd5mmh4u-abaloneeval-rqz0ph05st'}}}\n"
+      "{'StepName': 'AbaloneEval', 'StartTime': datetime.datetime(2022, 7, 13, 16, 16, 42, 221000, tzinfo=tzlocal()), 'EndTime': datetime.datetime(2022, 7, 13, 16, 21, 25, 344000, tzinfo=tzlocal()), 'StepStatus': 'Succeeded', 'AttemptCount': 0, 'Metadata': {'ProcessingJob': {'Arn': 'arn:aws:sagemaker:us-west-2:000000000000:processing-job/pipelines-d84ewltjmxhe-abaloneeval-gq8u48ijdl'}}}\n"
      ]
     },
     {
@@ -3847,7 +2789,7 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>s3://...394e09ce2edc51e/input/code/evaluation.py</td>\n",
+       "      <td>s3://...13-16-07-53-265/input/code/evaluation.py</td>\n",
        "      <td>Input</td>\n",
        "      <td>DataSet</td>\n",
        "      <td>ContributedTo</td>\n",
@@ -3855,7 +2797,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>s3://...50a0c87783b887401dc1ff1d9bf9/output/test</td>\n",
+       "      <td>s3://...cess-2022-07-13-16-07-51-228/output/test</td>\n",
        "      <td>Input</td>\n",
        "      <td>DataSet</td>\n",
        "      <td>ContributedTo</td>\n",
@@ -3863,7 +2805,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>s3://...loneTrain-SLIp45cF16/output/model.tar.gz</td>\n",
+       "      <td>s3://...loneTrain-3kYZvpYx11/output/model.tar.gz</td>\n",
        "      <td>Input</td>\n",
        "      <td>Model</td>\n",
        "      <td>ContributedTo</td>\n",
@@ -3879,7 +2821,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>s3://...3b74404394e09ce2edc51e/output/evaluation</td>\n",
+       "      <td>s3://...022-07-13-16-07-53-265/output/evaluation</td>\n",
        "      <td>Output</td>\n",
        "      <td>DataSet</td>\n",
        "      <td>Produced</td>\n",
@@ -3891,11 +2833,11 @@
       ],
       "text/plain": [
        "                                        Name/Source Direction     Type  \\\n",
-       "0  s3://...394e09ce2edc51e/input/code/evaluation.py     Input  DataSet   \n",
-       "1  s3://...50a0c87783b887401dc1ff1d9bf9/output/test     Input  DataSet   \n",
-       "2  s3://...loneTrain-SLIp45cF16/output/model.tar.gz     Input    Model   \n",
+       "0  s3://...13-16-07-53-265/input/code/evaluation.py     Input  DataSet   \n",
+       "1  s3://...cess-2022-07-13-16-07-51-228/output/test     Input  DataSet   \n",
+       "2  s3://...loneTrain-3kYZvpYx11/output/model.tar.gz     Input    Model   \n",
        "3  24661...naws.com/sagemaker-xgboost:1.0-1-cpu-py3     Input    Image   \n",
-       "4  s3://...3b74404394e09ce2edc51e/output/evaluation    Output  DataSet   \n",
+       "4  s3://...022-07-13-16-07-53-265/output/evaluation    Output  DataSet   \n",
        "\n",
        "  Association Type Lineage Type  \n",
        "0    ContributedTo     artifact  \n",
@@ -3912,7 +2854,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'StepName': 'AbaloneMSECond', 'StartTime': datetime.datetime(2022, 4, 18, 0, 38, 19, 251000, tzinfo=tzlocal()), 'EndTime': datetime.datetime(2022, 4, 18, 0, 38, 19, 670000, tzinfo=tzlocal()), 'StepStatus': 'Succeeded', 'AttemptCount': 0, 'Metadata': {'Condition': {'Outcome': 'True'}}}\n"
+      "{'StepName': 'AbaloneMSECond', 'StartTime': datetime.datetime(2022, 7, 13, 16, 21, 25, 972000, tzinfo=tzlocal()), 'EndTime': datetime.datetime(2022, 7, 13, 16, 21, 26, 697000, tzinfo=tzlocal()), 'StepStatus': 'Succeeded', 'AttemptCount': 0, 'Metadata': {'Condition': {'Outcome': 'True'}}}\n"
      ]
     },
     {
@@ -3928,7 +2870,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'StepName': 'AbaloneRegisterModel', 'StartTime': datetime.datetime(2022, 4, 18, 0, 38, 20, 252000, tzinfo=tzlocal()), 'EndTime': datetime.datetime(2022, 4, 18, 0, 38, 22, 18000, tzinfo=tzlocal()), 'StepStatus': 'Succeeded', 'AttemptCount': 0, 'Metadata': {'RegisterModel': {'Arn': 'arn:aws:sagemaker:us-west-2:000000000000:model-package/abalonemodelpackagegroupname/691'}}}\n"
+      "{'StepName': 'AbaloneCreateModel-CreateModel', 'StartTime': datetime.datetime(2022, 7, 13, 16, 21, 27, 227000, tzinfo=tzlocal()), 'EndTime': datetime.datetime(2022, 7, 13, 16, 21, 28, 88000, tzinfo=tzlocal()), 'StepStatus': 'Succeeded', 'AttemptCount': 0, 'Metadata': {'Model': {'Arn': 'arn:aws:sagemaker:us-west-2:000000000000:model/pipelines-d84ewltjmxhe-abalonecreatemodel-c-dgvk2pwxwn'}}}\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "None"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'StepName': 'AbaloneRegisterModel-RegisterModel', 'StartTime': datetime.datetime(2022, 7, 13, 16, 21, 27, 227000, tzinfo=tzlocal()), 'EndTime': datetime.datetime(2022, 7, 13, 16, 21, 27, 908000, tzinfo=tzlocal()), 'StepStatus': 'Succeeded', 'AttemptCount': 0, 'Metadata': {'RegisterModel': {'Arn': 'arn:aws:sagemaker:us-west-2:000000000000:model-package/abalonemodelpackagegroupname/1'}}}\n"
      ]
     },
     {
@@ -3962,7 +2920,7 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>s3://...loneTrain-SLIp45cF16/output/model.tar.gz</td>\n",
+       "      <td>s3://...loneTrain-3kYZvpYx11/output/model.tar.gz</td>\n",
        "      <td>Input</td>\n",
        "      <td>Model</td>\n",
        "      <td>ContributedTo</td>\n",
@@ -3978,7 +2936,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>abalonemodelpackagegroupname-691-PendingManual...</td>\n",
+       "      <td>abalonemodelpackagegroupname-1-PendingManualAp...</td>\n",
        "      <td>Input</td>\n",
        "      <td>Approval</td>\n",
        "      <td>ContributedTo</td>\n",
@@ -3986,7 +2944,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>AbaloneModelPackageGroupName-1607556271-aws-mo...</td>\n",
+       "      <td>AbaloneModelPackageGroupName-1657729287-aws-mo...</td>\n",
        "      <td>Output</td>\n",
        "      <td>ModelGroup</td>\n",
        "      <td>AssociatedWith</td>\n",
@@ -3998,10 +2956,10 @@
       ],
       "text/plain": [
        "                                         Name/Source Direction        Type  \\\n",
-       "0   s3://...loneTrain-SLIp45cF16/output/model.tar.gz     Input       Model   \n",
+       "0   s3://...loneTrain-3kYZvpYx11/output/model.tar.gz     Input       Model   \n",
        "1   24661...naws.com/sagemaker-xgboost:1.0-1-cpu-py3     Input       Image   \n",
-       "2  abalonemodelpackagegroupname-691-PendingManual...     Input    Approval   \n",
-       "3  AbaloneModelPackageGroupName-1607556271-aws-mo...    Output  ModelGroup   \n",
+       "2  abalonemodelpackagegroupname-1-PendingManualAp...     Input    Approval   \n",
+       "3  AbaloneModelPackageGroupName-1657729287-aws-mo...    Output  ModelGroup   \n",
        "\n",
        "  Association Type Lineage Type  \n",
        "0    ContributedTo     artifact  \n",
@@ -4017,23 +2975,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'StepName': 'AbaloneCreateModel', 'StartTime': datetime.datetime(2022, 4, 18, 0, 38, 20, 252000, tzinfo=tzlocal()), 'EndTime': datetime.datetime(2022, 4, 18, 0, 38, 21, 188000, tzinfo=tzlocal()), 'StepStatus': 'Succeeded', 'AttemptCount': 0, 'Metadata': {'Model': {'Arn': 'arn:aws:sagemaker:us-west-2:000000000000:model/pipelines-3a9dqd5mmh4u-abalonecreatemodel-nr28ooxmjn'}}}\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "None"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'StepName': 'AbaloneTransform', 'StartTime': datetime.datetime(2022, 4, 18, 0, 38, 21, 710000, tzinfo=tzlocal()), 'EndTime': datetime.datetime(2022, 4, 18, 0, 43, 5, 175000, tzinfo=tzlocal()), 'StepStatus': 'Succeeded', 'AttemptCount': 0, 'Metadata': {'TransformJob': {'Arn': 'arn:aws:sagemaker:us-west-2:000000000000:transform-job/pipelines-3a9dqd5mmh4u-abalonetransform-shmqcpjkhl'}}}\n"
+      "{'StepName': 'AbaloneTransform', 'StartTime': datetime.datetime(2022, 7, 13, 16, 21, 28, 497000, tzinfo=tzlocal()), 'EndTime': datetime.datetime(2022, 7, 13, 16, 26, 41, 240000, tzinfo=tzlocal()), 'StepStatus': 'Succeeded', 'AttemptCount': 0, 'Metadata': {'TransformJob': {'Arn': 'arn:aws:sagemaker:us-west-2:000000000000:transform-job/pipelines-d84ewltjmxhe-abalonetransform-hjf5dpst8n'}}}\n"
      ]
     },
     {
@@ -4067,7 +3009,7 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>s3://...loneTrain-SLIp45cF16/output/model.tar.gz</td>\n",
+       "      <td>s3://...loneTrain-3kYZvpYx11/output/model.tar.gz</td>\n",
        "      <td>Input</td>\n",
        "      <td>Model</td>\n",
        "      <td>ContributedTo</td>\n",
@@ -4103,7 +3045,7 @@
       ],
       "text/plain": [
        "                                        Name/Source Direction     Type  \\\n",
-       "0  s3://...loneTrain-SLIp45cF16/output/model.tar.gz     Input    Model   \n",
+       "0  s3://...loneTrain-3kYZvpYx11/output/model.tar.gz     Input    Model   \n",
        "1  24661...naws.com/sagemaker-xgboost:1.0-1-cpu-py3     Input    Image   \n",
        "2  s3://...1695447989/abalone/abalone-dataset-batch     Input  DataSet   \n",
        "3  s3://...-us-west-2-000000000000/AbaloneTransform    Output  DataSet   \n",
@@ -4133,13 +3075,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b0f2959e",
+   "id": "76c5a401",
    "metadata": {
     "papermill": {
-     "duration": 0.085763,
-     "end_time": "2022-04-18T00:43:57.076519",
+     "duration": 0.045193,
+     "end_time": "2022-07-13T16:27:49.331609",
      "exception": false,
-     "start_time": "2022-04-18T00:43:56.990756",
+     "start_time": "2022-07-13T16:27:49.286416",
      "status": "completed"
     },
     "tags": []
@@ -4155,28 +3097,30 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "id": "7900a64c",
+   "id": "78b12dc2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:43:57.215340Z",
-     "iopub.status.busy": "2022-04-18T00:43:57.214645Z",
-     "iopub.status.idle": "2022-04-18T00:43:57.436693Z",
-     "shell.execute_reply": "2022-04-18T00:43:57.435572Z"
+     "iopub.execute_input": "2022-07-13T16:27:49.433540Z",
+     "iopub.status.busy": "2022-07-13T16:27:49.432536Z",
+     "iopub.status.idle": "2022-07-13T16:27:50.039997Z",
+     "shell.execute_reply": "2022-07-13T16:27:50.040356Z"
     },
     "papermill": {
-     "duration": 0.289411,
-     "end_time": "2022-04-18T00:43:57.436836",
+     "duration": 0.660009,
+     "end_time": "2022-07-13T16:27:50.040517",
      "exception": false,
-     "start_time": "2022-04-18T00:43:57.147425",
+     "start_time": "2022-07-13T16:27:49.380508",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [],
    "source": [
     "execution = pipeline.start(\n",
     "    parameters=dict(\n",
-    "        ProcessingInstanceType=\"ml.c5.xlarge\",\n",
     "        ModelApprovalStatus=\"Approved\",\n",
     "    )\n",
     ")"
@@ -4185,21 +3129,24 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "id": "ac422422",
+   "id": "c053eb92",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:43:57.740793Z",
-     "iopub.status.busy": "2022-04-18T00:43:57.737704Z",
-     "iopub.status.idle": "2022-04-18T00:59:31.046838Z",
-     "shell.execute_reply": "2022-04-18T00:59:31.044279Z"
+     "iopub.execute_input": "2022-07-13T16:27:50.127383Z",
+     "iopub.status.busy": "2022-07-13T16:27:50.126899Z",
+     "iopub.status.idle": "2022-07-13T16:45:59.346748Z",
+     "shell.execute_reply": "2022-07-13T16:45:59.346269Z"
     },
     "papermill": {
-     "duration": 933.469555,
-     "end_time": "2022-04-18T00:59:31.046992",
+     "duration": 1089.265129,
+     "end_time": "2022-07-13T16:45:59.346876",
      "exception": false,
-     "start_time": "2022-04-18T00:43:57.577437",
+     "start_time": "2022-07-13T16:27:50.081747",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [],
@@ -4210,21 +3157,24 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "7d9dec71",
+   "id": "e498d8a3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:59:31.185427Z",
-     "iopub.status.busy": "2022-04-18T00:59:31.184766Z",
-     "iopub.status.idle": "2022-04-18T00:59:31.296309Z",
-     "shell.execute_reply": "2022-04-18T00:59:31.296777Z"
+     "iopub.execute_input": "2022-07-13T16:45:59.460546Z",
+     "iopub.status.busy": "2022-07-13T16:45:59.459990Z",
+     "iopub.status.idle": "2022-07-13T16:45:59.682468Z",
+     "shell.execute_reply": "2022-07-13T16:45:59.682907Z"
     },
     "papermill": {
-     "duration": 0.178516,
-     "end_time": "2022-04-18T00:59:31.296922",
+     "duration": 0.290742,
+     "end_time": "2022-07-13T16:45:59.683052",
      "exception": false,
-     "start_time": "2022-04-18T00:59:31.118406",
+     "start_time": "2022-07-13T16:45:59.392310",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [
@@ -4232,47 +3182,47 @@
      "data": {
       "text/plain": [
        "[{'StepName': 'AbaloneTransform',\n",
-       "  'StartTime': datetime.datetime(2022, 4, 18, 0, 55, 17, 609000, tzinfo=tzlocal()),\n",
-       "  'EndTime': datetime.datetime(2022, 4, 18, 0, 59, 24, 521000, tzinfo=tzlocal()),\n",
+       "  'StartTime': datetime.datetime(2022, 7, 13, 16, 40, 37, 410000, tzinfo=tzlocal()),\n",
+       "  'EndTime': datetime.datetime(2022, 7, 13, 16, 45, 35, 417000, tzinfo=tzlocal()),\n",
        "  'StepStatus': 'Succeeded',\n",
        "  'AttemptCount': 0,\n",
-       "  'Metadata': {'TransformJob': {'Arn': 'arn:aws:sagemaker:us-west-2:000000000000:transform-job/pipelines-9275502uvwe2-abalonetransform-sdqf868czf'}}},\n",
-       " {'StepName': 'AbaloneCreateModel',\n",
-       "  'StartTime': datetime.datetime(2022, 4, 18, 0, 55, 16, 365000, tzinfo=tzlocal()),\n",
-       "  'EndTime': datetime.datetime(2022, 4, 18, 0, 55, 17, 124000, tzinfo=tzlocal()),\n",
+       "  'Metadata': {'TransformJob': {'Arn': 'arn:aws:sagemaker:us-west-2:000000000000:transform-job/pipelines-ib5u1ar77ell-abalonetransform-ybfqj1idhf'}}},\n",
+       " {'StepName': 'AbaloneRegisterModel-RegisterModel',\n",
+       "  'StartTime': datetime.datetime(2022, 7, 13, 16, 40, 35, 659000, tzinfo=tzlocal()),\n",
+       "  'EndTime': datetime.datetime(2022, 7, 13, 16, 40, 36, 745000, tzinfo=tzlocal()),\n",
        "  'StepStatus': 'Succeeded',\n",
        "  'AttemptCount': 0,\n",
-       "  'Metadata': {'Model': {'Arn': 'arn:aws:sagemaker:us-west-2:000000000000:model/pipelines-9275502uvwe2-abalonecreatemodel-qbuz4uirnh'}}},\n",
-       " {'StepName': 'AbaloneRegisterModel',\n",
-       "  'StartTime': datetime.datetime(2022, 4, 18, 0, 55, 16, 365000, tzinfo=tzlocal()),\n",
-       "  'EndTime': datetime.datetime(2022, 4, 18, 0, 55, 16, 953000, tzinfo=tzlocal()),\n",
+       "  'Metadata': {'RegisterModel': {'Arn': 'arn:aws:sagemaker:us-west-2:000000000000:model-package/abalonemodelpackagegroupname/2'}}},\n",
+       " {'StepName': 'AbaloneCreateModel-CreateModel',\n",
+       "  'StartTime': datetime.datetime(2022, 7, 13, 16, 40, 35, 659000, tzinfo=tzlocal()),\n",
+       "  'EndTime': datetime.datetime(2022, 7, 13, 16, 40, 36, 840000, tzinfo=tzlocal()),\n",
        "  'StepStatus': 'Succeeded',\n",
        "  'AttemptCount': 0,\n",
-       "  'Metadata': {'RegisterModel': {'Arn': 'arn:aws:sagemaker:us-west-2:000000000000:model-package/abalonemodelpackagegroupname/692'}}},\n",
+       "  'Metadata': {'Model': {'Arn': 'arn:aws:sagemaker:us-west-2:000000000000:model/pipelines-ib5u1ar77ell-abalonecreatemodel-c-z2inp9am2j'}}},\n",
        " {'StepName': 'AbaloneMSECond',\n",
-       "  'StartTime': datetime.datetime(2022, 4, 18, 0, 55, 15, 608000, tzinfo=tzlocal()),\n",
-       "  'EndTime': datetime.datetime(2022, 4, 18, 0, 55, 15, 837000, tzinfo=tzlocal()),\n",
+       "  'StartTime': datetime.datetime(2022, 7, 13, 16, 40, 34, 694000, tzinfo=tzlocal()),\n",
+       "  'EndTime': datetime.datetime(2022, 7, 13, 16, 40, 34, 978000, tzinfo=tzlocal()),\n",
        "  'StepStatus': 'Succeeded',\n",
        "  'AttemptCount': 0,\n",
        "  'Metadata': {'Condition': {'Outcome': 'True'}}},\n",
        " {'StepName': 'AbaloneEval',\n",
-       "  'StartTime': datetime.datetime(2022, 4, 18, 0, 51, 6, 271000, tzinfo=tzlocal()),\n",
-       "  'EndTime': datetime.datetime(2022, 4, 18, 0, 55, 14, 745000, tzinfo=tzlocal()),\n",
+       "  'StartTime': datetime.datetime(2022, 7, 13, 16, 35, 44, 913000, tzinfo=tzlocal()),\n",
+       "  'EndTime': datetime.datetime(2022, 7, 13, 16, 40, 34, 151000, tzinfo=tzlocal()),\n",
        "  'StepStatus': 'Succeeded',\n",
        "  'AttemptCount': 0,\n",
-       "  'Metadata': {'ProcessingJob': {'Arn': 'arn:aws:sagemaker:us-west-2:000000000000:processing-job/pipelines-9275502uvwe2-abaloneeval-v43er0jak2'}}},\n",
+       "  'Metadata': {'ProcessingJob': {'Arn': 'arn:aws:sagemaker:us-west-2:000000000000:processing-job/pipelines-ib5u1ar77ell-abaloneeval-d1olj9t5kb'}}},\n",
        " {'StepName': 'AbaloneTrain',\n",
-       "  'StartTime': datetime.datetime(2022, 4, 18, 0, 48, 20, 426000, tzinfo=tzlocal()),\n",
-       "  'EndTime': datetime.datetime(2022, 4, 18, 0, 51, 5, 564000, tzinfo=tzlocal()),\n",
+       "  'StartTime': datetime.datetime(2022, 7, 13, 16, 32, 39, 527000, tzinfo=tzlocal()),\n",
+       "  'EndTime': datetime.datetime(2022, 7, 13, 16, 35, 43, 780000, tzinfo=tzlocal()),\n",
        "  'StepStatus': 'Succeeded',\n",
        "  'AttemptCount': 0,\n",
-       "  'Metadata': {'TrainingJob': {'Arn': 'arn:aws:sagemaker:us-west-2:000000000000:training-job/pipelines-9275502uvwe2-abalonetrain-6rtveegego'}}},\n",
+       "  'Metadata': {'TrainingJob': {'Arn': 'arn:aws:sagemaker:us-west-2:000000000000:training-job/pipelines-ib5u1ar77ell-abalonetrain-hqep8kakhv'}}},\n",
        " {'StepName': 'AbaloneProcess',\n",
-       "  'StartTime': datetime.datetime(2022, 4, 18, 0, 43, 58, 687000, tzinfo=tzlocal()),\n",
-       "  'EndTime': datetime.datetime(2022, 4, 18, 0, 48, 18, 976000, tzinfo=tzlocal()),\n",
+       "  'StartTime': datetime.datetime(2022, 7, 13, 16, 27, 50, 852000, tzinfo=tzlocal()),\n",
+       "  'EndTime': datetime.datetime(2022, 7, 13, 16, 32, 38, 578000, tzinfo=tzlocal()),\n",
        "  'StepStatus': 'Succeeded',\n",
        "  'AttemptCount': 0,\n",
-       "  'Metadata': {'ProcessingJob': {'Arn': 'arn:aws:sagemaker:us-west-2:000000000000:processing-job/pipelines-9275502uvwe2-abaloneprocess-vynjsz7vps'}}}]"
+       "  'Metadata': {'ProcessingJob': {'Arn': 'arn:aws:sagemaker:us-west-2:000000000000:processing-job/pipelines-ib5u1ar77ell-abaloneprocess-pfwnwtmeef'}}}]"
       ]
      },
      "execution_count": 34,
@@ -4286,13 +3236,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bcda6312",
+   "id": "c1f9631d",
    "metadata": {
     "papermill": {
-     "duration": 0.116815,
-     "end_time": "2022-04-18T00:59:31.473495",
+     "duration": 0.043074,
+     "end_time": "2022-07-13T16:45:59.771340",
      "exception": false,
-     "start_time": "2022-04-18T00:59:31.356680",
+     "start_time": "2022-07-13T16:45:59.728266",
      "status": "completed"
     },
     "tags": []
@@ -4304,22 +3254,19 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "a844f423",
+   "id": "ff7f6820",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:59:31.674910Z",
-     "iopub.status.busy": "2022-04-18T00:59:31.674308Z",
-     "iopub.status.idle": "2022-04-18T00:59:31.880748Z",
-     "shell.execute_reply": "2022-04-18T00:59:31.881195Z"
-    },
-    "jupyter": {
-     "outputs_hidden": false
+     "iopub.execute_input": "2022-07-13T16:45:59.870874Z",
+     "iopub.status.busy": "2022-07-13T16:45:59.869936Z",
+     "iopub.status.idle": "2022-07-13T16:46:00.219548Z",
+     "shell.execute_reply": "2022-07-13T16:46:00.219917Z"
     },
     "papermill": {
-     "duration": 0.283862,
-     "end_time": "2022-04-18T00:59:31.881345",
+     "duration": 0.403974,
+     "end_time": "2022-07-13T16:46:00.220070",
      "exception": false,
-     "start_time": "2022-04-18T00:59:31.597483",
+     "start_time": "2022-07-13T16:45:59.816096",
      "status": "completed"
     },
     "pycharm": {
@@ -4334,13 +3281,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ff01c5f8",
+   "id": "c07f95d7",
    "metadata": {
     "papermill": {
-     "duration": 0.053964,
-     "end_time": "2022-04-18T00:59:32.002325",
+     "duration": 0.078875,
+     "end_time": "2022-07-13T16:46:00.342112",
      "exception": false,
-     "start_time": "2022-04-18T00:59:31.948361",
+     "start_time": "2022-07-13T16:46:00.263237",
      "status": "completed"
     },
     "tags": []
@@ -4352,22 +3299,19 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "9cc025f7",
+   "id": "1f244f42",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:59:32.111415Z",
-     "iopub.status.busy": "2022-04-18T00:59:32.110753Z",
-     "iopub.status.idle": "2022-04-18T01:11:34.809867Z",
-     "shell.execute_reply": "2022-04-18T01:11:34.810467Z"
-    },
-    "jupyter": {
-     "outputs_hidden": false
+     "iopub.execute_input": "2022-07-13T16:46:00.433733Z",
+     "iopub.status.busy": "2022-07-13T16:46:00.433177Z",
+     "iopub.status.idle": "2022-07-13T16:59:07.315832Z",
+     "shell.execute_reply": "2022-07-13T16:59:07.316625Z"
     },
     "papermill": {
-     "duration": 722.756876,
-     "end_time": "2022-04-18T01:11:34.810765",
+     "duration": 786.932424,
+     "end_time": "2022-07-13T16:59:07.316785",
      "exception": false,
-     "start_time": "2022-04-18T00:59:32.053889",
+     "start_time": "2022-07-13T16:46:00.384361",
      "status": "completed"
     },
     "pycharm": {
@@ -4394,22 +3338,19 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "8dd35ff3",
+   "id": "62081cff",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T01:11:34.938869Z",
-     "iopub.status.busy": "2022-04-18T01:11:34.938199Z",
-     "iopub.status.idle": "2022-04-18T01:11:35.018383Z",
-     "shell.execute_reply": "2022-04-18T01:11:35.019866Z"
-    },
-    "jupyter": {
-     "outputs_hidden": false
+     "iopub.execute_input": "2022-07-13T16:59:07.436018Z",
+     "iopub.status.busy": "2022-07-13T16:59:07.435161Z",
+     "iopub.status.idle": "2022-07-13T16:59:07.610216Z",
+     "shell.execute_reply": "2022-07-13T16:59:07.610648Z"
     },
     "papermill": {
-     "duration": 0.147713,
-     "end_time": "2022-04-18T01:11:35.020129",
+     "duration": 0.238686,
+     "end_time": "2022-07-13T16:59:07.610792",
      "exception": false,
-     "start_time": "2022-04-18T01:11:34.872416",
+     "start_time": "2022-07-13T16:59:07.372106",
      "status": "completed"
     },
     "pycharm": {
@@ -4422,36 +3363,36 @@
      "data": {
       "text/plain": [
        "[{'StepName': 'AbaloneMSEFail',\n",
-       "  'StartTime': datetime.datetime(2022, 4, 18, 1, 11, 4, 749000, tzinfo=tzlocal()),\n",
-       "  'EndTime': datetime.datetime(2022, 4, 18, 1, 11, 5, 139000, tzinfo=tzlocal()),\n",
+       "  'StartTime': datetime.datetime(2022, 7, 13, 16, 58, 50, 492000, tzinfo=tzlocal()),\n",
+       "  'EndTime': datetime.datetime(2022, 7, 13, 16, 58, 51, 63000, tzinfo=tzlocal()),\n",
        "  'StepStatus': 'Failed',\n",
        "  'AttemptCount': 0,\n",
        "  'FailureReason': 'Execution failed due to MSE > 3.0',\n",
        "  'Metadata': {'Fail': {'ErrorMessage': 'Execution failed due to MSE > 3.0'}}},\n",
        " {'StepName': 'AbaloneMSECond',\n",
-       "  'StartTime': datetime.datetime(2022, 4, 18, 1, 11, 3, 792000, tzinfo=tzlocal()),\n",
-       "  'EndTime': datetime.datetime(2022, 4, 18, 1, 11, 4, 204000, tzinfo=tzlocal()),\n",
+       "  'StartTime': datetime.datetime(2022, 7, 13, 16, 58, 49, 539000, tzinfo=tzlocal()),\n",
+       "  'EndTime': datetime.datetime(2022, 7, 13, 16, 58, 49, 926000, tzinfo=tzlocal()),\n",
        "  'StepStatus': 'Succeeded',\n",
        "  'AttemptCount': 0,\n",
        "  'Metadata': {'Condition': {'Outcome': 'False'}}},\n",
        " {'StepName': 'AbaloneEval',\n",
-       "  'StartTime': datetime.datetime(2022, 4, 18, 1, 6, 43, 271000, tzinfo=tzlocal()),\n",
-       "  'EndTime': datetime.datetime(2022, 4, 18, 1, 11, 2, 665000, tzinfo=tzlocal()),\n",
+       "  'StartTime': datetime.datetime(2022, 7, 13, 16, 54, 6, 136000, tzinfo=tzlocal()),\n",
+       "  'EndTime': datetime.datetime(2022, 7, 13, 16, 58, 49, 30000, tzinfo=tzlocal()),\n",
        "  'StepStatus': 'Succeeded',\n",
        "  'AttemptCount': 0,\n",
-       "  'Metadata': {'ProcessingJob': {'Arn': 'arn:aws:sagemaker:us-west-2:000000000000:processing-job/pipelines-0xj80rygp8xr-abaloneeval-ffrpzxrakl'}}},\n",
+       "  'Metadata': {'ProcessingJob': {'Arn': 'arn:aws:sagemaker:us-west-2:000000000000:processing-job/pipelines-hswk7n1v1oju-abaloneeval-z6banrjbgd'}}},\n",
        " {'StepName': 'AbaloneTrain',\n",
-       "  'StartTime': datetime.datetime(2022, 4, 18, 1, 3, 53, 900000, tzinfo=tzlocal()),\n",
-       "  'EndTime': datetime.datetime(2022, 4, 18, 1, 6, 42, 35000, tzinfo=tzlocal()),\n",
+       "  'StartTime': datetime.datetime(2022, 7, 13, 16, 50, 55, 273000, tzinfo=tzlocal()),\n",
+       "  'EndTime': datetime.datetime(2022, 7, 13, 16, 54, 5, 654000, tzinfo=tzlocal()),\n",
        "  'StepStatus': 'Succeeded',\n",
        "  'AttemptCount': 0,\n",
-       "  'Metadata': {'TrainingJob': {'Arn': 'arn:aws:sagemaker:us-west-2:000000000000:training-job/pipelines-0xj80rygp8xr-abalonetrain-u6uyl1wglo'}}},\n",
+       "  'Metadata': {'TrainingJob': {'Arn': 'arn:aws:sagemaker:us-west-2:000000000000:training-job/pipelines-hswk7n1v1oju-abalonetrain-ig59hd6pjy'}}},\n",
        " {'StepName': 'AbaloneProcess',\n",
-       "  'StartTime': datetime.datetime(2022, 4, 18, 0, 59, 32, 429000, tzinfo=tzlocal()),\n",
-       "  'EndTime': datetime.datetime(2022, 4, 18, 1, 3, 53, 361000, tzinfo=tzlocal()),\n",
+       "  'StartTime': datetime.datetime(2022, 7, 13, 16, 46, 1, 213000, tzinfo=tzlocal()),\n",
+       "  'EndTime': datetime.datetime(2022, 7, 13, 16, 50, 54, 782000, tzinfo=tzlocal()),\n",
        "  'StepStatus': 'Succeeded',\n",
        "  'AttemptCount': 0,\n",
-       "  'Metadata': {'ProcessingJob': {'Arn': 'arn:aws:sagemaker:us-west-2:000000000000:processing-job/pipelines-0xj80rygp8xr-abaloneprocess-cwlzrt0ero'}}}]"
+       "  'Metadata': {'ProcessingJob': {'Arn': 'arn:aws:sagemaker:us-west-2:000000000000:processing-job/pipelines-hswk7n1v1oju-abaloneprocess-npvk1jznbk'}}}]"
       ]
      },
      "execution_count": 37,
@@ -4467,9 +3408,9 @@
  "metadata": {
   "instance_type": "ml.t3.medium",
   "kernelspec": {
-   "display_name": "Python 3 (Data Science)",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east-2:000000000000:image/datascience-1.0"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -4485,19 +3426,19 @@
   },
   "papermill": {
    "default_parameters": {},
-   "duration": 2698.146273,
-   "end_time": "2022-04-18T01:11:36.131062",
+   "duration": 3085.109473,
+   "end_time": "2022-07-13T16:59:08.278091",
    "environment_variables": {},
    "exception": null,
    "input_path": "sagemaker-pipelines-preprocess-train-evaluate-batch-transform.ipynb",
-   "output_path": "/opt/ml/processing/output/sagemaker-pipelines-preprocess-train-evaluate-batch-transform-2022-04-18-00-13-04.ipynb",
+   "output_path": "/opt/ml/processing/output/sagemaker-pipelines-preprocess-train-evaluate-batch-transform-2022-07-13-15-54-21.ipynb",
    "parameters": {
     "kms_key": "arn:aws:kms:us-west-2:000000000000:1234abcd-12ab-34cd-56ef-1234567890ab"
    },
-   "start_time": "2022-04-18T00:26:37.984789",
+   "start_time": "2022-07-13T16:07:43.168618",
    "version": "2.3.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/sagemaker-pipelines/tabular/lambda-step/iam_helper.py b/sagemaker-pipelines/tabular/lambda-step/iam_helper.py
index 51ff704ff0..ec0717e582 100644
--- a/sagemaker-pipelines/tabular/lambda-step/iam_helper.py
+++ b/sagemaker-pipelines/tabular/lambda-step/iam_helper.py
@@ -1,42 +1,42 @@
 import boto3
 import json
 
-iam = boto3.client('iam')
+iam = boto3.client("iam")
+
 
 def create_lambda_role(role_name):
     try:
         response = iam.create_role(
-            RoleName = role_name,
-            AssumeRolePolicyDocument = json.dumps({
-                "Version": "2012-10-17",
-                "Statement": [
-                    {
-                        "Effect": "Allow",
-                        "Principal": {
-                            "Service": "lambda.amazonaws.com"
-                        },
-                        "Action": "sts:AssumeRole"
-                    }
-                ]
-            }),
-            Description='Role for Lambda to call SageMaker functions'
+            RoleName=role_name,
+            AssumeRolePolicyDocument=json.dumps(
+                {
+                    "Version": "2012-10-17",
+                    "Statement": [
+                        {
+                            "Effect": "Allow",
+                            "Principal": {"Service": "lambda.amazonaws.com"},
+                            "Action": "sts:AssumeRole",
+                        }
+                    ],
+                }
+            ),
+            Description="Role for Lambda to call SageMaker functions",
         )
 
-        role_arn = response['Role']['Arn']
+        role_arn = response["Role"]["Arn"]
 
         response = iam.attach_role_policy(
             RoleName=role_name,
-            PolicyArn='arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole'
+            PolicyArn="arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole",
         )
 
         response = iam.attach_role_policy(
-            PolicyArn='arn:aws:iam::aws:policy/AmazonSageMakerFullAccess',
-            RoleName=role_name
+            PolicyArn="arn:aws:iam::aws:policy/AmazonSageMakerFullAccess", RoleName=role_name
         )
 
         return role_arn
 
     except iam.exceptions.EntityAlreadyExistsException:
-        print(f'Using ARN from existing role: {role_name}')
+        print(f"Using ARN from existing role: {role_name}")
         response = iam.get_role(RoleName=role_name)
-        return response['Role']['Arn']
\ No newline at end of file
+        return response["Role"]["Arn"]
diff --git a/sagemaker-pipelines/tabular/lambda-step/sagemaker-pipelines-lambda-step.ipynb b/sagemaker-pipelines/tabular/lambda-step/sagemaker-pipelines-lambda-step.ipynb
index 9466e321c3..695995a096 100644
--- a/sagemaker-pipelines/tabular/lambda-step/sagemaker-pipelines-lambda-step.ipynb
+++ b/sagemaker-pipelines/tabular/lambda-step/sagemaker-pipelines-lambda-step.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# SageMaker Pipelines Lambda Step\n",
     "\n",
-    "This notebook illustrates how a Lambda function can be run as a step in a SageMaker Pipeline. \n",
+    "This notebook illustrates how a Lambda function can be run as a step in a SageMaker Pipeline.\n",
     "\n",
     "The steps in this pipeline include:\n",
     "* Preprocess the Abalone dataset\n",
@@ -94,13 +94,17 @@
     "        }\n",
     "    ]\n",
     "}\n",
-    "    \n",
+    "\n",
     "```"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "Let's start by importing necessary packages and installing the SageMaker Python SDK."
    ]
@@ -108,7 +112,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "import os\n",
@@ -142,10 +150,11 @@
     "    LambdaOutput,\n",
     "    LambdaOutputTypeEnum,\n",
     ")\n",
-    "from sagemaker.workflow.step_collections import CreateModelStep\n",
+    "from sagemaker.workflow.model_step import ModelStep\n",
     "from sagemaker.workflow.conditions import ConditionLessThanOrEqualTo\n",
     "from sagemaker.workflow.condition_step import ConditionStep\n",
     "from sagemaker.workflow.functions import JsonGet\n",
+    "from sagemaker.workflow.pipeline_context import PipelineSession\n",
     "\n",
     "from sagemaker.lambda_helper import Lambda\n",
     "import sys"
@@ -154,10 +163,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
-    "!{sys.executable} -m pip install \"sagemaker==2.91.1\""
+    "!{sys.executable} -m pip install \"sagemaker>=2.99.0\""
    ]
   },
   {
@@ -177,12 +190,17 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "# Create the SageMaker Session\n",
     "\n",
     "sagemaker_session = sagemaker.Session()\n",
+    "pipeline_session = PipelineSession()\n",
     "sm_client = sagemaker_session.sagemaker_client\n",
     "region = sagemaker_session.boto_region_name\n",
     "prefix = \"lambda-step-pipeline\"\n",
@@ -193,7 +211,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "# Define variables and parameters needed for the Pipeline steps\n",
@@ -226,7 +248,7 @@
    "source": [
     "## Data Preparation\n",
     "\n",
-    "An SKLearn processor is used to prepare the dataset for the Hyperparameter Tuning job. Using the script `preprocess.py`, the dataset is featurized and split into train, test, and validation datasets. \n",
+    "An SKLearn processor is used to prepare the dataset for the Hyperparameter Tuning job. Using the script `preprocess.py`, the dataset is featurized and split into train, test, and validation datasets.\n",
     "\n",
     "The output of this step is used as the input to the TrainingStep."
    ]
@@ -234,10 +256,27 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "!mkdir -p code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
-    "%%writefile preprocess.py\n",
+    "%%writefile code/preprocess.py\n",
     "\n",
     "\"\"\"Feature engineers the abalone dataset.\"\"\"\n",
     "import argparse\n",
@@ -367,7 +406,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "# Process the training data step using a python script.\n",
@@ -378,19 +421,23 @@
     "    instance_type=\"ml.m5.xlarge\",\n",
     "    instance_count=processing_instance_count,\n",
     "    base_job_name=f\"{base_job_prefix}/sklearn-abalone-preprocess\",\n",
-    "    sagemaker_session=sagemaker_session,\n",
+    "    sagemaker_session=pipeline_session,\n",
     "    role=role,\n",
     ")\n",
-    "step_process = ProcessingStep(\n",
-    "    name=\"PreprocessAbaloneData\",\n",
-    "    processor=sklearn_processor,\n",
+    "\n",
+    "processor_args = sklearn_processor.run(\n",
     "    outputs=[\n",
     "        ProcessingOutput(output_name=\"train\", source=\"/opt/ml/processing/train\"),\n",
     "        ProcessingOutput(output_name=\"validation\", source=\"/opt/ml/processing/validation\"),\n",
     "        ProcessingOutput(output_name=\"test\", source=\"/opt/ml/processing/test\"),\n",
     "    ],\n",
-    "    code=\"preprocess.py\",\n",
-    "    job_arguments=[\"--input-data\", input_data],\n",
+    "    code=\"code/preprocess.py\",\n",
+    "    arguments=[\"--input-data\", input_data],\n",
+    ")\n",
+    "\n",
+    "step_process = ProcessingStep(\n",
+    "    name=\"PreprocessAbaloneData\",\n",
+    "    step_args=processor_args,\n",
     "    cache_config=cache_config,\n",
     ")"
    ]
@@ -414,7 +461,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "# Define the output path for the model artifacts from the Hyperparameter Tuning Job\n",
@@ -434,7 +485,7 @@
     "    instance_count=1,\n",
     "    output_path=model_path,\n",
     "    base_job_name=f\"{prefix}/{base_job_prefix}/sklearn-abalone-preprocess\",\n",
-    "    sagemaker_session=sagemaker_session,\n",
+    "    sagemaker_session=pipeline_session,\n",
     "    role=role,\n",
     ")\n",
     "\n",
@@ -449,9 +500,7 @@
     "    silent=0,\n",
     ")\n",
     "\n",
-    "step_train = TrainingStep(\n",
-    "    name=\"TrainAbaloneModel\",\n",
-    "    estimator=xgb_train,\n",
+    "train_args = xgb_train.fit(\n",
     "    inputs={\n",
     "        \"train\": TrainingInput(\n",
     "            s3_data=step_process.properties.ProcessingOutputConfig.Outputs[\"train\"].S3Output.S3Uri,\n",
@@ -464,6 +513,11 @@
     "            content_type=\"text/csv\",\n",
     "        ),\n",
     "    },\n",
+    ")\n",
+    "\n",
+    "step_train = TrainingStep(\n",
+    "    name=\"TrainAbaloneModel\",\n",
+    "    step_args=train_args,\n",
     "    cache_config=cache_config,\n",
     ")"
    ]
@@ -474,16 +528,20 @@
    "source": [
     "#### Evaluating the model\n",
     "\n",
-    "Use a processing job to evaluate the model from the TrainingStep. If the output of the evaluation is True, a model is created and a Lambda function is invoked to deploy the model to a SageMaker Endpoint. "
+    "Use a processing job to evaluate the model from the TrainingStep. If the output of the evaluation is True, a model is created and a Lambda function is invoked to deploy the model to a SageMaker Endpoint."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
-    "%%writefile evaluate.py\n",
+    "%%writefile code/evaluate.py\n",
     "\n",
     "\"\"\"Evaluation script for measuring mean squared error.\"\"\"\n",
     "import json\n",
@@ -545,7 +603,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "# A ProcessingStep is used to evaluate the performance of the trained model.\n",
@@ -557,7 +619,7 @@
     "    instance_type=\"ml.m5.xlarge\",\n",
     "    instance_count=1,\n",
     "    base_job_name=f\"{prefix}/{base_job_prefix}/sklearn-abalone-preprocess\",\n",
-    "    sagemaker_session=sagemaker_session,\n",
+    "    sagemaker_session=pipeline_session,\n",
     "    role=role,\n",
     ")\n",
     "\n",
@@ -567,9 +629,7 @@
     "    path=\"evaluation.json\",\n",
     ")\n",
     "\n",
-    "step_eval = ProcessingStep(\n",
-    "    name=\"EvaluateAbaloneModel\",\n",
-    "    processor=script_eval,\n",
+    "eval_args = script_eval.run(\n",
     "    inputs=[\n",
     "        ProcessingInput(\n",
     "            source=step_train.properties.ModelArtifacts.S3ModelArtifacts,\n",
@@ -587,7 +647,11 @@
     "            destination=f\"s3://{default_bucket}/{s3_prefix}/evaluation_report\",\n",
     "        ),\n",
     "    ],\n",
-    "    code=\"evaluate.py\",\n",
+    "    code=\"code/evaluate.py\",\n",
+    ")\n",
+    "step_eval = ProcessingStep(\n",
+    "    name=\"EvaluateAbaloneModel\",\n",
+    "    step_args=eval_args,\n",
     "    property_files=[evaluation_report],\n",
     "    cache_config=cache_config,\n",
     ")"
@@ -605,22 +669,25 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "# Create Model\n",
     "model = Model(\n",
     "    image_uri=image_uri,\n",
     "    model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,\n",
-    "    sagemaker_session=sagemaker_session,\n",
+    "    sagemaker_session=pipeline_session,\n",
     "    role=role,\n",
     "    predictor_cls=XGBoostPredictor,\n",
     ")\n",
     "\n",
-    "step_create_model = CreateModelStep(\n",
+    "step_create_model = ModelStep(\n",
     "    name=\"CreateModel\",\n",
-    "    model=model,\n",
-    "    inputs=sagemaker.inputs.CreateModelInput(instance_type=\"ml.m4.large\"),\n",
+    "    step_args=model.create(\"ml.m4.large\"),\n",
     ")"
    ]
   },
@@ -630,11 +697,11 @@
    "source": [
     "## Setting up Lambda\n",
     "\n",
-    "When defining the LambdaStep, the SageMaker Lambda helper class provides helper functions for creating the Lambda function. Users can either use the `lambda_func` argument to provide the function ARN to an already deployed Lambda function OR use the `Lambda` class to create a Lambda function by providing a script, function name and role for the Lambda function. \n",
+    "When defining the LambdaStep, the SageMaker Lambda helper class provides helper functions for creating the Lambda function. Users can either use the `lambda_func` argument to provide the function ARN to an already deployed Lambda function OR use the `Lambda` class to create a Lambda function by providing a script, function name and role for the Lambda function.\n",
     "\n",
     "When passing inputs to the Lambda, the `inputs` argument can be used and within the Lambda function's handler, the `event` argument can be used to retrieve the inputs.\n",
     "\n",
-    "The dictionary response from the Lambda function is parsed through the `LambdaOutput` objects provided to the `outputs` argument. The `output_name` in `LambdaOutput` corresponds to the dictionary key in the Lambda's return dictionary. "
+    "The dictionary response from the Lambda function is parsed through the `LambdaOutput` objects provided to the `outputs` argument. The `output_name` in `LambdaOutput` corresponds to the dictionary key in the Lambda's return dictionary."
    ]
   },
   {
@@ -643,19 +710,23 @@
    "source": [
     "#### Define the Lambda function\n",
     "\n",
-    "Users can choose the leverage the Lambda helper class to create a Lambda function and provide that function object to the LambdaStep. Alternatively, users can use a pre-deployed Lambda function and provide the function ARN to the `Lambda` helper class in the Lambda step. "
+    "Users can choose the leverage the Lambda helper class to create a Lambda function and provide that function object to the LambdaStep. Alternatively, users can use a pre-deployed Lambda function and provide the function ARN to the `Lambda` helper class in the Lambda step."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
-    "%%writefile lambda_helper.py\n",
+    "%%writefile code/lambda_helper.py\n",
     "\n",
     "\"\"\"\n",
-    "This Lambda function creates an Endpoint Configuration and deploys a model to an Endpoint. \n",
+    "This Lambda function creates an Endpoint Configuration and deploys a model to an Endpoint.\n",
     "The name of the model to deploy is provided via the `event` argument\n",
     "\"\"\"\n",
     "\n",
@@ -703,9 +774,9 @@
    "source": [
     "#### Setting up the custom IAM Role\n",
     "\n",
-    "The Lambda function needs an IAM role that allows it to deploy a SageMaker Endpoint. The role ARN must be provided in the LambdaStep. \n",
+    "The Lambda function needs an IAM role that allows it to deploy a SageMaker Endpoint. The role ARN must be provided in the LambdaStep.\n",
     "\n",
-    "The Lambda role should at minimum have policies to allow `sagemaker:CreateModel`, `sagemaker:CreateEndpointConfig`, `sagemaker:CreateEndpoint` in addition to the based Lambda execution policies. \n",
+    "The Lambda role should at minimum have policies to allow `sagemaker:CreateModel`, `sagemaker:CreateEndpointConfig`, `sagemaker:CreateEndpoint` in addition to the based Lambda execution policies.\n",
     "\n",
     "A helper function in `iam_helper.py` is available to create the Lambda function role. Please note that the role uses the Amazon managed policy - `SageMakerFullAccess`. This should be replaced with an IAM policy with least privileges as per AWS IAM best practices."
    ]
@@ -713,7 +784,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "from iam_helper import create_lambda_role\n",
@@ -724,7 +799,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "# Custom Lambda Step\n",
@@ -740,7 +819,7 @@
     "func = Lambda(\n",
     "    function_name=function_name,\n",
     "    execution_role_arn=lambda_role,\n",
-    "    script=\"lambda_helper.py\",\n",
+    "    script=\"code/lambda_helper.py\",\n",
     "    handler=\"lambda_helper.lambda_handler\",\n",
     ")\n",
     "\n",
@@ -763,7 +842,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "# ConditionStep for evaluating model quality and branching execution.\n",
@@ -789,7 +872,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "# Use the same pipeline name across executions for cache usage.\n",
@@ -805,7 +892,7 @@
     "        model_approval_status,\n",
     "    ],\n",
     "    steps=[step_process, step_train, step_eval, step_cond],\n",
-    "    sagemaker_session=sagemaker_session,\n",
+    "    sagemaker_session=pipeline_session,\n",
     ")"
    ]
   },
@@ -819,7 +906,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "import json\n",
@@ -831,7 +922,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "pipeline.upsert(role_arn=role)"
@@ -840,7 +935,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "execution = pipeline.start()"
@@ -849,7 +948,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "execution.wait()"
@@ -858,7 +961,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "# Create a SageMaker client\n",
@@ -886,7 +993,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "# Get the model name from the EndpointCofig. The CreateModelStep properties are not available\n",
@@ -916,9 +1027,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (Data Science)",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east-2:429704687514:image/datascience-1.0"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -930,7 +1041,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.10"
+   "version": "3.6.14"
   },
   "metadata": {
    "interpreter": {
@@ -940,4 +1051,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/sagemaker-pipelines/tabular/lambda-step/sagemaker-pipelines-lambda-step_outputs.ipynb b/sagemaker-pipelines/tabular/lambda-step/sagemaker-pipelines-lambda-step_outputs.ipynb
index d816d2f766..fa431f8a44 100644
--- a/sagemaker-pipelines/tabular/lambda-step/sagemaker-pipelines-lambda-step_outputs.ipynb
+++ b/sagemaker-pipelines/tabular/lambda-step/sagemaker-pipelines-lambda-step_outputs.ipynb
@@ -1,14 +1,42 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0425e1c2",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-07-13T15:58:52.158481Z",
+     "iopub.status.busy": "2022-07-13T15:58:52.157838Z",
+     "iopub.status.idle": "2022-07-13T15:58:52.159725Z",
+     "shell.execute_reply": "2022-07-13T15:58:52.160093Z"
+    },
+    "papermill": {
+     "duration": 0.021573,
+     "end_time": "2022-07-13T15:58:52.160240",
+     "exception": false,
+     "start_time": "2022-07-13T15:58:52.138667",
+     "status": "completed"
+    },
+    "tags": [
+     "injected-parameters"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Parameters\n",
+    "kms_key = \"arn:aws:kms:us-west-2:000000000000:1234abcd-12ab-34cd-56ef-1234567890ab\""
+   ]
+  },
   {
    "cell_type": "markdown",
-   "id": "6d1e3a0f",
+   "id": "66b60a2b",
    "metadata": {
     "papermill": {
-     "duration": 0.015373,
-     "end_time": "2022-04-18T00:17:45.715541",
+     "duration": 0.015296,
+     "end_time": "2022-07-13T15:58:52.191291",
      "exception": false,
-     "start_time": "2022-04-18T00:17:45.700168",
+     "start_time": "2022-07-13T15:58:52.175995",
      "status": "completed"
     },
     "tags": []
@@ -16,7 +44,7 @@
    "source": [
     "# SageMaker Pipelines Lambda Step\n",
     "\n",
-    "This notebook illustrates how a Lambda function can be run as a step in a SageMaker Pipeline. \n",
+    "This notebook illustrates how a Lambda function can be run as a step in a SageMaker Pipeline.\n",
     "\n",
     "The steps in this pipeline include:\n",
     "* Preprocess the Abalone dataset\n",
@@ -30,13 +58,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9789db0e",
+   "id": "3c741e28",
    "metadata": {
     "papermill": {
-     "duration": 0.015114,
-     "end_time": "2022-04-18T00:17:45.745929",
+     "duration": 0.015223,
+     "end_time": "2022-07-13T15:58:52.221806",
      "exception": false,
-     "start_time": "2022-04-18T00:17:45.730815",
+     "start_time": "2022-07-13T15:58:52.206583",
      "status": "completed"
     },
     "tags": []
@@ -59,13 +87,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "29ba2743",
+   "id": "c5a68c81",
    "metadata": {
     "papermill": {
-     "duration": 0.015126,
-     "end_time": "2022-04-18T00:17:45.776211",
+     "duration": 0.015125,
+     "end_time": "2022-07-13T15:58:52.252140",
      "exception": false,
-     "start_time": "2022-04-18T00:17:45.761085",
+     "start_time": "2022-07-13T15:58:52.237015",
      "status": "completed"
     },
     "tags": []
@@ -124,21 +152,24 @@
     "        }\n",
     "    ]\n",
     "}\n",
-    "    \n",
+    "\n",
     "```"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c5b8a07e",
+   "id": "09611418",
    "metadata": {
     "papermill": {
-     "duration": 0.01508,
-     "end_time": "2022-04-18T00:17:45.806414",
+     "duration": 0.015163,
+     "end_time": "2022-07-13T15:58:52.282544",
      "exception": false,
-     "start_time": "2022-04-18T00:17:45.791334",
+     "start_time": "2022-07-13T15:58:52.267381",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%% md\n"
+    },
     "tags": []
    },
    "source": [
@@ -148,21 +179,24 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "9e80d1ce",
+   "id": "b7a871ec",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:17:45.843391Z",
-     "iopub.status.busy": "2022-04-18T00:17:45.842642Z",
-     "iopub.status.idle": "2022-04-18T00:17:46.319287Z",
-     "shell.execute_reply": "2022-04-18T00:17:46.318882Z"
+     "iopub.execute_input": "2022-07-13T15:58:52.322697Z",
+     "iopub.status.busy": "2022-07-13T15:58:52.321906Z",
+     "iopub.status.idle": "2022-07-13T15:58:52.827711Z",
+     "shell.execute_reply": "2022-07-13T15:58:52.827193Z"
     },
     "papermill": {
-     "duration": 0.497958,
-     "end_time": "2022-04-18T00:17:46.319400",
+     "duration": 0.52726,
+     "end_time": "2022-07-13T15:58:52.827823",
      "exception": false,
-     "start_time": "2022-04-18T00:17:45.821442",
+     "start_time": "2022-07-13T15:58:52.300563",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [],
@@ -198,10 +232,11 @@
     "    LambdaOutput,\n",
     "    LambdaOutputTypeEnum,\n",
     ")\n",
-    "from sagemaker.workflow.step_collections import CreateModelStep\n",
+    "from sagemaker.workflow.model_step import ModelStep\n",
     "from sagemaker.workflow.conditions import ConditionLessThanOrEqualTo\n",
     "from sagemaker.workflow.condition_step import ConditionStep\n",
     "from sagemaker.workflow.functions import JsonGet\n",
+    "from sagemaker.workflow.pipeline_context import PipelineSession\n",
     "\n",
     "from sagemaker.lambda_helper import Lambda\n",
     "import sys"
@@ -210,21 +245,24 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "ac873be6",
+   "id": "481497d6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:17:46.354877Z",
-     "iopub.status.busy": "2022-04-18T00:17:46.354174Z",
-     "iopub.status.idle": "2022-04-18T00:17:48.093814Z",
-     "shell.execute_reply": "2022-04-18T00:17:48.094221Z"
+     "iopub.execute_input": "2022-07-13T15:58:52.862463Z",
+     "iopub.status.busy": "2022-07-13T15:58:52.861935Z",
+     "iopub.status.idle": "2022-07-13T15:58:54.581086Z",
+     "shell.execute_reply": "2022-07-13T15:58:54.581465Z"
     },
     "papermill": {
-     "duration": 1.759493,
-     "end_time": "2022-04-18T00:17:48.094394",
+     "duration": 1.738298,
+     "end_time": "2022-07-13T15:58:54.581614",
      "exception": false,
-     "start_time": "2022-04-18T00:17:46.334901",
+     "start_time": "2022-07-13T15:58:52.843316",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [
@@ -232,54 +270,84 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/opt/conda/lib/python3.7/site-packages/secretstorage/dhcrypto.py:16: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n",
-      "  from cryptography.utils import int_from_bytes\n",
-      "/opt/conda/lib/python3.7/site-packages/secretstorage/util.py:25: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n",
-      "  from cryptography.utils import int_from_bytes\n",
-      "Requirement already satisfied: sagemaker>=2.51.0 in /opt/conda/lib/python3.7/site-packages (2.69.1.dev0)\n",
-      "Requirement already satisfied: numpy>=1.9.0 in /opt/conda/lib/python3.7/site-packages (from sagemaker>=2.51.0) (1.21.1)\n",
-      "Requirement already satisfied: protobuf>=3.1 in /opt/conda/lib/python3.7/site-packages (from sagemaker>=2.51.0) (3.17.3)\n",
-      "Requirement already satisfied: google-pasta in /opt/conda/lib/python3.7/site-packages (from sagemaker>=2.51.0) (0.2.0)\n",
-      "Requirement already satisfied: protobuf3-to-dict>=0.1.5 in /opt/conda/lib/python3.7/site-packages (from sagemaker>=2.51.0) (0.1.5)\n",
-      "Requirement already satisfied: importlib-metadata>=1.4.0 in /opt/conda/lib/python3.7/site-packages (from sagemaker>=2.51.0) (1.5.0)\n",
-      "Requirement already satisfied: attrs in /opt/conda/lib/python3.7/site-packages (from sagemaker>=2.51.0) (19.3.0)\n",
-      "Requirement already satisfied: boto3>=1.16.32 in /opt/conda/lib/python3.7/site-packages (from sagemaker>=2.51.0) (1.20.7)\n",
-      "Requirement already satisfied: smdebug-rulesconfig==1.0.1 in /opt/conda/lib/python3.7/site-packages (from sagemaker>=2.51.0) (1.0.1)\n",
-      "Requirement already satisfied: pathos in /opt/conda/lib/python3.7/site-packages (from sagemaker>=2.51.0) (0.2.8)\n",
-      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.7/site-packages (from sagemaker>=2.51.0) (20.1)\n",
-      "Requirement already satisfied: pandas in /opt/conda/lib/python3.7/site-packages (from sagemaker>=2.51.0) (1.0.1)\n",
-      "Requirement already satisfied: botocore<1.24.0,>=1.23.7 in /opt/conda/lib/python3.7/site-packages (from boto3>=1.16.32->sagemaker>=2.51.0) (1.23.7)\n",
-      "Requirement already satisfied: s3transfer<0.6.0,>=0.5.0 in /opt/conda/lib/python3.7/site-packages (from boto3>=1.16.32->sagemaker>=2.51.0) (0.5.0)\n",
-      "Requirement already satisfied: jmespath<1.0.0,>=0.7.1 in /opt/conda/lib/python3.7/site-packages (from boto3>=1.16.32->sagemaker>=2.51.0) (0.10.0)\n",
-      "Requirement already satisfied: urllib3<1.27,>=1.25.4 in /opt/conda/lib/python3.7/site-packages (from botocore<1.24.0,>=1.23.7->boto3>=1.16.32->sagemaker>=2.51.0) (1.26.6)\n",
-      "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /opt/conda/lib/python3.7/site-packages (from botocore<1.24.0,>=1.23.7->boto3>=1.16.32->sagemaker>=2.51.0) (2.8.1)\n",
-      "Requirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata>=1.4.0->sagemaker>=2.51.0) (2.2.0)\n",
-      "Requirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from packaging>=20.0->sagemaker>=2.51.0) (1.14.0)\n",
-      "Requirement already satisfied: pyparsing>=2.0.2 in /opt/conda/lib/python3.7/site-packages (from packaging>=20.0->sagemaker>=2.51.0) (2.4.6)\n",
-      "Requirement already satisfied: pytz>=2017.2 in /opt/conda/lib/python3.7/site-packages (from pandas->sagemaker>=2.51.0) (2019.3)\n",
-      "Requirement already satisfied: pox>=0.3.0 in /opt/conda/lib/python3.7/site-packages (from pathos->sagemaker>=2.51.0) (0.3.0)\n",
-      "Requirement already satisfied: dill>=0.3.4 in /opt/conda/lib/python3.7/site-packages (from pathos->sagemaker>=2.51.0) (0.3.4)\n",
-      "Requirement already satisfied: multiprocess>=0.70.12 in /opt/conda/lib/python3.7/site-packages (from pathos->sagemaker>=2.51.0) (0.70.12.2)\n",
-      "Requirement already satisfied: ppft>=1.6.6.4 in /opt/conda/lib/python3.7/site-packages (from pathos->sagemaker>=2.51.0) (1.6.6.4)\n",
-      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n",
-      "\u001b[33mWARNING: You are using pip version 21.1.3; however, version 22.0.4 is available.\n",
-      "You should consider upgrading via the '/opt/conda/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n"
+      "/opt/conda/lib/python3.7/site-packages/secretstorage/dhcrypto.py:16: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\r\n",
+      "  from cryptography.utils import int_from_bytes\r\n",
+      "/opt/conda/lib/python3.7/site-packages/secretstorage/util.py:25: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\r\n",
+      "  from cryptography.utils import int_from_bytes\r\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: sagemaker>=2.99.0 in /opt/conda/lib/python3.7/site-packages (2.99.0)\r\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: protobuf3-to-dict<1.0,>=0.1.5 in /opt/conda/lib/python3.7/site-packages (from sagemaker>=2.99.0) (0.1.5)\r\n",
+      "Requirement already satisfied: google-pasta in /opt/conda/lib/python3.7/site-packages (from sagemaker>=2.99.0) (0.2.0)\r\n",
+      "Requirement already satisfied: smdebug-rulesconfig==1.0.1 in /opt/conda/lib/python3.7/site-packages (from sagemaker>=2.99.0) (1.0.1)\r\n",
+      "Requirement already satisfied: numpy<2.0,>=1.9.0 in /opt/conda/lib/python3.7/site-packages (from sagemaker>=2.99.0) (1.21.1)\r\n",
+      "Requirement already satisfied: boto3<2.0,>=1.20.21 in /opt/conda/lib/python3.7/site-packages (from sagemaker>=2.99.0) (1.20.47)\r\n",
+      "Requirement already satisfied: attrs<22,>=20.3.0 in /opt/conda/lib/python3.7/site-packages (from sagemaker>=2.99.0) (21.4.0)\r\n",
+      "Requirement already satisfied: pandas in /opt/conda/lib/python3.7/site-packages (from sagemaker>=2.99.0) (1.0.1)\r\n",
+      "Requirement already satisfied: importlib-metadata<5.0,>=1.4.0 in /opt/conda/lib/python3.7/site-packages (from sagemaker>=2.99.0) (1.5.0)\r\n",
+      "Requirement already satisfied: protobuf<4.0,>=3.1 in /opt/conda/lib/python3.7/site-packages (from sagemaker>=2.99.0) (3.17.3)\r\n",
+      "Requirement already satisfied: pathos in /opt/conda/lib/python3.7/site-packages (from sagemaker>=2.99.0) (0.2.8)\r\n",
+      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.7/site-packages (from sagemaker>=2.99.0) (20.1)\r\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: botocore<1.24.0,>=1.23.47 in /opt/conda/lib/python3.7/site-packages (from boto3<2.0,>=1.20.21->sagemaker>=2.99.0) (1.23.47)\r\n",
+      "Requirement already satisfied: s3transfer<0.6.0,>=0.5.0 in /opt/conda/lib/python3.7/site-packages (from boto3<2.0,>=1.20.21->sagemaker>=2.99.0) (0.5.0)\r\n",
+      "Requirement already satisfied: jmespath<1.0.0,>=0.7.1 in /opt/conda/lib/python3.7/site-packages (from boto3<2.0,>=1.20.21->sagemaker>=2.99.0) (0.10.0)\r\n",
+      "Requirement already satisfied: urllib3<1.27,>=1.25.4 in /opt/conda/lib/python3.7/site-packages (from botocore<1.24.0,>=1.23.47->boto3<2.0,>=1.20.21->sagemaker>=2.99.0) (1.26.6)\r\n",
+      "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /opt/conda/lib/python3.7/site-packages (from botocore<1.24.0,>=1.23.47->boto3<2.0,>=1.20.21->sagemaker>=2.99.0) (2.8.1)\r\n",
+      "Requirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata<5.0,>=1.4.0->sagemaker>=2.99.0) (2.2.0)\r\n",
+      "Requirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from packaging>=20.0->sagemaker>=2.99.0) (1.14.0)\r\n",
+      "Requirement already satisfied: pyparsing>=2.0.2 in /opt/conda/lib/python3.7/site-packages (from packaging>=20.0->sagemaker>=2.99.0) (2.4.6)\r\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: pytz>=2017.2 in /opt/conda/lib/python3.7/site-packages (from pandas->sagemaker>=2.99.0) (2019.3)\r\n",
+      "Requirement already satisfied: ppft>=1.6.6.4 in /opt/conda/lib/python3.7/site-packages (from pathos->sagemaker>=2.99.0) (1.6.6.4)\r\n",
+      "Requirement already satisfied: pox>=0.3.0 in /opt/conda/lib/python3.7/site-packages (from pathos->sagemaker>=2.99.0) (0.3.0)\r\n",
+      "Requirement already satisfied: multiprocess>=0.70.12 in /opt/conda/lib/python3.7/site-packages (from pathos->sagemaker>=2.99.0) (0.70.12.2)\r\n",
+      "Requirement already satisfied: dill>=0.3.4 in /opt/conda/lib/python3.7/site-packages (from pathos->sagemaker>=2.99.0) (0.3.4)\r\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n",
+      "\u001b[33mWARNING: You are using pip version 21.1.3; however, version 22.1.2 is available.\r\n",
+      "You should consider upgrading via the '/opt/conda/bin/python -m pip install --upgrade pip' command.\u001b[0m\r\n"
      ]
     }
    ],
    "source": [
-    "!{sys.executable} -m pip install \"sagemaker>=2.51.0\""
+    "!{sys.executable} -m pip install \"sagemaker>=2.99.0\""
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "322f8d23",
+   "id": "dda7b3e5",
    "metadata": {
     "papermill": {
-     "duration": 0.016932,
-     "end_time": "2022-04-18T00:17:48.128714",
+     "duration": 0.017215,
+     "end_time": "2022-07-13T15:58:54.616067",
      "exception": false,
-     "start_time": "2022-04-18T00:17:48.111782",
+     "start_time": "2022-07-13T15:58:54.598852",
      "status": "completed"
     },
     "tags": []
@@ -290,13 +358,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "deffe595",
+   "id": "2ce0242d",
    "metadata": {
     "papermill": {
-     "duration": 0.016766,
-     "end_time": "2022-04-18T00:17:48.162442",
+     "duration": 0.016806,
+     "end_time": "2022-07-13T15:58:54.649817",
      "exception": false,
-     "start_time": "2022-04-18T00:17:48.145676",
+     "start_time": "2022-07-13T15:58:54.633011",
      "status": "completed"
     },
     "tags": []
@@ -308,21 +376,24 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "a8115972",
+   "id": "a55e1178",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:17:48.209712Z",
-     "iopub.status.busy": "2022-04-18T00:17:48.209176Z",
-     "iopub.status.idle": "2022-04-18T00:17:48.361714Z",
-     "shell.execute_reply": "2022-04-18T00:17:48.361286Z"
+     "iopub.execute_input": "2022-07-13T15:58:54.696396Z",
+     "iopub.status.busy": "2022-07-13T15:58:54.695661Z",
+     "iopub.status.idle": "2022-07-13T15:58:54.900760Z",
+     "shell.execute_reply": "2022-07-13T15:58:54.900289Z"
     },
     "papermill": {
-     "duration": 0.182419,
-     "end_time": "2022-04-18T00:17:48.361819",
+     "duration": 0.234346,
+     "end_time": "2022-07-13T15:58:54.900874",
      "exception": false,
-     "start_time": "2022-04-18T00:17:48.179400",
+     "start_time": "2022-07-13T15:58:54.666528",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [],
@@ -330,6 +401,7 @@
     "# Create the SageMaker Session\n",
     "\n",
     "sagemaker_session = sagemaker.Session()\n",
+    "pipeline_session = PipelineSession()\n",
     "sm_client = sagemaker_session.sagemaker_client\n",
     "region = sagemaker_session.boto_region_name\n",
     "prefix = \"lambda-step-pipeline\"\n",
@@ -340,21 +412,24 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "0ff78481",
+   "id": "1832cf2e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:17:48.410227Z",
-     "iopub.status.busy": "2022-04-18T00:17:48.409395Z",
-     "iopub.status.idle": "2022-04-18T00:17:49.042133Z",
-     "shell.execute_reply": "2022-04-18T00:17:49.041700Z"
+     "iopub.execute_input": "2022-07-13T15:58:54.947742Z",
+     "iopub.status.busy": "2022-07-13T15:58:54.947270Z",
+     "iopub.status.idle": "2022-07-13T15:58:55.684395Z",
+     "shell.execute_reply": "2022-07-13T15:58:55.683908Z"
     },
     "papermill": {
-     "duration": 0.663019,
-     "end_time": "2022-04-18T00:17:49.042285",
+     "duration": 0.766426,
+     "end_time": "2022-07-13T15:58:55.684513",
      "exception": false,
-     "start_time": "2022-04-18T00:17:48.379266",
+     "start_time": "2022-07-13T15:58:54.918087",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [],
@@ -367,9 +442,6 @@
     "s3_prefix = \"lambda-step-pipeline\"\n",
     "\n",
     "processing_instance_count = ParameterInteger(name=\"ProcessingInstanceCount\", default_value=1)\n",
-    "processing_instance_type = ParameterString(\n",
-    "    name=\"ProcessingInstanceType\", default_value=\"ml.m5.xlarge\"\n",
-    ")\n",
     "training_instance_type = ParameterString(name=\"TrainingInstanceType\", default_value=\"ml.m5.xlarge\")\n",
     "model_approval_status = ParameterString(\n",
     "    name=\"ModelApprovalStatus\", default_value=\"PendingManualApproval\"\n",
@@ -388,13 +460,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5f243888",
+   "id": "ba7cd3ba",
    "metadata": {
     "papermill": {
-     "duration": 0.017327,
-     "end_time": "2022-04-18T00:17:49.077018",
+     "duration": 0.016885,
+     "end_time": "2022-07-13T15:58:55.718428",
      "exception": false,
-     "start_time": "2022-04-18T00:17:49.059691",
+     "start_time": "2022-07-13T15:58:55.701543",
      "status": "completed"
     },
     "tags": []
@@ -402,7 +474,7 @@
    "source": [
     "## Data Preparation\n",
     "\n",
-    "An SKLearn processor is used to prepare the dataset for the Hyperparameter Tuning job. Using the script `preprocess.py`, the dataset is featurized and split into train, test, and validation datasets. \n",
+    "An SKLearn processor is used to prepare the dataset for the Hyperparameter Tuning job. Using the script `preprocess.py`, the dataset is featurized and split into train, test, and validation datasets.\n",
     "\n",
     "The output of this step is used as the input to the TrainingStep."
    ]
@@ -410,21 +482,52 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "0cb15d01",
+   "id": "6ef53ec4",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-07-13T15:58:55.759267Z",
+     "iopub.status.busy": "2022-07-13T15:58:55.755957Z",
+     "iopub.status.idle": "2022-07-13T15:58:55.907503Z",
+     "shell.execute_reply": "2022-07-13T15:58:55.907053Z"
+    },
+    "papermill": {
+     "duration": 0.172373,
+     "end_time": "2022-07-13T15:58:55.907627",
+     "exception": false,
+     "start_time": "2022-07-13T15:58:55.735254",
+     "status": "completed"
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!mkdir -p code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ea68f51c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:17:49.116655Z",
-     "iopub.status.busy": "2022-04-18T00:17:49.116031Z",
-     "iopub.status.idle": "2022-04-18T00:17:49.118696Z",
-     "shell.execute_reply": "2022-04-18T00:17:49.119062Z"
+     "iopub.execute_input": "2022-07-13T15:58:55.948608Z",
+     "iopub.status.busy": "2022-07-13T15:58:55.947461Z",
+     "iopub.status.idle": "2022-07-13T15:58:55.951620Z",
+     "shell.execute_reply": "2022-07-13T15:58:55.951193Z"
     },
     "papermill": {
-     "duration": 0.024982,
-     "end_time": "2022-04-18T00:17:49.119206",
+     "duration": 0.026484,
+     "end_time": "2022-07-13T15:58:55.951738",
      "exception": false,
-     "start_time": "2022-04-18T00:17:49.094224",
+     "start_time": "2022-07-13T15:58:55.925254",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [
@@ -432,12 +535,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Writing preprocess.py\n"
+      "Writing code/preprocess.py\n"
      ]
     }
    ],
    "source": [
-    "%%writefile preprocess.py\n",
+    "%%writefile code/preprocess.py\n",
     "\n",
     "\"\"\"Feature engineers the abalone dataset.\"\"\"\n",
     "import argparse\n",
@@ -566,60 +669,86 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "cd0f71fb",
+   "execution_count": 8,
+   "id": "5cc7e807",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:17:49.163730Z",
-     "iopub.status.busy": "2022-04-18T00:17:49.162913Z",
-     "iopub.status.idle": "2022-04-18T00:17:49.179630Z",
-     "shell.execute_reply": "2022-04-18T00:17:49.179988Z"
+     "iopub.execute_input": "2022-07-13T15:58:55.993872Z",
+     "iopub.status.busy": "2022-07-13T15:58:55.992990Z",
+     "iopub.status.idle": "2022-07-13T15:58:56.276627Z",
+     "shell.execute_reply": "2022-07-13T15:58:56.277057Z"
     },
     "papermill": {
-     "duration": 0.043225,
-     "end_time": "2022-04-18T00:17:49.180127",
+     "duration": 0.307809,
+     "end_time": "2022-07-13T15:58:56.277208",
      "exception": false,
-     "start_time": "2022-04-18T00:17:49.136902",
+     "start_time": "2022-07-13T15:58:55.969399",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.7/site-packages/sagemaker/workflow/pipeline_context.py:197: UserWarning: Running within a PipelineSession, there will be No Wait, No Logs, and No Job being started.\n",
+      "  UserWarning,\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Job Name:  lambda-step-example/sklearn-abalone-pre-2022-07-13-15-58-56-005\n",
+      "Inputs:  [{'InputName': 'code', 'AppManaged': False, 'S3Input': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/lambda-step-example/sklearn-abalone-pre-2022-07-13-15-58-56-005/input/code/preprocess.py', 'LocalPath': '/opt/ml/processing/input/code', 'S3DataType': 'S3Prefix', 'S3InputMode': 'File', 'S3DataDistributionType': 'FullyReplicated', 'S3CompressionType': 'None'}}]\n",
+      "Outputs:  [{'OutputName': 'train', 'AppManaged': False, 'S3Output': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/lambda-step-example/sklearn-abalone-pre-2022-07-13-15-58-56-005/output/train', 'LocalPath': '/opt/ml/processing/train', 'S3UploadMode': 'EndOfJob'}}, {'OutputName': 'validation', 'AppManaged': False, 'S3Output': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/lambda-step-example/sklearn-abalone-pre-2022-07-13-15-58-56-005/output/validation', 'LocalPath': '/opt/ml/processing/validation', 'S3UploadMode': 'EndOfJob'}}, {'OutputName': 'test', 'AppManaged': False, 'S3Output': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/lambda-step-example/sklearn-abalone-pre-2022-07-13-15-58-56-005/output/test', 'LocalPath': '/opt/ml/processing/test', 'S3UploadMode': 'EndOfJob'}}]\n"
+     ]
+    }
+   ],
    "source": [
     "# Process the training data step using a python script.\n",
     "# Split the training data set into train, test, and validation datasets\n",
     "\n",
     "sklearn_processor = SKLearnProcessor(\n",
-    "    framework_version=\"1.0-1\",\n",
-    "    instance_type=processing_instance_type,\n",
+    "    framework_version=\"0.23-1\",\n",
+    "    instance_type=\"ml.m5.xlarge\",\n",
     "    instance_count=processing_instance_count,\n",
     "    base_job_name=f\"{base_job_prefix}/sklearn-abalone-preprocess\",\n",
-    "    sagemaker_session=sagemaker_session,\n",
+    "    sagemaker_session=pipeline_session,\n",
     "    role=role,\n",
     ")\n",
-    "step_process = ProcessingStep(\n",
-    "    name=\"PreprocessAbaloneData\",\n",
-    "    processor=sklearn_processor,\n",
+    "\n",
+    "processor_args = sklearn_processor.run(\n",
     "    outputs=[\n",
     "        ProcessingOutput(output_name=\"train\", source=\"/opt/ml/processing/train\"),\n",
     "        ProcessingOutput(output_name=\"validation\", source=\"/opt/ml/processing/validation\"),\n",
     "        ProcessingOutput(output_name=\"test\", source=\"/opt/ml/processing/test\"),\n",
     "    ],\n",
-    "    code=\"preprocess.py\",\n",
-    "    job_arguments=[\"--input-data\", input_data],\n",
+    "    code=\"code/preprocess.py\",\n",
+    "    arguments=[\"--input-data\", input_data],\n",
+    ")\n",
+    "\n",
+    "step_process = ProcessingStep(\n",
+    "    name=\"PreprocessAbaloneData\",\n",
+    "    step_args=processor_args,\n",
     "    cache_config=cache_config,\n",
     ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "01f476dc",
+   "id": "ff61dc5a",
    "metadata": {
     "papermill": {
-     "duration": 0.017547,
-     "end_time": "2022-04-18T00:17:49.215444",
+     "duration": 0.017909,
+     "end_time": "2022-07-13T15:58:56.313344",
      "exception": false,
-     "start_time": "2022-04-18T00:17:49.197897",
+     "start_time": "2022-07-13T15:58:56.295435",
      "status": "completed"
     },
     "tags": []
@@ -632,13 +761,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d949b96d",
+   "id": "bda6892e",
    "metadata": {
     "papermill": {
-     "duration": 0.017417,
-     "end_time": "2022-04-18T00:17:49.250440",
+     "duration": 0.01789,
+     "end_time": "2022-07-13T15:58:56.350320",
      "exception": false,
-     "start_time": "2022-04-18T00:17:49.233023",
+     "start_time": "2022-07-13T15:58:56.332430",
      "status": "completed"
     },
     "tags": []
@@ -649,25 +778,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "a683373c",
+   "execution_count": 9,
+   "id": "a575d6e3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:17:49.292700Z",
-     "iopub.status.busy": "2022-04-18T00:17:49.292202Z",
-     "iopub.status.idle": "2022-04-18T00:17:49.306149Z",
-     "shell.execute_reply": "2022-04-18T00:17:49.306566Z"
+     "iopub.execute_input": "2022-07-13T15:58:56.394662Z",
+     "iopub.status.busy": "2022-07-13T15:58:56.394093Z",
+     "iopub.status.idle": "2022-07-13T15:58:56.421180Z",
+     "shell.execute_reply": "2022-07-13T15:58:56.421606Z"
     },
     "papermill": {
-     "duration": 0.038713,
-     "end_time": "2022-04-18T00:17:49.306706",
+     "duration": 0.053298,
+     "end_time": "2022-07-13T15:58:56.421753",
      "exception": false,
-     "start_time": "2022-04-18T00:17:49.267993",
+     "start_time": "2022-07-13T15:58:56.368455",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.7/site-packages/sagemaker/workflow/steps.py:391: UserWarning: Profiling is enabled on the provided estimator. The default profiler rule includes a timestamp which will change each time the pipeline is upserted, causing cache misses. If profiling is not needed, set disable_profiler to True on the estimator.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    }
+   ],
    "source": [
     "# Define the output path for the model artifacts from the Hyperparameter Tuning Job\n",
     "model_path = f\"s3://{default_bucket}/{base_job_prefix}/AbaloneTrain\"\n",
@@ -677,7 +818,7 @@
     "    region=region,\n",
     "    version=\"1.0-1\",\n",
     "    py_version=\"py3\",\n",
-    "    instance_type=training_instance_type,\n",
+    "    instance_type=\"ml.m5.xlarge\",\n",
     ")\n",
     "\n",
     "xgb_train = Estimator(\n",
@@ -686,7 +827,7 @@
     "    instance_count=1,\n",
     "    output_path=model_path,\n",
     "    base_job_name=f\"{prefix}/{base_job_prefix}/sklearn-abalone-preprocess\",\n",
-    "    sagemaker_session=sagemaker_session,\n",
+    "    sagemaker_session=pipeline_session,\n",
     "    role=role,\n",
     ")\n",
     "\n",
@@ -701,9 +842,7 @@
     "    silent=0,\n",
     ")\n",
     "\n",
-    "step_train = TrainingStep(\n",
-    "    name=\"TrainAbaloneModel\",\n",
-    "    estimator=xgb_train,\n",
+    "train_args = xgb_train.fit(\n",
     "    inputs={\n",
     "        \"train\": TrainingInput(\n",
     "            s3_data=step_process.properties.ProcessingOutputConfig.Outputs[\"train\"].S3Output.S3Uri,\n",
@@ -716,19 +855,24 @@
     "            content_type=\"text/csv\",\n",
     "        ),\n",
     "    },\n",
+    ")\n",
+    "\n",
+    "step_train = TrainingStep(\n",
+    "    name=\"TrainAbaloneModel\",\n",
+    "    step_args=train_args,\n",
     "    cache_config=cache_config,\n",
     ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "28b11fc1",
+   "id": "9c40566b",
    "metadata": {
     "papermill": {
-     "duration": 0.017655,
-     "end_time": "2022-04-18T00:17:49.342172",
+     "duration": 0.018193,
+     "end_time": "2022-07-13T15:58:56.458560",
      "exception": false,
-     "start_time": "2022-04-18T00:17:49.324517",
+     "start_time": "2022-07-13T15:58:56.440367",
      "status": "completed"
     },
     "tags": []
@@ -736,27 +880,30 @@
    "source": [
     "#### Evaluating the model\n",
     "\n",
-    "Use a processing job to evaluate the model from the TrainingStep. If the output of the evaluation is True, a model is created and a Lambda function is invoked to deploy the model to a SageMaker Endpoint. "
+    "Use a processing job to evaluate the model from the TrainingStep. If the output of the evaluation is True, a model is created and a Lambda function is invoked to deploy the model to a SageMaker Endpoint."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "e2c5f52a",
+   "execution_count": 10,
+   "id": "1545156e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:17:49.381974Z",
-     "iopub.status.busy": "2022-04-18T00:17:49.381353Z",
-     "iopub.status.idle": "2022-04-18T00:17:49.384458Z",
-     "shell.execute_reply": "2022-04-18T00:17:49.384016Z"
+     "iopub.execute_input": "2022-07-13T15:58:56.499375Z",
+     "iopub.status.busy": "2022-07-13T15:58:56.498823Z",
+     "iopub.status.idle": "2022-07-13T15:58:56.501906Z",
+     "shell.execute_reply": "2022-07-13T15:58:56.501502Z"
     },
     "papermill": {
-     "duration": 0.024593,
-     "end_time": "2022-04-18T00:17:49.384562",
+     "duration": 0.02515,
+     "end_time": "2022-07-13T15:58:56.502014",
      "exception": false,
-     "start_time": "2022-04-18T00:17:49.359969",
+     "start_time": "2022-07-13T15:58:56.476864",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [
@@ -764,12 +911,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Writing evaluate.py\n"
+      "Writing code/evaluate.py\n"
      ]
     }
    ],
    "source": [
-    "%%writefile evaluate.py\n",
+    "%%writefile code/evaluate.py\n",
     "\n",
     "\"\"\"Evaluation script for measuring mean squared error.\"\"\"\n",
     "import json\n",
@@ -830,25 +977,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "5112c1e7",
+   "execution_count": 11,
+   "id": "edd37d25",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:17:49.428306Z",
-     "iopub.status.busy": "2022-04-18T00:17:49.427644Z",
-     "iopub.status.idle": "2022-04-18T00:17:49.429594Z",
-     "shell.execute_reply": "2022-04-18T00:17:49.429983Z"
+     "iopub.execute_input": "2022-07-13T15:58:56.549199Z",
+     "iopub.status.busy": "2022-07-13T15:58:56.548644Z",
+     "iopub.status.idle": "2022-07-13T15:58:56.640043Z",
+     "shell.execute_reply": "2022-07-13T15:58:56.640488Z"
     },
     "papermill": {
-     "duration": 0.027021,
-     "end_time": "2022-04-18T00:17:49.430118",
+     "duration": 0.119643,
+     "end_time": "2022-07-13T15:58:56.640642",
      "exception": false,
-     "start_time": "2022-04-18T00:17:49.403097",
+     "start_time": "2022-07-13T15:58:56.520999",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Job Name:  lambda-step-pipeline/lambda-step-exampl-2022-07-13-15-58-56-545\n",
+      "Inputs:  [{'InputName': 'input-1', 'AppManaged': False, 'S3Input': {'S3Uri': <sagemaker.workflow.properties.Properties object at 0x7f15b0ee9350>, 'LocalPath': '/opt/ml/processing/model', 'S3DataType': 'S3Prefix', 'S3InputMode': 'File', 'S3DataDistributionType': 'FullyReplicated', 'S3CompressionType': 'None'}}, {'InputName': 'input-2', 'AppManaged': False, 'S3Input': {'S3Uri': <sagemaker.workflow.properties.Properties object at 0x7f15b0f199d0>, 'LocalPath': '/opt/ml/processing/test', 'S3DataType': 'S3Prefix', 'S3InputMode': 'File', 'S3DataDistributionType': 'FullyReplicated', 'S3CompressionType': 'None'}}, {'InputName': 'code', 'AppManaged': False, 'S3Input': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/lambda-step-pipeline/lambda-step-exampl-2022-07-13-15-58-56-545/input/code/evaluate.py', 'LocalPath': '/opt/ml/processing/input/code', 'S3DataType': 'S3Prefix', 'S3InputMode': 'File', 'S3DataDistributionType': 'FullyReplicated', 'S3CompressionType': 'None'}}]\n",
+      "Outputs:  [{'OutputName': 'evaluation', 'AppManaged': False, 'S3Output': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/lambda-step-pipeline/evaluation_report', 'LocalPath': '/opt/ml/processing/evaluation', 'S3UploadMode': 'EndOfJob'}}]\n"
+     ]
+    }
+   ],
    "source": [
     "# A ProcessingStep is used to evaluate the performance of the trained model.\n",
     "# Based on the results of the evaluation, the model is created and deployed.\n",
@@ -856,10 +1017,10 @@
     "script_eval = ScriptProcessor(\n",
     "    image_uri=image_uri,\n",
     "    command=[\"python3\"],\n",
-    "    instance_type=processing_instance_type,\n",
+    "    instance_type=\"ml.m5.xlarge\",\n",
     "    instance_count=1,\n",
     "    base_job_name=f\"{prefix}/{base_job_prefix}/sklearn-abalone-preprocess\",\n",
-    "    sagemaker_session=sagemaker_session,\n",
+    "    sagemaker_session=pipeline_session,\n",
     "    role=role,\n",
     ")\n",
     "\n",
@@ -869,9 +1030,7 @@
     "    path=\"evaluation.json\",\n",
     ")\n",
     "\n",
-    "step_eval = ProcessingStep(\n",
-    "    name=\"EvaluateAbaloneModel\",\n",
-    "    processor=script_eval,\n",
+    "eval_args = script_eval.run(\n",
     "    inputs=[\n",
     "        ProcessingInput(\n",
     "            source=step_train.properties.ModelArtifacts.S3ModelArtifacts,\n",
@@ -889,7 +1048,11 @@
     "            destination=f\"s3://{default_bucket}/{s3_prefix}/evaluation_report\",\n",
     "        ),\n",
     "    ],\n",
-    "    code=\"evaluate.py\",\n",
+    "    code=\"code/evaluate.py\",\n",
+    ")\n",
+    "step_eval = ProcessingStep(\n",
+    "    name=\"EvaluateAbaloneModel\",\n",
+    "    step_args=eval_args,\n",
     "    property_files=[evaluation_report],\n",
     "    cache_config=cache_config,\n",
     ")"
@@ -897,13 +1060,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6057e873",
+   "id": "ce969364",
    "metadata": {
     "papermill": {
-     "duration": 0.018122,
-     "end_time": "2022-04-18T00:17:49.466653",
+     "duration": 0.018851,
+     "end_time": "2022-07-13T15:58:56.678471",
      "exception": false,
-     "start_time": "2022-04-18T00:17:49.448531",
+     "start_time": "2022-07-13T15:58:56.659620",
      "status": "completed"
     },
     "tags": []
@@ -916,22 +1079,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "f3cb5cf6",
+   "execution_count": 12,
+   "id": "8017b70a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:17:49.507558Z",
-     "iopub.status.busy": "2022-04-18T00:17:49.506946Z",
-     "iopub.status.idle": "2022-04-18T00:17:49.508786Z",
-     "shell.execute_reply": "2022-04-18T00:17:49.509197Z"
+     "iopub.execute_input": "2022-07-13T15:58:56.721922Z",
+     "iopub.status.busy": "2022-07-13T15:58:56.721196Z",
+     "iopub.status.idle": "2022-07-13T15:58:56.723177Z",
+     "shell.execute_reply": "2022-07-13T15:58:56.723545Z"
     },
     "papermill": {
-     "duration": 0.024605,
-     "end_time": "2022-04-18T00:17:49.509328",
+     "duration": 0.026308,
+     "end_time": "2022-07-13T15:58:56.723689",
      "exception": false,
-     "start_time": "2022-04-18T00:17:49.484723",
+     "start_time": "2022-07-13T15:58:56.697381",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [],
@@ -940,27 +1106,26 @@
     "model = Model(\n",
     "    image_uri=image_uri,\n",
     "    model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,\n",
-    "    sagemaker_session=sagemaker_session,\n",
+    "    sagemaker_session=pipeline_session,\n",
     "    role=role,\n",
     "    predictor_cls=XGBoostPredictor,\n",
     ")\n",
     "\n",
-    "step_create_model = CreateModelStep(\n",
+    "step_create_model = ModelStep(\n",
     "    name=\"CreateModel\",\n",
-    "    model=model,\n",
-    "    inputs=sagemaker.inputs.CreateModelInput(instance_type=\"ml.m4.large\"),\n",
+    "    step_args=model.create(\"ml.m4.large\"),\n",
     ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f97f9e65",
+   "id": "e8d94f5b",
    "metadata": {
     "papermill": {
-     "duration": 0.018099,
-     "end_time": "2022-04-18T00:17:49.545716",
+     "duration": 0.018913,
+     "end_time": "2022-07-13T15:58:56.761761",
      "exception": false,
-     "start_time": "2022-04-18T00:17:49.527617",
+     "start_time": "2022-07-13T15:58:56.742848",
      "status": "completed"
     },
     "tags": []
@@ -968,22 +1133,22 @@
    "source": [
     "## Setting up Lambda\n",
     "\n",
-    "When defining the LambdaStep, the SageMaker Lambda helper class provides helper functions for creating the Lambda function. Users can either use the `lambda_func` argument to provide the function ARN to an already deployed Lambda function OR use the `Lambda` class to create a Lambda function by providing a script, function name and role for the Lambda function. \n",
+    "When defining the LambdaStep, the SageMaker Lambda helper class provides helper functions for creating the Lambda function. Users can either use the `lambda_func` argument to provide the function ARN to an already deployed Lambda function OR use the `Lambda` class to create a Lambda function by providing a script, function name and role for the Lambda function.\n",
     "\n",
     "When passing inputs to the Lambda, the `inputs` argument can be used and within the Lambda function's handler, the `event` argument can be used to retrieve the inputs.\n",
     "\n",
-    "The dictionary response from the Lambda function is parsed through the `LambdaOutput` objects provided to the `outputs` argument. The `output_name` in `LambdaOutput` corresponds to the dictionary key in the Lambda's return dictionary. "
+    "The dictionary response from the Lambda function is parsed through the `LambdaOutput` objects provided to the `outputs` argument. The `output_name` in `LambdaOutput` corresponds to the dictionary key in the Lambda's return dictionary."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "562dd374",
+   "id": "f813825f",
    "metadata": {
     "papermill": {
-     "duration": 0.018179,
-     "end_time": "2022-04-18T00:17:49.582321",
+     "duration": 0.018947,
+     "end_time": "2022-07-13T15:58:56.799670",
      "exception": false,
-     "start_time": "2022-04-18T00:17:49.564142",
+     "start_time": "2022-07-13T15:58:56.780723",
      "status": "completed"
     },
     "tags": []
@@ -991,27 +1156,30 @@
    "source": [
     "#### Define the Lambda function\n",
     "\n",
-    "Users can choose the leverage the Lambda helper class to create a Lambda function and provide that function object to the LambdaStep. Alternatively, users can use a pre-deployed Lambda function and provide the function ARN to the `Lambda` helper class in the Lambda step. "
+    "Users can choose the leverage the Lambda helper class to create a Lambda function and provide that function object to the LambdaStep. Alternatively, users can use a pre-deployed Lambda function and provide the function ARN to the `Lambda` helper class in the Lambda step."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "21b0ff38",
+   "execution_count": 13,
+   "id": "c3782959",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:17:49.622985Z",
-     "iopub.status.busy": "2022-04-18T00:17:49.622243Z",
-     "iopub.status.idle": "2022-04-18T00:17:49.624984Z",
-     "shell.execute_reply": "2022-04-18T00:17:49.625347Z"
+     "iopub.execute_input": "2022-07-13T15:58:56.842251Z",
+     "iopub.status.busy": "2022-07-13T15:58:56.841631Z",
+     "iopub.status.idle": "2022-07-13T15:58:56.844526Z",
+     "shell.execute_reply": "2022-07-13T15:58:56.844085Z"
     },
     "papermill": {
-     "duration": 0.024985,
-     "end_time": "2022-04-18T00:17:49.625476",
+     "duration": 0.026015,
+     "end_time": "2022-07-13T15:58:56.844646",
      "exception": false,
-     "start_time": "2022-04-18T00:17:49.600491",
+     "start_time": "2022-07-13T15:58:56.818631",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [
@@ -1019,15 +1187,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Writing lambda_helper.py\n"
+      "Writing code/lambda_helper.py\n"
      ]
     }
    ],
    "source": [
-    "%%writefile lambda_helper.py\n",
+    "%%writefile code/lambda_helper.py\n",
     "\n",
     "\"\"\"\n",
-    "This Lambda function creates an Endpoint Configuration and deploys a model to an Endpoint. \n",
+    "This Lambda function creates an Endpoint Configuration and deploys a model to an Endpoint.\n",
     "The name of the model to deploy is provided via the `event` argument\n",
     "\"\"\"\n",
     "\n",
@@ -1071,13 +1239,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "37c86090",
+   "id": "7835966e",
    "metadata": {
     "papermill": {
-     "duration": 0.018627,
-     "end_time": "2022-04-18T00:17:49.662825",
+     "duration": 0.019367,
+     "end_time": "2022-07-13T15:58:56.883384",
      "exception": false,
-     "start_time": "2022-04-18T00:17:49.644198",
+     "start_time": "2022-07-13T15:58:56.864017",
      "status": "completed"
     },
     "tags": []
@@ -1085,31 +1253,34 @@
    "source": [
     "#### Setting up the custom IAM Role\n",
     "\n",
-    "The Lambda function needs an IAM role that allows it to deploy a SageMaker Endpoint. The role ARN must be provided in the LambdaStep. \n",
+    "The Lambda function needs an IAM role that allows it to deploy a SageMaker Endpoint. The role ARN must be provided in the LambdaStep.\n",
     "\n",
-    "The Lambda role should at minimum have policies to allow `sagemaker:CreateModel`, `sagemaker:CreateEndpointConfig`, `sagemaker:CreateEndpoint` in addition to the based Lambda execution policies. \n",
+    "The Lambda role should at minimum have policies to allow `sagemaker:CreateModel`, `sagemaker:CreateEndpointConfig`, `sagemaker:CreateEndpoint` in addition to the based Lambda execution policies.\n",
     "\n",
     "A helper function in `iam_helper.py` is available to create the Lambda function role. Please note that the role uses the Amazon managed policy - `SageMakerFullAccess`. This should be replaced with an IAM policy with least privileges as per AWS IAM best practices."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "7f302df2",
+   "execution_count": 14,
+   "id": "928270f8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:17:49.703722Z",
-     "iopub.status.busy": "2022-04-18T00:17:49.702924Z",
-     "iopub.status.idle": "2022-04-18T00:17:50.206467Z",
-     "shell.execute_reply": "2022-04-18T00:17:50.206847Z"
+     "iopub.execute_input": "2022-07-13T15:58:56.926249Z",
+     "iopub.status.busy": "2022-07-13T15:58:56.925421Z",
+     "iopub.status.idle": "2022-07-13T15:58:57.385000Z",
+     "shell.execute_reply": "2022-07-13T15:58:57.384557Z"
     },
     "papermill": {
-     "duration": 0.525683,
-     "end_time": "2022-04-18T00:17:50.206984",
+     "duration": 0.482377,
+     "end_time": "2022-07-13T15:58:57.385112",
      "exception": false,
-     "start_time": "2022-04-18T00:17:49.681301",
+     "start_time": "2022-07-13T15:58:56.902735",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [
@@ -1129,22 +1300,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "22bcd1f5",
+   "execution_count": 15,
+   "id": "430e04f4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:17:50.251245Z",
-     "iopub.status.busy": "2022-04-18T00:17:50.250734Z",
-     "iopub.status.idle": "2022-04-18T00:17:50.279900Z",
-     "shell.execute_reply": "2022-04-18T00:17:50.279400Z"
+     "iopub.execute_input": "2022-07-13T15:58:57.431162Z",
+     "iopub.status.busy": "2022-07-13T15:58:57.430310Z",
+     "iopub.status.idle": "2022-07-13T15:58:57.540082Z",
+     "shell.execute_reply": "2022-07-13T15:58:57.540527Z"
     },
     "papermill": {
-     "duration": 0.054153,
-     "end_time": "2022-04-18T00:17:50.280008",
+     "duration": 0.135811,
+     "end_time": "2022-07-13T15:58:57.540673",
      "exception": false,
-     "start_time": "2022-04-18T00:17:50.225855",
+     "start_time": "2022-07-13T15:58:57.404862",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [],
@@ -1162,7 +1336,7 @@
     "func = Lambda(\n",
     "    function_name=function_name,\n",
     "    execution_role_arn=lambda_role,\n",
-    "    script=\"lambda_helper.py\",\n",
+    "    script=\"code/lambda_helper.py\",\n",
     "    handler=\"lambda_helper.lambda_handler\",\n",
     ")\n",
     "\n",
@@ -1184,22 +1358,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "1f96dce6",
+   "execution_count": 16,
+   "id": "5c6c4475",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:17:50.323116Z",
-     "iopub.status.busy": "2022-04-18T00:17:50.322583Z",
-     "iopub.status.idle": "2022-04-18T00:17:50.324359Z",
-     "shell.execute_reply": "2022-04-18T00:17:50.324730Z"
+     "iopub.execute_input": "2022-07-13T15:58:57.584145Z",
+     "iopub.status.busy": "2022-07-13T15:58:57.583668Z",
+     "iopub.status.idle": "2022-07-13T15:58:57.585521Z",
+     "shell.execute_reply": "2022-07-13T15:58:57.585892Z"
     },
     "papermill": {
-     "duration": 0.025749,
-     "end_time": "2022-04-18T00:17:50.324861",
+     "duration": 0.025694,
+     "end_time": "2022-07-13T15:58:57.586019",
      "exception": false,
-     "start_time": "2022-04-18T00:17:50.299112",
+     "start_time": "2022-07-13T15:58:57.560325",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [],
@@ -1226,22 +1403,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "e59c0049",
+   "execution_count": 17,
+   "id": "36c4a4c5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:17:50.367772Z",
-     "iopub.status.busy": "2022-04-18T00:17:50.367212Z",
-     "iopub.status.idle": "2022-04-18T00:17:50.369378Z",
-     "shell.execute_reply": "2022-04-18T00:17:50.368975Z"
+     "iopub.execute_input": "2022-07-13T15:58:57.629412Z",
+     "iopub.status.busy": "2022-07-13T15:58:57.628880Z",
+     "iopub.status.idle": "2022-07-13T15:58:57.630967Z",
+     "shell.execute_reply": "2022-07-13T15:58:57.630510Z"
     },
     "papermill": {
-     "duration": 0.025431,
-     "end_time": "2022-04-18T00:17:50.369486",
+     "duration": 0.025542,
+     "end_time": "2022-07-13T15:58:57.631070",
      "exception": false,
-     "start_time": "2022-04-18T00:17:50.344055",
+     "start_time": "2022-07-13T15:58:57.605528",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [],
@@ -1253,26 +1433,25 @@
     "pipeline = Pipeline(\n",
     "    name=pipeline_name,\n",
     "    parameters=[\n",
-    "        processing_instance_type,\n",
     "        processing_instance_count,\n",
     "        training_instance_type,\n",
     "        input_data,\n",
     "        model_approval_status,\n",
     "    ],\n",
     "    steps=[step_process, step_train, step_eval, step_cond],\n",
-    "    sagemaker_session=sagemaker_session,\n",
+    "    sagemaker_session=pipeline_session,\n",
     ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b19ba0c7",
+   "id": "bc75af4c",
    "metadata": {
     "papermill": {
-     "duration": 0.019137,
-     "end_time": "2022-04-18T00:17:50.407773",
+     "duration": 0.019407,
+     "end_time": "2022-07-13T15:58:57.670110",
      "exception": false,
-     "start_time": "2022-04-18T00:17:50.388636",
+     "start_time": "2022-07-13T15:58:57.650703",
      "status": "completed"
     },
     "tags": []
@@ -1283,22 +1462,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "5ef428da",
+   "execution_count": 18,
+   "id": "0183c74f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:17:50.451723Z",
-     "iopub.status.busy": "2022-04-18T00:17:50.449084Z",
-     "iopub.status.idle": "2022-04-18T00:17:50.875491Z",
-     "shell.execute_reply": "2022-04-18T00:17:50.875857Z"
+     "iopub.execute_input": "2022-07-13T15:58:57.713276Z",
+     "iopub.status.busy": "2022-07-13T15:58:57.712514Z",
+     "iopub.status.idle": "2022-07-13T15:58:57.996122Z",
+     "shell.execute_reply": "2022-07-13T15:58:57.995686Z"
     },
     "papermill": {
-     "duration": 0.449276,
-     "end_time": "2022-04-18T00:17:50.875989",
+     "duration": 0.30664,
+     "end_time": "2022-07-13T15:58:57.996232",
      "exception": false,
-     "start_time": "2022-04-18T00:17:50.426713",
+     "start_time": "2022-07-13T15:58:57.689592",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [
@@ -1307,10 +1489,9 @@
       "text/plain": [
        "{'Version': '2020-12-01',\n",
        " 'Metadata': {},\n",
-       " 'Parameters': [{'Name': 'ProcessingInstanceType',\n",
-       "   'Type': 'String',\n",
-       "   'DefaultValue': 'ml.m5.xlarge'},\n",
-       "  {'Name': 'ProcessingInstanceCount', 'Type': 'Integer', 'DefaultValue': 1},\n",
+       " 'Parameters': [{'Name': 'ProcessingInstanceCount',\n",
+       "   'Type': 'Integer',\n",
+       "   'DefaultValue': 1},\n",
        "  {'Name': 'TrainingInstanceType',\n",
        "   'Type': 'String',\n",
        "   'DefaultValue': 'ml.m5.xlarge'},\n",
@@ -1324,18 +1505,18 @@
        "  'TrialName': {'Get': 'Execution.PipelineExecutionId'}},\n",
        " 'Steps': [{'Name': 'PreprocessAbaloneData',\n",
        "   'Type': 'Processing',\n",
-       "   'Arguments': {'ProcessingResources': {'ClusterConfig': {'InstanceType': {'Get': 'Parameters.ProcessingInstanceType'},\n",
+       "   'Arguments': {'ProcessingResources': {'ClusterConfig': {'InstanceType': 'ml.m5.xlarge',\n",
        "      'InstanceCount': {'Get': 'Parameters.ProcessingInstanceCount'},\n",
        "      'VolumeSizeInGB': 30}},\n",
-       "    'AppSpecification': {'ImageUri': '246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-scikit-learn:1.0-1-cpu-py3',\n",
+       "    'AppSpecification': {'ImageUri': '246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-scikit-learn:0.23-1-cpu-py3',\n",
        "     'ContainerArguments': ['--input-data',\n",
        "      {'Get': 'Parameters.InputDataUrl'}],\n",
        "     'ContainerEntrypoint': ['python3',\n",
        "      '/opt/ml/processing/input/code/preprocess.py']},\n",
-       "    'RoleArn': 'arn:aws:iam::000000000000:role/ProdBuildSystemStack-ReleaseBuildRoleFB326D49-QK8LUA2UI1IC',\n",
+       "    'RoleArn': 'arn:aws:iam::000000000000:role/SageMakerRole',\n",
        "    'ProcessingInputs': [{'InputName': 'code',\n",
        "      'AppManaged': False,\n",
-       "      'S3Input': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/lambda-step-example/sklearn-abalone-pre-2022-04-18-00-17-50-447/input/code/preprocess.py',\n",
+       "      'S3Input': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/lambda-step-example/sklearn-abalone-pre-2022-07-13-15-58-56-005/input/code/preprocess.py',\n",
        "       'LocalPath': '/opt/ml/processing/input/code',\n",
        "       'S3DataType': 'S3Prefix',\n",
        "       'S3InputMode': 'File',\n",
@@ -1343,17 +1524,17 @@
        "       'S3CompressionType': 'None'}}],\n",
        "    'ProcessingOutputConfig': {'Outputs': [{'OutputName': 'train',\n",
        "       'AppManaged': False,\n",
-       "       'S3Output': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/lambda-step-example/sklearn-abalone-pre-2022-04-18-00-17-50-447/output/train',\n",
+       "       'S3Output': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/lambda-step-example/sklearn-abalone-pre-2022-07-13-15-58-56-005/output/train',\n",
        "        'LocalPath': '/opt/ml/processing/train',\n",
        "        'S3UploadMode': 'EndOfJob'}},\n",
        "      {'OutputName': 'validation',\n",
        "       'AppManaged': False,\n",
-       "       'S3Output': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/lambda-step-example/sklearn-abalone-pre-2022-04-18-00-17-50-447/output/validation',\n",
+       "       'S3Output': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/lambda-step-example/sklearn-abalone-pre-2022-07-13-15-58-56-005/output/validation',\n",
        "        'LocalPath': '/opt/ml/processing/validation',\n",
        "        'S3UploadMode': 'EndOfJob'}},\n",
        "      {'OutputName': 'test',\n",
        "       'AppManaged': False,\n",
-       "       'S3Output': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/lambda-step-example/sklearn-abalone-pre-2022-04-18-00-17-50-447/output/test',\n",
+       "       'S3Output': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/lambda-step-example/sklearn-abalone-pre-2022-07-13-15-58-56-005/output/test',\n",
        "        'LocalPath': '/opt/ml/processing/test',\n",
        "        'S3UploadMode': 'EndOfJob'}}]}},\n",
        "   'CacheConfig': {'Enabled': True, 'ExpireAfter': '30d'}},\n",
@@ -1363,10 +1544,10 @@
        "     'TrainingImage': '246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-xgboost:1.0-1-cpu-py3'},\n",
        "    'OutputDataConfig': {'S3OutputPath': 's3://sagemaker-us-west-2-000000000000/lambda-step-example/AbaloneTrain'},\n",
        "    'StoppingCondition': {'MaxRuntimeInSeconds': 86400},\n",
-       "    'ResourceConfig': {'InstanceCount': 1,\n",
-       "     'InstanceType': {'Get': 'Parameters.TrainingInstanceType'},\n",
-       "     'VolumeSizeInGB': 30},\n",
-       "    'RoleArn': 'arn:aws:iam::000000000000:role/ProdBuildSystemStack-ReleaseBuildRoleFB326D49-QK8LUA2UI1IC',\n",
+       "    'ResourceConfig': {'VolumeSizeInGB': 30,\n",
+       "     'InstanceCount': 1,\n",
+       "     'InstanceType': {'Get': 'Parameters.TrainingInstanceType'}},\n",
+       "    'RoleArn': 'arn:aws:iam::000000000000:role/SageMakerRole',\n",
        "    'InputDataConfig': [{'DataSource': {'S3DataSource': {'S3DataType': 'S3Prefix',\n",
        "        'S3Uri': {'Get': \"Steps.PreprocessAbaloneData.ProcessingOutputConfig.Outputs['train'].S3Output.S3Uri\"},\n",
        "        'S3DataDistributionType': 'FullyReplicated'}},\n",
@@ -1385,20 +1566,20 @@
        "     'min_child_weight': '6',\n",
        "     'subsample': '0.7',\n",
        "     'silent': '0'},\n",
-       "    'ProfilerRuleConfigurations': [{'RuleConfigurationName': 'ProfilerReport-1650241070',\n",
+       "    'ProfilerRuleConfigurations': [{'RuleConfigurationName': 'ProfilerReport-1657727936',\n",
        "      'RuleEvaluatorImage': '895741380848.dkr.ecr.us-west-2.amazonaws.com/sagemaker-debugger-rules:latest',\n",
        "      'RuleParameters': {'rule_to_invoke': 'ProfilerReport'}}],\n",
        "    'ProfilerConfig': {'S3OutputPath': 's3://sagemaker-us-west-2-000000000000/lambda-step-example/AbaloneTrain'}},\n",
        "   'CacheConfig': {'Enabled': True, 'ExpireAfter': '30d'}},\n",
        "  {'Name': 'EvaluateAbaloneModel',\n",
        "   'Type': 'Processing',\n",
-       "   'Arguments': {'ProcessingResources': {'ClusterConfig': {'InstanceType': {'Get': 'Parameters.ProcessingInstanceType'},\n",
+       "   'Arguments': {'ProcessingResources': {'ClusterConfig': {'InstanceType': 'ml.m5.xlarge',\n",
        "      'InstanceCount': 1,\n",
        "      'VolumeSizeInGB': 30}},\n",
        "    'AppSpecification': {'ImageUri': '246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-xgboost:1.0-1-cpu-py3',\n",
        "     'ContainerEntrypoint': ['python3',\n",
        "      '/opt/ml/processing/input/code/evaluate.py']},\n",
-       "    'RoleArn': 'arn:aws:iam::000000000000:role/ProdBuildSystemStack-ReleaseBuildRoleFB326D49-QK8LUA2UI1IC',\n",
+       "    'RoleArn': 'arn:aws:iam::000000000000:role/SageMakerRole',\n",
        "    'ProcessingInputs': [{'InputName': 'input-1',\n",
        "      'AppManaged': False,\n",
        "      'S3Input': {'S3Uri': {'Get': 'Steps.TrainAbaloneModel.ModelArtifacts.S3ModelArtifacts'},\n",
@@ -1417,7 +1598,7 @@
        "       'S3CompressionType': 'None'}},\n",
        "     {'InputName': 'code',\n",
        "      'AppManaged': False,\n",
-       "      'S3Input': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/lambda-step-pipeline/lambda-step-exampl-2022-04-18-00-17-50-536/input/code/evaluate.py',\n",
+       "      'S3Input': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/lambda-step-pipeline/lambda-step-exampl-2022-07-13-15-58-56-545/input/code/evaluate.py',\n",
        "       'LocalPath': '/opt/ml/processing/input/code',\n",
        "       'S3DataType': 'S3Prefix',\n",
        "       'S3InputMode': 'File',\n",
@@ -1438,18 +1619,18 @@
        "      'LeftValue': {'Std:JsonGet': {'PropertyFile': {'Get': 'Steps.EvaluateAbaloneModel.PropertyFiles.AbaloneEvaluationReport'},\n",
        "        'Path': 'regression_metrics.mse.value'}},\n",
        "      'RightValue': 6.0}],\n",
-       "    'IfSteps': [{'Name': 'CreateModel',\n",
+       "    'IfSteps': [{'Name': 'CreateModel-CreateModel',\n",
        "      'Type': 'Model',\n",
-       "      'Arguments': {'ExecutionRoleArn': 'arn:aws:iam::000000000000:role/ProdBuildSystemStack-ReleaseBuildRoleFB326D49-QK8LUA2UI1IC',\n",
+       "      'Arguments': {'ExecutionRoleArn': 'arn:aws:iam::000000000000:role/SageMakerRole',\n",
        "       'PrimaryContainer': {'Image': '246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-xgboost:1.0-1-cpu-py3',\n",
        "        'Environment': {},\n",
        "        'ModelDataUrl': {'Get': 'Steps.TrainAbaloneModel.ModelArtifacts.S3ModelArtifacts'}}}},\n",
        "     {'Name': 'LambdaStep',\n",
        "      'Type': 'Lambda',\n",
-       "      'Arguments': {'model_name': {'Get': 'Steps.CreateModel.ModelName'},\n",
-       "       'endpoint_config_name': 'demo-lambda-deploy-endpoint-config-04-18-00-17-50',\n",
-       "       'endpoint_name': 'demo-lambda-deploy-endpoint-04-18-00-17-50'},\n",
-       "      'FunctionArn': 'arn:aws:lambda:us-west-2:000000000000:function:sagemaker-lambda-step-endpoint-deploy-04-18-00-17-50',\n",
+       "      'Arguments': {'model_name': {'Get': 'Steps.CreateModel-CreateModel.ModelName'},\n",
+       "       'endpoint_config_name': 'demo-lambda-deploy-endpoint-config-07-13-15-58-57',\n",
+       "       'endpoint_name': 'demo-lambda-deploy-endpoint-07-13-15-58-57'},\n",
+       "      'FunctionArn': 'arn:aws:lambda:us-west-2:000000000000:function:sagemaker-lambda-step-endpoint-deploy-07-13-15-58-57',\n",
        "      'OutputParameters': [{'OutputName': 'statusCode',\n",
        "        'OutputType': 'String'},\n",
        "       {'OutputName': 'body', 'OutputType': 'String'},\n",
@@ -1457,7 +1638,7 @@
        "    'ElseSteps': []}}]}"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1471,39 +1652,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "fa70bdb2",
+   "execution_count": 19,
+   "id": "468eb3d7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:17:50.919107Z",
-     "iopub.status.busy": "2022-04-18T00:17:50.918503Z",
-     "iopub.status.idle": "2022-04-18T00:17:51.361206Z",
-     "shell.execute_reply": "2022-04-18T00:17:51.360785Z"
+     "iopub.execute_input": "2022-07-13T15:58:58.041896Z",
+     "iopub.status.busy": "2022-07-13T15:58:58.041369Z",
+     "iopub.status.idle": "2022-07-13T15:58:59.758190Z",
+     "shell.execute_reply": "2022-07-13T15:58:59.757766Z"
     },
     "papermill": {
-     "duration": 0.465591,
-     "end_time": "2022-04-18T00:17:51.361313",
+     "duration": 1.74154,
+     "end_time": "2022-07-13T15:58:59.758302",
      "exception": false,
-     "start_time": "2022-04-18T00:17:50.895722",
+     "start_time": "2022-07-13T15:58:58.016762",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'PipelineArn': 'arn:aws:sagemaker:us-west-2:000000000000:pipeline/lambda-step-pipeline04-18-00-17-50',\n",
-       " 'ResponseMetadata': {'RequestId': '405aaf34-c135-4efc-a44b-7804be51583a',\n",
+       "{'PipelineArn': 'arn:aws:sagemaker:us-west-2:000000000000:pipeline/lambda-step-pipeline07-13-15-58-57',\n",
+       " 'ResponseMetadata': {'RequestId': 'b8946d56-ca49-4275-add7-d48f765b4931',\n",
        "  'HTTPStatusCode': 200,\n",
-       "  'HTTPHeaders': {'x-amzn-requestid': '405aaf34-c135-4efc-a44b-7804be51583a',\n",
+       "  'HTTPHeaders': {'x-amzn-requestid': 'b8946d56-ca49-4275-add7-d48f765b4931',\n",
        "   'content-type': 'application/x-amz-json-1.1',\n",
        "   'content-length': '102',\n",
-       "   'date': 'Mon, 18 Apr 2022 00:17:51 GMT'},\n",
+       "   'date': 'Wed, 13 Jul 2022 15:58:59 GMT'},\n",
        "  'RetryAttempts': 0}}"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1514,22 +1698,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "c3ff5590",
+   "execution_count": 20,
+   "id": "bd52d7e0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:17:51.406344Z",
-     "iopub.status.busy": "2022-04-18T00:17:51.405776Z",
-     "iopub.status.idle": "2022-04-18T00:17:51.638250Z",
-     "shell.execute_reply": "2022-04-18T00:17:51.637817Z"
+     "iopub.execute_input": "2022-07-13T15:58:59.805754Z",
+     "iopub.status.busy": "2022-07-13T15:58:59.805233Z",
+     "iopub.status.idle": "2022-07-13T15:59:00.068810Z",
+     "shell.execute_reply": "2022-07-13T15:59:00.068289Z"
     },
     "papermill": {
-     "duration": 0.257072,
-     "end_time": "2022-04-18T00:17:51.638388",
+     "duration": 0.289644,
+     "end_time": "2022-07-13T15:59:00.068954",
      "exception": false,
-     "start_time": "2022-04-18T00:17:51.381316",
+     "start_time": "2022-07-13T15:58:59.779310",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [],
@@ -1539,22 +1726,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "1891de27",
+   "execution_count": 21,
+   "id": "d30b7748",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:17:51.683933Z",
-     "iopub.status.busy": "2022-04-18T00:17:51.683371Z",
-     "iopub.status.idle": "2022-04-18T00:29:54.052358Z",
-     "shell.execute_reply": "2022-04-18T00:29:54.052761Z"
+     "iopub.execute_input": "2022-07-13T15:59:00.116180Z",
+     "iopub.status.busy": "2022-07-13T15:59:00.115665Z",
+     "iopub.status.idle": "2022-07-13T16:13:07.234138Z",
+     "shell.execute_reply": "2022-07-13T16:13:07.233620Z"
     },
     "papermill": {
-     "duration": 722.394302,
-     "end_time": "2022-04-18T00:29:54.052900",
+     "duration": 847.144553,
+     "end_time": "2022-07-13T16:13:07.234262",
      "exception": false,
-     "start_time": "2022-04-18T00:17:51.658598",
+     "start_time": "2022-07-13T15:59:00.089709",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [],
@@ -1564,22 +1754,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "id": "3d747238",
+   "execution_count": 22,
+   "id": "ca40dc3a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:29:54.103947Z",
-     "iopub.status.busy": "2022-04-18T00:29:54.101545Z",
-     "iopub.status.idle": "2022-04-18T00:33:24.717122Z",
-     "shell.execute_reply": "2022-04-18T00:33:24.717530Z"
+     "iopub.execute_input": "2022-07-13T16:13:07.286763Z",
+     "iopub.status.busy": "2022-07-13T16:13:07.286297Z",
+     "iopub.status.idle": "2022-07-13T16:16:07.831769Z",
+     "shell.execute_reply": "2022-07-13T16:16:07.831267Z"
     },
     "papermill": {
-     "duration": 210.644301,
-     "end_time": "2022-04-18T00:33:24.717669",
+     "duration": 180.576174,
+     "end_time": "2022-07-13T16:16:07.831903",
      "exception": false,
-     "start_time": "2022-04-18T00:29:54.073368",
+     "start_time": "2022-07-13T16:13:07.255729",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [],
@@ -1594,13 +1787,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9b4a173b",
+   "id": "66aa70a4",
    "metadata": {
     "papermill": {
-     "duration": 0.020528,
-     "end_time": "2022-04-18T00:33:24.758623",
+     "duration": 0.021213,
+     "end_time": "2022-07-13T16:16:07.874605",
      "exception": false,
-     "start_time": "2022-04-18T00:33:24.738095",
+     "start_time": "2022-07-13T16:16:07.853392",
      "status": "completed"
     },
     "tags": []
@@ -1618,39 +1811,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "e6305fbe",
+   "execution_count": 23,
+   "id": "6b8f340a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-04-18T00:33:24.842207Z",
-     "iopub.status.busy": "2022-04-18T00:33:24.841656Z",
-     "iopub.status.idle": "2022-04-18T00:33:25.560327Z",
-     "shell.execute_reply": "2022-04-18T00:33:25.560701Z"
+     "iopub.execute_input": "2022-07-13T16:16:07.926674Z",
+     "iopub.status.busy": "2022-07-13T16:16:07.926158Z",
+     "iopub.status.idle": "2022-07-13T16:16:08.804031Z",
+     "shell.execute_reply": "2022-07-13T16:16:08.804450Z"
     },
     "papermill": {
-     "duration": 0.782103,
-     "end_time": "2022-04-18T00:33:25.560838",
+     "duration": 0.908752,
+     "end_time": "2022-07-13T16:16:08.804592",
      "exception": false,
-     "start_time": "2022-04-18T00:33:24.778735",
+     "start_time": "2022-07-13T16:16:07.895840",
      "status": "completed"
     },
+    "pycharm": {
+     "name": "#%%\n"
+    },
     "tags": []
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'PipelineArn': 'arn:aws:sagemaker:us-west-2:000000000000:pipeline/lambda-step-pipeline04-18-00-17-50',\n",
-       " 'ResponseMetadata': {'RequestId': '48e9d128-0f40-4c52-92dd-93b057e06e12',\n",
+       "{'PipelineArn': 'arn:aws:sagemaker:us-west-2:000000000000:pipeline/lambda-step-pipeline07-13-15-58-57',\n",
+       " 'ResponseMetadata': {'RequestId': 'f71a3ce5-bac1-4250-9129-4161507f62d4',\n",
        "  'HTTPStatusCode': 200,\n",
-       "  'HTTPHeaders': {'x-amzn-requestid': '48e9d128-0f40-4c52-92dd-93b057e06e12',\n",
+       "  'HTTPHeaders': {'x-amzn-requestid': 'f71a3ce5-bac1-4250-9129-4161507f62d4',\n",
        "   'content-type': 'application/x-amz-json-1.1',\n",
        "   'content-length': '102',\n",
-       "   'date': 'Mon, 18 Apr 2022 00:33:24 GMT'},\n",
+       "   'date': 'Wed, 13 Jul 2022 16:16:08 GMT'},\n",
        "  'RetryAttempts': 0}}"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1683,7 +1879,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -1697,7 +1893,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.9"
+   "version": "3.7.10"
   },
   "metadata": {
    "interpreter": {
@@ -1706,16 +1902,16 @@
   },
   "papermill": {
    "default_parameters": {},
-   "duration": 941.276516,
-   "end_time": "2022-04-18T00:33:26.098307",
+   "duration": 1037.911631,
+   "end_time": "2022-07-13T16:16:09.344184",
    "environment_variables": {},
    "exception": null,
    "input_path": "sagemaker-pipelines-lambda-step.ipynb",
-   "output_path": "/opt/ml/processing/output/sagemaker-pipelines-lambda-step-2022-04-18-00-13-01.ipynb",
+   "output_path": "/opt/ml/processing/output/sagemaker-pipelines-lambda-step-2022-07-13-15-54-23.ipynb",
    "parameters": {
     "kms_key": "arn:aws:kms:us-west-2:000000000000:1234abcd-12ab-34cd-56ef-1234567890ab"
    },
-   "start_time": "2022-04-18T00:17:44.821791",
+   "start_time": "2022-07-13T15:58:51.432553",
    "version": "2.3.4"
   }
  },
diff --git a/sagemaker-pipelines/tabular/model-monitor-clarify-pipelines/sagemaker-pipeline-model-monitor-clarify-steps.ipynb b/sagemaker-pipelines/tabular/model-monitor-clarify-pipelines/sagemaker-pipeline-model-monitor-clarify-steps.ipynb
index 73bbebf015..53f2958a51 100644
--- a/sagemaker-pipelines/tabular/model-monitor-clarify-pipelines/sagemaker-pipeline-model-monitor-clarify-steps.ipynb
+++ b/sagemaker-pipelines/tabular/model-monitor-clarify-pipelines/sagemaker-pipeline-model-monitor-clarify-steps.ipynb
@@ -7,7 +7,7 @@
     "## SageMaker Pipelines integration with Model Monitor and Clarify\n",
     "\n",
     "This notebook showcases how Model Monitor and Clarify steps can be integrated with SageMaker Pipelines. This allows users to calculate\n",
-    "baselines for data quality and model quality checks by running the underlying Model Monitor and Clarify containers. "
+    "baselines for data quality and model quality checks by running the underlying Model Monitor and Clarify containers."
    ]
   },
   {
@@ -16,7 +16,7 @@
    "source": [
     "## Data/Model Quality, Bias, and Model Explainability Checks in SageMaker Pipelines\n",
     "\n",
-    "This notebook introduces two new step types in SageMaker Pipelines - \n",
+    "This notebook introduces two new step types in SageMaker Pipelines -\n",
     "* `QualityCheckStep`\n",
     "* `ClarifyCheckStep`\n",
     "\n",
@@ -29,7 +29,7 @@
     "\n",
     "The training dataset that you used to train the model is usually a good baseline dataset. The training dataset data schema and the inference dataset schema should exactly match (the number and order of the features). Note that the prediction/output columns are assumed to be the first columns in the training dataset. From the training dataset, you can ask SageMaker to suggest a set of baseline constraints and generate descriptive statistics to explore the data.\n",
     "\n",
-    "These two new steps will always calculate new baselines using the dataset provided. "
+    "These two new steps will always calculate new baselines using the dataset provided."
    ]
   },
   {
@@ -38,20 +38,20 @@
    "source": [
     "### Drift Check Baselines in the Model Registry\n",
     "\n",
-    "The `RegisterStep` has a new parameter called `drift_check_baselines`. This refers to the baseline files associated with the model. When deployed, these baseline files are used by Model Monitor for Model Quality/Data Quality checks. In addition, these baselines can be used in `QualityCheckStep` and `ClarifyCheckStep` to compare newly trained models against models that have already been registered in the Model Registry. \n",
+    "The `RegisterStep` has a new parameter called `drift_check_baselines`. This refers to the baseline files associated with the model. When deployed, these baseline files are used by Model Monitor for Model Quality/Data Quality checks. In addition, these baselines can be used in `QualityCheckStep` and `ClarifyCheckStep` to compare newly trained models against models that have already been registered in the Model Registry.\n",
     "\n",
     "### Step Properties\n",
     "\n",
     "The `QualityCheckStep` has the following properties -\n",
     "\n",
     "* `CalculatedBaselineStatistics` : The baseline statistics file calculated by the underlying Model Monitor container.\n",
-    "* `CalculatedBaselineConstraints` : The baseline constraints file calculated by the underlying Model Monitor container. \n",
-    "* `BaselineUsedForDriftCheckStatistics` and `BaselineUsedForDriftCheckConstraints` : These are the two properties used to set `drift_check_baseline` in the Model Registry. The values set in these properties vary depending on the parameters passed to the step. The different behaviors are described in the table below. \n",
-    "  \n",
-    "The `ClarifyCheckStep` has the following properties - \n",
+    "* `CalculatedBaselineConstraints` : The baseline constraints file calculated by the underlying Model Monitor container.\n",
+    "* `BaselineUsedForDriftCheckStatistics` and `BaselineUsedForDriftCheckConstraints` : These are the two properties used to set `drift_check_baseline` in the Model Registry. The values set in these properties vary depending on the parameters passed to the step. The different behaviors are described in the table below.\n",
     "\n",
-    "* `CalculatedBaselineConstraints` : The baseline constraints file calculated by the underlying Clarify container. \n",
-    "* `BaselineUsedForDriftCheckConstraints` : This property is used to set `drift_check_baseline` in the Model Registry. The values set in this property will vary depending on the parameters passed to the step. The different behaviors are described in the table below. "
+    "The `ClarifyCheckStep` has the following properties -\n",
+    "\n",
+    "* `CalculatedBaselineConstraints` : The baseline constraints file calculated by the underlying Clarify container.\n",
+    "* `BaselineUsedForDriftCheckConstraints` : This property is used to set `drift_check_baseline` in the Model Registry. The values set in this property will vary depending on the parameters passed to the step. The different behaviors are described in the table below."
    ]
   },
   {
@@ -60,7 +60,7 @@
    "source": [
     "### Notebook Overview\n",
     "\n",
-    "This notebook should be run with `Python 3.9` using the SageMaker Studio `Python3 (Data Science)` kernel. The `sagemaker` sdk version required for this notebook is `>2.70.0`."
+    "This notebook should be run with `Python 3.9` using the SageMaker Studio `Python3 (Data Science)` kernel."
    ]
   },
   {
@@ -73,17 +73,25 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "! pip install botocore boto3 awscli --upgrade\n",
-    "! pip install \"sagemaker==2.91.1\""
+    "! pip install \"sagemaker>=2.99.0\""
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "import os\n",
@@ -123,7 +131,8 @@
     "    CreateModelStep,\n",
     "    TransformStep,\n",
     ")\n",
-    "from sagemaker.workflow.step_collections import RegisterModel\n",
+    "from sagemaker.workflow.model_step import ModelStep\n",
+    "from sagemaker.workflow.pipeline_context import PipelineSession\n",
     "\n",
     "# Importing new steps and helper functions\n",
     "\n",
@@ -157,13 +166,18 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "region = sagemaker.Session().boto_region_name\n",
     "sm_client = boto3.client(\"sagemaker\")\n",
     "boto_session = boto3.Session(region_name=region)\n",
     "sagemaker_session = sagemaker.session.Session(boto_session=boto_session, sagemaker_client=sm_client)\n",
+    "pipeline_session = PipelineSession()\n",
     "prefix = \"model-monitor-clarify-step-pipeline\""
    ]
   },
@@ -177,7 +191,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "role = sagemaker.get_execution_role()\n",
@@ -193,11 +211,11 @@
    "source": [
     "### Define pipeline parameters\n",
     "\n",
-    "Both `QualityCheckStep` and `ClarifyCheckStep` use two boolean flags `skip_check` and `register_new_baseline` to control their behavior. \n",
+    "Both `QualityCheckStep` and `ClarifyCheckStep` use two boolean flags `skip_check` and `register_new_baseline` to control their behavior.\n",
     "\n",
     "* `skip_check` : This determines if a drift check is executed or not.\n",
     "* `register_new_baseline` : This determines if the newly calculated baselines (in the step property `CalculatedBaselines`) should be set in the step property `BaselineUsedForDriftCheck`.\n",
-    "* `supplied_baseline_statistics` and `supplied_baseline_constraints` : If `skip_check` is set to False, baselines can be provided to this step through this parameter. If provided, the step will compare the newly calculated baselines (`CalculatedBaselines`) against those provided here instead of finding the latest baselines from the Model Registry. In the case of `ClarifyCheckStep`, only `supplied_baseline_constraints` is a valid parameter, for `QualityCheckStep`, both parameters are used. \n",
+    "* `supplied_baseline_statistics` and `supplied_baseline_constraints` : If `skip_check` is set to False, baselines can be provided to this step through this parameter. If provided, the step will compare the newly calculated baselines (`CalculatedBaselines`) against those provided here instead of finding the latest baselines from the Model Registry. In the case of `ClarifyCheckStep`, only `supplied_baseline_constraints` is a valid parameter, for `QualityCheckStep`, both parameters are used.\n",
     "* `model_package_group_name` : The step will use the `drift_check_baselines` from the latest approved model in the model package group for the drift check. If `supplied_baseline_*` is provided, this field will be ignored.\n",
     "\n",
     "The first time the pipeline is run, the `skip_check` value should be set to True using the pipeline execution parameters so that new baselines are registered and no drift check is executed."
@@ -209,13 +227,13 @@
    "source": [
     "### Combining Pipeline parameters\n",
     "\n",
-    "This table summarizes how the pipeline parameters work when combined. \n",
+    "This table summarizes how the pipeline parameters work when combined.\n",
     "\n",
     "The parameter `drift_check_baselines` is used to supply baselines to the `RegisterStep` that will be used for all drift checks involving the model.\n",
     "\n",
-    "Newly calculated baselines can be reference by the properties `CalculatedBaselineStatistics` and `CalculatedBaselineConstraints` on the `QualityCheckStep` and `CalculatedBaselineConstraints` on the `ClarifyCheckStep`. \n",
+    "Newly calculated baselines can be reference by the properties `CalculatedBaselineStatistics` and `CalculatedBaselineConstraints` on the `QualityCheckStep` and `CalculatedBaselineConstraints` on the `ClarifyCheckStep`.\n",
     "\n",
-    "For example, `data_quality_check_step.properties.CalculatedBaselineStatistics` and `data_quality_check_step.properties.CalculatedBaselineConstraints`. This property refers to the baseline that is calculated when the data quality check step is executed. "
+    "For example, `data_quality_check_step.properties.CalculatedBaselineStatistics` and `data_quality_check_step.properties.CalculatedBaselineConstraints`. This property refers to the baseline that is calculated when the data quality check step is executed."
    ]
   },
   {
@@ -224,7 +242,7 @@
    "source": [
     "\n",
     "| `skip_check` / `register_new_baseline` | Does step do a drift check?                              | Value of step property `CalculatedBaseline`                                                           | Value of step property `BaselineUsedForDriftCheck`      | Possible Circumstances for this parameter combination|\n",
-    "| -------------------------------------- | ---------------------------------------------------------|------------------------------------------------------------                                           |------------------------------------------------- | -----------------------------------------------------| \n",
+    "| -------------------------------------- | ---------------------------------------------------------|------------------------------------------------------------                                           |------------------------------------------------- | -----------------------------------------------------|\n",
     "| F / F                                  | Drift Check executed against existing baselines.         | New baselines calculated by step execution                                                            |  Baseline from latest approved model in Model Registry or baseline supplied as step parameter                                    | Regular re-training with checks enabled to get a new model version, but carry over previous baselines as DriftCheckBaselines in Registry for new model version.                                                                                                                                                             |\n",
     "| F / T                                  | Drift Check executed against existing baselines.         | New baselines calculated by step execution                  | Newly calculated baseline by step execution (value of property `CalculatedBaseline`)                                     | Regular re-training with checks enabled to get a new model version, but refresh DriftCheckBaselines in Registry with newly calculated baselines for the new model version.                                                                                                                                                  |\n",
     "| T / F                                  | No Drift Check.                                          | New baselines calculated by step execution          | Baseline from latest approved model in Model Registry or baseline supplied as step parameter                                     | Violation detected by the model monitor on endpoint for a particular type of check and the pipeline is triggered for retraining a new model. Skip the check against previous baselines, but carry over previous baselines as DriftCheckBaselines in Registry for new model version.             |\n",
@@ -234,7 +252,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "processing_instance_count = ParameterInteger(name=\"ProcessingInstanceCount\", default_value=1)\n",
@@ -313,10 +335,27 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
-    "%%writefile preprocess.py\n",
+    "!mkdir -p code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%%writefile code/preprocess.py\n",
     "\n",
     "\"\"\"Feature engineers the abalone dataset.\"\"\"\n",
     "import argparse\n",
@@ -446,7 +485,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "sklearn_processor = SKLearnProcessor(\n",
@@ -454,21 +497,19 @@
     "    instance_type=\"ml.m5.xlarge\",\n",
     "    instance_count=processing_instance_count,\n",
     "    base_job_name=f\"{base_job_prefix}/sklearn-abalone-preprocess\",\n",
-    "    sagemaker_session=sagemaker_session,\n",
+    "    sagemaker_session=pipeline_session,\n",
     "    role=role,\n",
     ")\n",
-    "\n",
-    "step_process = ProcessingStep(\n",
-    "    name=\"PreprocessAbaloneData\",\n",
-    "    processor=sklearn_processor,\n",
+    "processor_args = sklearn_processor.run(\n",
     "    outputs=[\n",
     "        ProcessingOutput(output_name=\"train\", source=\"/opt/ml/processing/train\"),\n",
     "        ProcessingOutput(output_name=\"validation\", source=\"/opt/ml/processing/validation\"),\n",
     "        ProcessingOutput(output_name=\"test\", source=\"/opt/ml/processing/test\"),\n",
     "    ],\n",
-    "    code=\"preprocess.py\",\n",
-    "    job_arguments=[\"--input-data\", input_data],\n",
-    ")"
+    "    code=\"code/preprocess.py\",\n",
+    "    arguments=[\"--input-data\", input_data],\n",
+    ")\n",
+    "step_process = ProcessingStep(name=\"PreprocessAbaloneData\", step_args=processor_args)"
    ]
   },
   {
@@ -477,15 +518,19 @@
    "source": [
     "### Calculating the Data Quality\n",
     "\n",
-    "`CheckJobConfig` is a helper function that's used to define the job configurations used by the `QualityCheckStep`. By separating the job configuration from the step parameters, the same `CheckJobConfig` can be used across multiple steps for quality checks. \n",
+    "`CheckJobConfig` is a helper function that's used to define the job configurations used by the `QualityCheckStep`. By separating the job configuration from the step parameters, the same `CheckJobConfig` can be used across multiple steps for quality checks.\n",
     "\n",
-    "The `DataQualityCheckConfig` is used to define the Quality Check job by specifying the dataset used to calculate the baseline, in this case, the training dataset from the data processing step, the dataset format, in this case, a csv file with no headers, and the output path for the results of the data quality check. "
+    "The `DataQualityCheckConfig` is used to define the Quality Check job by specifying the dataset used to calculate the baseline, in this case, the training dataset from the data processing step, the dataset format, in this case, a csv file with no headers, and the output path for the results of the data quality check."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "check_job_config = CheckJobConfig(\n",
@@ -529,9 +574,9 @@
    "source": [
     "### Calculating the Data Bias\n",
     "\n",
-    "The job configuration from the previous step is used here and the `DataConfig` class is used to define how the `ClarifyCheckStep` should compute the data bias. The training dataset is used again for the bias evaluation, the column representing the label is specified through the `label` parameter, and a `BiasConfig` is provided. \n",
+    "The job configuration from the previous step is used here and the `DataConfig` class is used to define how the `ClarifyCheckStep` should compute the data bias. The training dataset is used again for the bias evaluation, the column representing the label is specified through the `label` parameter, and a `BiasConfig` is provided.\n",
     "\n",
-    "In the `BiasConfig`, we specify a facet name (the column that is the focal point of the bias calculation), the value of the facet that determines the range of values it can hold, and the threshold value for the label. \n",
+    "In the `BiasConfig`, we specify a facet name (the column that is the focal point of the bias calculation), the value of the facet that determines the range of values it can hold, and the threshold value for the label.\n",
     "\n",
     "More details on `BiasConfig` can be found [here](https://sagemaker.readthedocs.io/en/stable/api/training/processing.html#sagemaker.clarify.BiasConfig)."
    ]
@@ -539,7 +584,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "data_bias_analysis_cfg_output_path = (\n",
@@ -596,7 +645,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "model_path = f\"s3://{sagemaker_session.default_bucket()}/{base_job_prefix}/AbaloneTrain\"\n",
@@ -614,7 +667,7 @@
     "    instance_count=1,\n",
     "    output_path=model_path,\n",
     "    base_job_name=f\"{base_job_prefix}/abalone-train\",\n",
-    "    sagemaker_session=sagemaker_session,\n",
+    "    sagemaker_session=pipeline_session,\n",
     "    role=role,\n",
     ")\n",
     "\n",
@@ -629,9 +682,7 @@
     "    silent=0,\n",
     ")\n",
     "\n",
-    "step_train = TrainingStep(\n",
-    "    name=\"TrainAbaloneModel\",\n",
-    "    estimator=xgb_train,\n",
+    "train_args = xgb_train.fit(\n",
     "    inputs={\n",
     "        \"train\": TrainingInput(\n",
     "            s3_data=step_process.properties.ProcessingOutputConfig.Outputs[\"train\"].S3Output.S3Uri,\n",
@@ -644,6 +695,10 @@
     "            content_type=\"text/csv\",\n",
     "        ),\n",
     "    },\n",
+    ")\n",
+    "step_train = TrainingStep(\n",
+    "    name=\"TrainAbaloneModel\",\n",
+    "    step_args=train_args,\n",
     "    depends_on=[data_bias_check_step.name, data_quality_check_step.name],\n",
     ")"
    ]
@@ -660,25 +715,23 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "model = Model(\n",
     "    image_uri=image_uri,\n",
     "    model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,\n",
-    "    sagemaker_session=sagemaker_session,\n",
+    "    sagemaker_session=pipeline_session,\n",
     "    role=role,\n",
     ")\n",
     "\n",
-    "inputs = CreateModelInput(\n",
-    "    instance_type=\"ml.m5.large\",\n",
-    "    accelerator_type=\"ml.eia1.medium\",\n",
-    ")\n",
-    "\n",
-    "step_create_model = CreateModelStep(\n",
+    "step_create_model = ModelStep(\n",
     "    name=\"AbaloneCreateModel\",\n",
-    "    model=model,\n",
-    "    inputs=inputs,\n",
+    "    step_args=model.create(instance_type=\"ml.m5.large\", accelerator_type=\"ml.eia1.medium\"),\n",
     ")"
    ]
   },
@@ -695,7 +748,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "transformer = Transformer(\n",
@@ -727,13 +784,17 @@
    "source": [
     "### Check the Model Quality\n",
     "\n",
-    "In this `QualityCheckStep` we calculate the baselines for statistics and constraints using the predictions that the model generates from the test dataset (output from the TransformStep). We define the problem type as 'Regression' in the `ModelQualityCheckConfig` along with specifying the columns which represent the input and output. Since the dataset has no headers, `_c0`, `_c1` are auto-generated header names that should be used in the `ModelQualityCheckConfig`. "
+    "In this `QualityCheckStep` we calculate the baselines for statistics and constraints using the predictions that the model generates from the test dataset (output from the TransformStep). We define the problem type as 'Regression' in the `ModelQualityCheckConfig` along with specifying the columns which represent the input and output. Since the dataset has no headers, `_c0`, `_c1` are auto-generated header names that should be used in the `ModelQualityCheckConfig`."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "model_quality_check_config = ModelQualityCheckConfig(\n",
@@ -772,13 +833,17 @@
    "source": [
     "### Check for Model Bias\n",
     "\n",
-    "Similar to the Data Bias check step, a `BiasConfig` is defined and Clarify is used to calculate the model bias using the training dataset and the model. "
+    "Similar to the Data Bias check step, a `BiasConfig` is defined and Clarify is used to calculate the model bias using the training dataset and the model."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "model_bias_analysis_cfg_output_path = (\n",
@@ -839,15 +904,19 @@
    "source": [
     "### Check Model Explainability\n",
     "\n",
-    "SageMaker Clarify uses a model-agnostic feature attribution approach, which you can use to understand why a model made a prediction after training and to provide per-instance explanation during inference. The implementation includes a scalable and efficient implementation of SHAP, based on the concept of a Shapley value from the field of cooperative game theory that assigns each feature an importance value for a particular prediction. \n",
+    "SageMaker Clarify uses a model-agnostic feature attribution approach, which you can use to understand why a model made a prediction after training and to provide per-instance explanation during inference. The implementation includes a scalable and efficient implementation of SHAP, based on the concept of a Shapley value from the field of cooperative game theory that assigns each feature an importance value for a particular prediction.\n",
     "\n",
-    "For Model Explainability, Clarify requires an explainability configuration to be provided. In this example, we use `SHAPConfig`. For more information of `explainability_config`, visit the [Clarify documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-model-explainability.html). "
+    "For Model Explainability, Clarify requires an explainability configuration to be provided. In this example, we use `SHAPConfig`. For more information of `explainability_config`, visit the [Clarify documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-model-explainability.html)."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "model_explainability_analysis_cfg_output_path = \"s3://{}/{}/{}/{}\".format(\n",
@@ -895,16 +964,20 @@
    "source": [
     "### Evaluate the performance of the model\n",
     "\n",
-    "Using a processing job, evaluate the performance of the model. The performance is used in the Condition Step to determine if the model should be registered or not. "
+    "Using a processing job, evaluate the performance of the model. The performance is used in the Condition Step to determine if the model should be registered or not."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
-    "%%writefile evaluate.py\n",
+    "%%writefile code/evaluate.py\n",
     "\n",
     "\"\"\"Evaluation script for measuring mean squared error.\"\"\"\n",
     "import json\n",
@@ -966,7 +1039,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "script_eval = ScriptProcessor(\n",
@@ -975,7 +1052,7 @@
     "    instance_type=\"ml.m5.xlarge\",\n",
     "    instance_count=1,\n",
     "    base_job_name=f\"{base_job_prefix}/script-abalone-eval\",\n",
-    "    sagemaker_session=sagemaker_session,\n",
+    "    sagemaker_session=pipeline_session,\n",
     "    role=role,\n",
     ")\n",
     "evaluation_report = PropertyFile(\n",
@@ -983,9 +1060,8 @@
     "    output_name=\"evaluation\",\n",
     "    path=\"evaluation.json\",\n",
     ")\n",
-    "step_eval = ProcessingStep(\n",
-    "    name=\"EvaluateAbaloneModel\",\n",
-    "    processor=script_eval,\n",
+    "\n",
+    "eval_args = script_eval.run(\n",
     "    inputs=[\n",
     "        ProcessingInput(\n",
     "            source=step_train.properties.ModelArtifacts.S3ModelArtifacts,\n",
@@ -999,7 +1075,11 @@
     "    outputs=[\n",
     "        ProcessingOutput(output_name=\"evaluation\", source=\"/opt/ml/processing/evaluation\"),\n",
     "    ],\n",
-    "    code=\"evaluate.py\",\n",
+    "    code=\"code/evaluate.py\",\n",
+    ")\n",
+    "step_eval = ProcessingStep(\n",
+    "    name=\"EvaluateAbaloneModel\",\n",
+    "    step_args=eval_args,\n",
     "    property_files=[evaluation_report],\n",
     ")"
    ]
@@ -1014,7 +1094,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "model_metrics = ModelMetrics(\n",
@@ -1094,7 +1178,7 @@
    "source": [
     "### Register the model\n",
     "\n",
-    "The two parameters in `RegisterModel` that hold the metrics calculated by the `ClarifyCheckStep` and `QualityCheckStep` are `model_metrics` and `drift_check_baselines`. \n",
+    "The two parameters in `RegisterModel` that hold the metrics calculated by the `ClarifyCheckStep` and `QualityCheckStep` are `model_metrics` and `drift_check_baselines`.\n",
     "\n",
     "`drift_check_baselines` - these are the baseline files that will be used for drift checks in `QualityCheckStep` or `ClarifyCheckStep` and model monitoring jobs that are set up on endpoints hosting this model.\n",
     "\n",
@@ -1102,19 +1186,20 @@
     "\n",
     "The intention behind these parameters is to give users a way to configure the baselines associated with a model so they can be used in drift checks or model monitoring jobs. Each time a pipeline is executed, users can choose to update the `drift_check_baselines` with newly calculated baselines. The `model_metrics` can be used to register the newly calculated baselines or any other metrics associated with the model.\n",
     "\n",
-    "Every time a baseline is calculated, it is not necessary that the baselines used for drift checks are updated to the newly calculated baselines. In some cases, users may retain an older version of the baseline file to be used for drift checks and not register new baselines that are calculated in the Pipeline run. "
+    "Every time a baseline is calculated, it is not necessary that the baselines used for drift checks are updated to the newly calculated baselines. In some cases, users may retain an older version of the baseline file to be used for drift checks and not register new baselines that are calculated in the Pipeline run."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
-    "step_register = RegisterModel(\n",
-    "    name=\"RegisterAbaloneModel\",\n",
-    "    estimator=xgb_train,\n",
-    "    model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,\n",
+    "register_args = model.register(\n",
     "    content_types=[\"text/csv\"],\n",
     "    response_types=[\"text/csv\"],\n",
     "    inference_instances=[\"ml.t2.medium\", \"ml.m5.large\"],\n",
@@ -1123,13 +1208,19 @@
     "    approval_status=model_approval_status,\n",
     "    model_metrics=model_metrics,\n",
     "    drift_check_baselines=drift_check_baselines,\n",
-    ")"
+    ")\n",
+    "\n",
+    "step_register = ModelStep(name=\"RegisterAbaloneModel\", step_args=register_args)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "# condition step for evaluating model quality and branching execution\n",
@@ -1159,7 +1250,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "# pipeline instance\n",
@@ -1201,7 +1296,7 @@
     "        step_eval,\n",
     "        step_cond,\n",
     "    ],\n",
-    "    sagemaker_session=sagemaker_session,\n",
+    "    sagemaker_session=pipeline_session,\n",
     ")"
    ]
   },
@@ -1215,7 +1310,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "import json\n",
@@ -1227,7 +1326,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "pipeline.upsert(role_arn=role)"
@@ -1245,7 +1348,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "execution = pipeline.start(\n",
@@ -1274,7 +1381,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "execution.wait()"
@@ -1292,7 +1403,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "# Create a SageMaker client\n",
@@ -1315,21 +1430,21 @@
  "metadata": {
   "instance_type": "ml.t3.medium",
   "kernelspec": {
-   "display_name": "conda_amazonei_mxnet_p27",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "conda_amazonei_mxnet_p27"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.16"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.14"
   }
  },
  "nbformat": 4,
diff --git a/sagemaker-pipelines/tabular/train-register-deploy-pipeline-model/train register and deploy a pipeline model.ipynb b/sagemaker-pipelines/tabular/train-register-deploy-pipeline-model/train register and deploy a pipeline model.ipynb
index 7b6a7f83fc..2fbf96183d 100644
--- a/sagemaker-pipelines/tabular/train-register-deploy-pipeline-model/train register and deploy a pipeline model.ipynb	
+++ b/sagemaker-pipelines/tabular/train-register-deploy-pipeline-model/train register and deploy a pipeline model.ipynb	
@@ -6,13 +6,32 @@
    "source": [
     "# SageMaker Pipelines\n",
     "\n",
-    "The following notebook shows how to create an Amazon SageMaker Pipeline that builds and trains a **PipelineModel** consisting of a preprocessing SKLearn script followed by a TensorFlow model. The pipeline model is then registered to the Model Registry and deployed from there into a real-time endpoint. "
+    "The following notebook shows how to create an Amazon SageMaker Pipeline that builds and trains a **PipelineModel** consisting of a preprocessing SKLearn script followed by a TensorFlow model. The pipeline model is then registered to the Model Registry and deployed from there into a real-time endpoint."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "\n",
+    "!{sys.executable} -m pip install \"sagemaker>=2.99.0\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "import os\n",
@@ -21,13 +40,18 @@
     "import numpy as np\n",
     "import pandas as pd\n",
     "import sagemaker\n",
-    "from sagemaker import get_execution_role"
+    "from sagemaker import get_execution_role\n",
+    "from sagemaker.workflow.pipeline_context import PipelineSession"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "sess = boto3.Session()\n",
@@ -37,9 +61,11 @@
     "bucket = sagemaker_session.default_bucket()\n",
     "region = boto3.Session().region_name\n",
     "\n",
+    "pipeline_session = PipelineSession()\n",
+    "\n",
     "model_package_group_name = \"PipelineModelPackageGroup\"\n",
     "prefix = \"pipeline-model-example\"\n",
-    "pipeline_name = \"TrainingPipelineForModel\"  # SageMaker Pipeline name"
+    "pipeline_name = \"serial-inference-pipeline\"  # SageMaker Pipeline name"
    ]
   },
   {
@@ -62,7 +88,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "data_dir = os.path.join(os.getcwd(), \"data\")\n",
@@ -75,7 +105,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "!aws s3 cp s3://sagemaker-sample-files/datasets/tabular/california_housing/cal_housing.tgz ."
@@ -84,7 +118,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "!tar -zxf cal_housing.tgz"
@@ -93,7 +131,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "columns = [\n",
@@ -135,7 +177,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "from sagemaker.workflow.parameters import ParameterInteger, ParameterString, ParameterFloat\n",
@@ -144,14 +190,16 @@
     "input_data = ParameterString(name=\"InputData\", default_value=raw_s3)\n",
     "\n",
     "# status of newly trained model in registry\n",
-    "model_approval_status = ParameterString(\n",
-    "    name=\"ModelApprovalStatus\", default_value=\"Approved\"\n",
-    ")  # PendingManualApproval | Rejected\n",
+    "model_approval_status = ParameterString(name=\"ModelApprovalStatus\", default_value=\"Approved\")\n",
     "\n",
     "# processing step parameters\n",
+    "processing_instance_type = ParameterString(\n",
+    "    name=\"ProcessingInstanceType\", default_value=\"ml.m5.xlarge\"\n",
+    ")\n",
     "processing_instance_count = ParameterInteger(name=\"ProcessingInstanceCount\", default_value=1)\n",
     "\n",
     "# training step parameters\n",
+    "training_instance_type = ParameterString(name=\"TrainingInstanceType\", default_value=\"ml.m5.xlarge\")\n",
     "training_epochs = ParameterString(name=\"TrainingEpochs\", default_value=\"100\")\n",
     "\n",
     "# model performance step parameters\n",
@@ -169,13 +217,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The below preprocessing script, in addition to creating a scaler, contains the necessary functions for it to be deployed as part of a pipeline model. "
+    "The below preprocessing script, in addition to creating a scaler, contains the necessary functions for it to be deployed as part of a pipeline model."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "!mkdir -p code"
@@ -184,7 +236,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "%%writefile code/preprocess.py\n",
@@ -325,11 +381,15 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "from sagemaker.sklearn.processing import SKLearnProcessor\n",
-    "\n",
+    "from sagemaker.processing import ProcessingInput, ProcessingOutput\n",
     "\n",
     "sklearn_framework_version = \"0.23-1\"\n",
     "\n",
@@ -339,13 +399,30 @@
     "    instance_count=processing_instance_count,\n",
     "    base_job_name=\"sklearn-housing-data-process\",\n",
     "    role=role,\n",
+    "    sagemaker_session=pipeline_session,\n",
+    ")\n",
+    "\n",
+    "processor_args = sklearn_processor.run(\n",
+    "    inputs=[\n",
+    "        ProcessingInput(source=input_data, destination=\"/opt/ml/processing/input\"),\n",
+    "    ],\n",
+    "    outputs=[\n",
+    "        ProcessingOutput(output_name=\"scaler_model\", source=\"/opt/ml/processing/scaler_model\"),\n",
+    "        ProcessingOutput(output_name=\"train\", source=\"/opt/ml/processing/train\"),\n",
+    "        ProcessingOutput(output_name=\"test\", source=\"/opt/ml/processing/test\"),\n",
+    "    ],\n",
+    "    code=\"code/preprocess.py\",\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "from sagemaker.processing import ProcessingInput, ProcessingOutput\n",
@@ -354,16 +431,7 @@
     "\n",
     "step_process = ProcessingStep(\n",
     "    name=\"PreprocessData\",\n",
-    "    processor=sklearn_processor,\n",
-    "    inputs=[\n",
-    "        ProcessingInput(source=input_data, destination=\"/opt/ml/processing/input\"),\n",
-    "    ],\n",
-    "    outputs=[\n",
-    "        ProcessingOutput(output_name=\"scaler_model\", source=\"/opt/ml/processing/scaler_model\"),\n",
-    "        ProcessingOutput(output_name=\"train\", source=\"/opt/ml/processing/train\"),\n",
-    "        ProcessingOutput(output_name=\"test\", source=\"/opt/ml/processing/test\"),\n",
-    "    ],\n",
-    "    code=\"code/preprocess.py\",\n",
+    "    step_args=processor_args,\n",
     ")"
    ]
   },
@@ -377,7 +445,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "%%writefile code/train.py\n",
@@ -482,13 +554,17 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "from sagemaker.tensorflow import TensorFlow\n",
     "from sagemaker.inputs import TrainingInput\n",
     "from sagemaker.workflow.steps import TrainingStep\n",
-    "from sagemaker.workflow.step_collections import RegisterModel\n",
+    "from sagemaker.workflow.model_step import ModelStep\n",
     "import time\n",
     "\n",
     "# Where to store the trained model\n",
@@ -501,7 +577,7 @@
     "tf2_estimator = TensorFlow(\n",
     "    source_dir=\"code\",\n",
     "    entry_point=\"train.py\",\n",
-    "    instance_type=\"ml.m5.xlarge\",\n",
+    "    instance_type=training_instance_type,\n",
     "    instance_count=1,\n",
     "    framework_version=tensorflow_version,\n",
     "    role=role,\n",
@@ -509,13 +585,11 @@
     "    output_path=model_path,\n",
     "    hyperparameters=hyperparameters,\n",
     "    py_version=python_version,\n",
+    "    sagemaker_session=pipeline_session,\n",
     ")\n",
     "\n",
-    "# Use the tf2_estimator in a Sagemaker pipelines ProcessingStep.\n",
     "# NOTE how the input to the training job directly references the output of the previous step.\n",
-    "step_train_model = TrainingStep(\n",
-    "    name=\"TrainTensorflowModel\",\n",
-    "    estimator=tf2_estimator,\n",
+    "train_args = tf2_estimator.fit(\n",
     "    inputs={\n",
     "        \"train\": TrainingInput(\n",
     "            s3_data=step_process.properties.ProcessingOutputConfig.Outputs[\"train\"].S3Output.S3Uri,\n",
@@ -525,17 +599,12 @@
     "            s3_data=step_process.properties.ProcessingOutputConfig.Outputs[\"test\"].S3Output.S3Uri,\n",
     "            content_type=\"text/csv\",\n",
     "        ),\n",
-    "    },\n",
-    ")"
+    "    }\n",
+    ")\n",
+    "\n",
+    "step_train_model = TrainingStep(name=\"TrainTensorflowModel\", step_args=train_args)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -546,7 +615,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "%%writefile code/evaluate.py\n",
@@ -605,14 +678,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "from sagemaker.workflow.properties import PropertyFile\n",
@@ -632,7 +702,8 @@
     "    image_uri=tf_eval_image_uri,\n",
     "    command=[\"python3\"],\n",
     "    instance_count=1,\n",
-    "    instance_type=\"ml.m5.xlarge\",\n",
+    "    instance_type=processing_instance_type,\n",
+    "    sagemaker_session=pipeline_session,\n",
     ")\n",
     "\n",
     "# Create a PropertyFile\n",
@@ -642,10 +713,7 @@
     "    name=\"EvaluationReport\", output_name=\"evaluation\", path=\"evaluation.json\"\n",
     ")\n",
     "\n",
-    "# Use the evaluate_model_processor in a Sagemaker pipelines ProcessingStep.\n",
-    "step_evaluate_model = ProcessingStep(\n",
-    "    name=\"EvaluateModelPerformance\",\n",
-    "    processor=evaluate_model_processor,\n",
+    "eval_args = evaluate_model_processor.run(\n",
     "    inputs=[\n",
     "        ProcessingInput(\n",
     "            source=step_train_model.properties.ModelArtifacts.S3ModelArtifacts,\n",
@@ -660,17 +728,15 @@
     "        ProcessingOutput(output_name=\"evaluation\", source=\"/opt/ml/processing/evaluation\"),\n",
     "    ],\n",
     "    code=\"code/evaluate.py\",\n",
+    ")\n",
+    "\n",
+    "step_evaluate_model = ProcessingStep(\n",
+    "    name=\"EvaluateModelPerformance\",\n",
+    "    step_args=eval_args,\n",
     "    property_files=[evaluation_report],\n",
     ")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -681,7 +747,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "from sagemaker.model import Model\n",
@@ -696,12 +766,11 @@
     "scaler_model = SKLearnModel(\n",
     "    model_data=scaler_model_s3,\n",
     "    role=role,\n",
-    "    sagemaker_session=sagemaker_session,\n",
+    "    sagemaker_session=pipeline_session,\n",
     "    entry_point=\"code/preprocess.py\",\n",
     "    framework_version=sklearn_framework_version,\n",
     ")\n",
     "\n",
-    "\n",
     "tf_model_image_uri = sagemaker.image_uris.retrieve(\n",
     "    framework=\"tensorflow\",\n",
     "    region=region,\n",
@@ -714,19 +783,23 @@
     "tf_model = Model(\n",
     "    image_uri=tf_model_image_uri,\n",
     "    model_data=step_train_model.properties.ModelArtifacts.S3ModelArtifacts,\n",
-    "    sagemaker_session=sagemaker_session,\n",
+    "    sagemaker_session=pipeline_session,\n",
     "    role=role,\n",
     ")\n",
     "\n",
     "pipeline_model = PipelineModel(\n",
-    "    models=[scaler_model, tf_model], role=role, sagemaker_session=sagemaker_session\n",
+    "    models=[scaler_model, tf_model], role=role, sagemaker_session=pipeline_session\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "from sagemaker.model_metrics import MetricsSource, ModelMetrics\n",
@@ -744,9 +817,7 @@
     "    )\n",
     ")\n",
     "\n",
-    "step_register_pipeline_model = RegisterModel(\n",
-    "    name=\"PipelineModel\",\n",
-    "    model=pipeline_model,\n",
+    "register_args = pipeline_model.register(\n",
     "    content_types=[\"text/csv\"],\n",
     "    response_types=[\"text/csv\"],\n",
     "    inference_instances=[\"ml.m5.large\", \"ml.m5.xlarge\"],\n",
@@ -754,6 +825,11 @@
     "    model_package_group_name=model_package_group_name,\n",
     "    model_metrics=model_metrics,\n",
     "    approval_status=model_approval_status,\n",
+    ")\n",
+    "\n",
+    "step_register_pipeline_model = ModelStep(\n",
+    "    name=\"PipelineModel\",\n",
+    "    step_args=register_args,\n",
     ")"
    ]
   },
@@ -767,7 +843,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "from sagemaker.workflow.conditions import ConditionLessThanOrEqualTo\n",
@@ -805,7 +885,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "from sagemaker.workflow.pipeline import Pipeline\n",
@@ -818,6 +902,8 @@
     "pipeline = Pipeline(\n",
     "    name=pipeline_name,\n",
     "    parameters=[\n",
+    "        training_instance_type,\n",
+    "        processing_instance_type,\n",
     "        processing_instance_count,\n",
     "        input_data,\n",
     "        model_approval_status,\n",
@@ -831,7 +917,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "import json\n",
@@ -850,7 +940,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "pipeline.upsert(role_arn=role)"
@@ -859,7 +953,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "execution = pipeline.start()"
@@ -868,26 +966,16 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "execution.wait()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -898,7 +986,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "%%writefile utils.py\n",
@@ -967,7 +1059,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "from utils import get_approved_package\n",
@@ -985,7 +1081,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "from sagemaker import ModelPackage\n",
@@ -1003,7 +1103,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "from sagemaker.predictor import Predictor\n",
@@ -1014,7 +1118,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "data = pd.read_csv(\"data/raw/raw_data_all.csv\")\n",
@@ -1030,7 +1138,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "blue, stop = \"\\033[94m\", \"\\033[0m\"\n",
@@ -1052,7 +1164,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "sm_client = boto3.client(\"sagemaker\")\n",
@@ -1069,7 +1185,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "predictor.delete_endpoint()"
@@ -1078,26 +1198,23 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "pipeline.delete()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "instance_type": "ml.t3.medium",
   "kernelspec": {
-   "display_name": "Python 3 (Data Science)",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:eu-west-1:470317259841:image/datascience-1.0"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1109,9 +1226,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.10"
+   "version": "3.6.14"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/sagemaker-pipelines/tabular/tuning-step/sagemaker-pipelines-tuning-step.ipynb b/sagemaker-pipelines/tabular/tuning-step/sagemaker-pipelines-tuning-step.ipynb
index c279514393..4ea0d6e112 100644
--- a/sagemaker-pipelines/tabular/tuning-step/sagemaker-pipelines-tuning-step.ipynb
+++ b/sagemaker-pipelines/tabular/tuning-step/sagemaker-pipelines-tuning-step.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "#### SageMaker Pipelines Tuning Step\n",
     "\n",
-    "This notebook illustrates how a Hyperparameter Tuning Job can be run as a step in a SageMaker Pipeline. \n",
+    "This notebook illustrates how a Hyperparameter Tuning Job can be run as a step in a SageMaker Pipeline.\n",
     "\n",
     "The steps in this pipeline include -\n",
     "* Preprocessing the abalone dataset\n",
@@ -19,18 +19,26 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "import sys\n",
     "\n",
-    "!{sys.executable} -m pip install \"sagemaker==2.91.1\""
+    "!{sys.executable} -m pip install \"sagemaker>=2.99.0\""
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "import os\n",
@@ -61,18 +69,14 @@
     ")\n",
     "from sagemaker.workflow.pipeline import Pipeline\n",
     "from sagemaker.workflow.properties import PropertyFile\n",
-    "from sagemaker.workflow.steps import (\n",
-    "    ProcessingStep,\n",
-    "    CacheConfig,\n",
-    "    TuningStep,\n",
-    ")\n",
-    "from sagemaker.workflow.step_collections import RegisterModel, CreateModelStep\n",
+    "from sagemaker.workflow.steps import ProcessingStep, CacheConfig, TuningStep\n",
+    "from sagemaker.workflow.model_step import ModelStep\n",
     "from sagemaker.workflow.conditions import ConditionLessThanOrEqualTo\n",
     "from sagemaker.workflow.condition_step import ConditionStep\n",
     "\n",
     "from sagemaker.workflow.functions import Join, JsonGet\n",
     "from sagemaker.workflow.execution_variables import ExecutionVariables\n",
-    "\n",
+    "from sagemaker.workflow.pipeline_context import PipelineSession\n",
     "from sagemaker.tuner import (\n",
     "    ContinuousParameter,\n",
     "    HyperparameterTuner,\n",
@@ -84,7 +88,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "# Create the SageMaker Session\n",
@@ -92,13 +100,20 @@
     "region = sagemaker.Session().boto_region_name\n",
     "sm_client = boto3.client(\"sagemaker\")\n",
     "boto_session = boto3.Session(region_name=region)\n",
-    "sagemaker_session = sagemaker.session.Session(boto_session=boto_session, sagemaker_client=sm_client)"
+    "sagemaker_session = sagemaker.session.Session(boto_session=boto_session, sagemaker_client=sm_client)\n",
+    "\n",
+    "# Create a Pipeline Session\n",
+    "pipeline_session = PipelineSession()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "# Define variables and parameters needed for the Pipeline steps\n",
@@ -131,7 +146,7 @@
    "source": [
     "#### Data Preparation\n",
     "\n",
-    "An SKLearn processor is used to prepare the dataset for the Hyperparameter Tuning job. Using the script `preprocess.py`, the dataset is featurized and split into train, test, and validation datasets. \n",
+    "An SKLearn processor is used to prepare the dataset for the Hyperparameter Tuning job. Using the script `preprocess.py`, the dataset is featurized and split into train, test, and validation datasets.\n",
     "\n",
     "The output of this step is used as the input to the TuningStep"
    ]
@@ -139,10 +154,27 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
-    "%%writefile preprocess.py\n",
+    "!mkdir -p code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%%writefile code/preprocess.py\n",
     "\n",
     "\"\"\"Feature engineers the abalone dataset.\"\"\"\n",
     "import argparse\n",
@@ -272,7 +304,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "# Process the training data step using a python script.\n",
@@ -287,12 +323,11 @@
     "    instance_type=\"ml.m5.xlarge\",\n",
     "    instance_count=processing_instance_count,\n",
     "    base_job_name=f\"{base_job_prefix}/sklearn-abalone-preprocess\",\n",
-    "    sagemaker_session=sagemaker_session,\n",
+    "    sagemaker_session=pipeline_session,\n",
     "    role=role,\n",
     ")\n",
-    "step_process = ProcessingStep(\n",
-    "    name=\"PreprocessAbaloneDataForHPO\",\n",
-    "    processor=sklearn_processor,\n",
+    "\n",
+    "processor_run_args = sklearn_processor.run(\n",
     "    outputs=[\n",
     "        ProcessingOutput(\n",
     "            output_name=\"train\",\n",
@@ -337,8 +372,13 @@
     "            ),\n",
     "        ),\n",
     "    ],\n",
-    "    code=\"preprocess.py\",\n",
-    "    job_arguments=[\"--input-data\", input_data],\n",
+    "    code=\"code/preprocess.py\",\n",
+    "    arguments=[\"--input-data\", input_data],\n",
+    ")\n",
+    "\n",
+    "step_process = ProcessingStep(\n",
+    "    name=\"PreprocessAbaloneDataForHPO\",\n",
+    "    step_args=processor_run_args,\n",
     ")"
    ]
   },
@@ -348,7 +388,7 @@
    "source": [
     "#### Hyperparameter Tuning\n",
     "\n",
-    "Amazon SageMaker automatic model tuning, also known as hyperparameter tuning, finds the best version of a model by running many training jobs on your dataset using the algorithm and ranges of hyperparameters that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by a metric that you choose. \n",
+    "Amazon SageMaker automatic model tuning, also known as hyperparameter tuning, finds the best version of a model by running many training jobs on your dataset using the algorithm and ranges of hyperparameters that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by a metric that you choose.\n",
     "\n",
     "[Valid metrics](https://github.com/dmlc/xgboost/blob/master/doc/parameter.rst#learning-task-parameters) for XGBoost Tuning Job\n",
     "\n",
@@ -358,7 +398,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "# Define the output path for the model artifacts from the Hyperparameter Tuning Job\n",
@@ -378,7 +422,7 @@
     "    instance_count=1,\n",
     "    output_path=model_path,\n",
     "    base_job_name=f\"{base_job_prefix}/abalone-train\",\n",
-    "    sagemaker_session=sagemaker_session,\n",
+    "    sagemaker_session=pipeline_session,\n",
     "    role=role,\n",
     ")\n",
     "\n",
@@ -411,9 +455,7 @@
     "    objective_type=\"Minimize\",\n",
     ")\n",
     "\n",
-    "step_tuning = TuningStep(\n",
-    "    name=\"HPTuning\",\n",
-    "    tuner=tuner_log,\n",
+    "hpo_args = tuner_log.fit(\n",
     "    inputs={\n",
     "        \"train\": TrainingInput(\n",
     "            s3_data=step_process.properties.ProcessingOutputConfig.Outputs[\"train\"].S3Output.S3Uri,\n",
@@ -425,7 +467,12 @@
     "            ].S3Output.S3Uri,\n",
     "            content_type=\"text/csv\",\n",
     "        ),\n",
-    "    },\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "step_tuning = TuningStep(\n",
+    "    name=\"HPTuning\",\n",
+    "    step_args=hpo_args,\n",
     "    cache_config=cache_config,\n",
     ")"
    ]
@@ -440,15 +487,19 @@
     "\n",
     "Find more information on [Warm Starts](https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-warm-start.html) in the SageMaker docs.\n",
     "\n",
-    "In a training pipeline, the parent tuning job name can be provided as a pipeline parameter if there is an already complete Hyperparameter tuning job that should be used as the basis for the warm start. \n",
+    "In a training pipeline, the parent tuning job name can be provided as a pipeline parameter if there is an already complete Hyperparameter tuning job that should be used as the basis for the warm start.\n",
     "\n",
-    "This step is left out of the pipeline steps in this notebook. It can be added into the steps while defining the pipeline and the appropriate parent tuning job should be specified. "
+    "This step is left out of the pipeline steps in this notebook. It can be added into the steps while defining the pipeline and the appropriate parent tuning job should be specified."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "# This is an example to illustrate how a the name of the tuning job from the previous step can be used as the parent tuning job, in practice,\n",
@@ -474,9 +525,7 @@
     "    warm_start_config=warm_start_config,\n",
     ")\n",
     "\n",
-    "step_tuning_warm_start = TuningStep(\n",
-    "    name=\"HPTuningWarmStart\",\n",
-    "    tuner=tuner_log_warm_start,\n",
+    "tuner_run_args = tuner_log_warm_start.fit(\n",
     "    inputs={\n",
     "        \"train\": TrainingInput(\n",
     "            s3_data=step_process.properties.ProcessingOutputConfig.Outputs[\"train\"].S3Output.S3Uri,\n",
@@ -488,7 +537,12 @@
     "            ].S3Output.S3Uri,\n",
     "            content_type=\"text/csv\",\n",
     "        ),\n",
-    "    },\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "step_tuning_warm_start = TuningStep(\n",
+    "    name=\"HPTuningWarmStart\",\n",
+    "    step_args=tuner_run_args,\n",
     "    cache_config=cache_config,\n",
     ")"
    ]
@@ -499,9 +553,9 @@
    "source": [
     "#### Creating and Registering the best models\n",
     "\n",
-    "After successfully completing the Hyperparameter Tuning job. You can either create SageMaker models from the model artifacts created by the training jobs from the TuningStep or register the models into the Model Registry. \n",
+    "After successfully completing the Hyperparameter Tuning job. You can either create SageMaker models from the model artifacts created by the training jobs from the TuningStep or register the models into the Model Registry.\n",
     "\n",
-    "When using the model Registry, if you register multiple models from the TuningStep, they will be registered as versions within the same model package group unless unique model package groups are specified for each RegisterModelStep that is part of the pipeline. \n",
+    "When using the Model Registry, if you register multiple models from the TuningStep, they will be registered as versions within the same model package group unless unique model package groups are specified for each `ModelStep` that is part of the pipeline.\n",
     "\n",
     "In this example, the two best models from the TuningStep are added to the same model package group in the Model Registry as v0 and v1.\n",
     "\n",
@@ -511,38 +565,40 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "# Creating 2 SageMaker Models\n",
-    "\n",
     "model_bucket_key = f\"{default_bucket}/{base_job_prefix}/AbaloneTrain\"\n",
+    "\n",
     "best_model = Model(\n",
     "    image_uri=image_uri,\n",
     "    model_data=step_tuning.get_top_model_s3_uri(top_k=0, s3_bucket=model_bucket_key),\n",
-    "    sagemaker_session=sagemaker_session,\n",
-    "    role=role,\n",
     "    predictor_cls=XGBoostPredictor,\n",
+    "    sagemaker_session=pipeline_session,\n",
+    "    role=role,\n",
     ")\n",
     "\n",
-    "step_create_first = CreateModelStep(\n",
-    "    name=\"CreateTopModel\",\n",
-    "    model=best_model,\n",
-    "    inputs=sagemaker.inputs.CreateModelInput(instance_type=\"ml.m4.large\"),\n",
+    "step_create_first = ModelStep(\n",
+    "    name=\"CreateBestModel\",\n",
+    "    step_args=best_model.create(instance_type=\"ml.m5.xlarge\"),\n",
     ")\n",
     "\n",
     "second_best_model = Model(\n",
     "    image_uri=image_uri,\n",
     "    model_data=step_tuning.get_top_model_s3_uri(top_k=1, s3_bucket=model_bucket_key),\n",
-    "    sagemaker_session=sagemaker_session,\n",
-    "    role=role,\n",
     "    predictor_cls=XGBoostPredictor,\n",
+    "    sagemaker_session=pipeline_session,\n",
+    "    role=role,\n",
     ")\n",
     "\n",
-    "step_create_second = CreateModelStep(\n",
+    "step_create_second = ModelStep(\n",
     "    name=\"CreateSecondBestModel\",\n",
-    "    model=second_best_model,\n",
-    "    inputs=sagemaker.inputs.CreateModelInput(instance_type=\"ml.m4.large\"),\n",
+    "    step_args=second_best_model.create(instance_type=\"ml.m5.xlarge\"),\n",
     ")"
    ]
   },
@@ -558,10 +614,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
-    "%%writefile evaluate.py\n",
+    "%%writefile code/evaluate.py\n",
     "\n",
     "\"\"\"Evaluation script for measuring mean squared error.\"\"\"\n",
     "import json\n",
@@ -623,7 +683,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "# A ProcessingStep is used to evaluate the performance of a selected model from the HPO step. In this case, the top performing model\n",
@@ -635,7 +699,7 @@
     "    instance_type=\"ml.m5.xlarge\",\n",
     "    instance_count=1,\n",
     "    base_job_name=f\"{base_job_prefix}/script-tuning-step-eval\",\n",
-    "    sagemaker_session=sagemaker_session,\n",
+    "    sagemaker_session=pipeline_session,\n",
     "    role=role,\n",
     ")\n",
     "\n",
@@ -645,10 +709,7 @@
     "    path=\"evaluation.json\",\n",
     ")\n",
     "\n",
-    "# This can be extended to evaluate multiple models from the HPO step\n",
-    "step_eval = ProcessingStep(\n",
-    "    name=\"EvaluateTopModel\",\n",
-    "    processor=script_eval,\n",
+    "processor_args = script_eval.run(\n",
     "    inputs=[\n",
     "        ProcessingInput(\n",
     "            source=step_tuning.get_top_model_s3_uri(top_k=0, s3_bucket=model_bucket_key),\n",
@@ -662,7 +723,13 @@
     "    outputs=[\n",
     "        ProcessingOutput(output_name=\"evaluation\", source=\"/opt/ml/processing/evaluation\"),\n",
     "    ],\n",
-    "    code=\"evaluate.py\",\n",
+    "    code=\"code/evaluate.py\",\n",
+    ")\n",
+    "\n",
+    "# This can be extended to evaluate multiple models from the HPO step\n",
+    "step_eval = ProcessingStep(\n",
+    "    name=\"EvaluateTopModel\",\n",
+    "    step_args=processor_args,\n",
     "    property_files=[evaluation_report],\n",
     "    cache_config=cache_config,\n",
     ")\n",
@@ -680,30 +747,38 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "# Register the model in the Model Registry\n",
-    "# Multiple models can be registered into the Model Registry using multiple RegisterModel steps. These models can either be added to the\n",
+    "# Multiple models can be registered into the Model Registry using multiple ModelSteps. These models can either be added to the\n",
     "# same model package group as different versions within the group or the models can be added to different model package groups.\n",
     "\n",
-    "step_register_best = RegisterModel(\n",
-    "    name=\"RegisterBestAbaloneModel\",\n",
-    "    estimator=xgb_train,\n",
-    "    model_data=step_tuning.get_top_model_s3_uri(top_k=0, s3_bucket=model_bucket_key),\n",
+    "register_args = best_model.register(\n",
     "    content_types=[\"text/csv\"],\n",
     "    response_types=[\"text/csv\"],\n",
     "    inference_instances=[\"ml.t2.medium\", \"ml.m5.large\"],\n",
     "    transform_instances=[\"ml.m5.large\"],\n",
     "    model_package_group_name=model_package_group_name,\n",
     "    approval_status=model_approval_status,\n",
-    ")"
+    ")\n",
+    "\n",
+    "\n",
+    "step_register_best = ModelStep(name=\"RegisterBestAbaloneModel\", step_args=register_args)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "# condition step for evaluating model quality and branching execution\n",
@@ -727,7 +802,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "pipeline = Pipeline(\n",
@@ -746,7 +825,7 @@
     "        step_eval,\n",
     "        step_cond,\n",
     "    ],\n",
-    "    sagemaker_session=sagemaker_session,\n",
+    "    sagemaker_session=pipeline_session,\n",
     ")"
    ]
   },
@@ -760,7 +839,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "import json\n",
@@ -772,7 +855,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "pipeline.upsert(role_arn=role)"
@@ -781,7 +868,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "pipeline.start()"
@@ -799,7 +890,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "# # Create a SageMaker client\n",
@@ -822,12 +917,21 @@
  "metadata": {
   "instance_type": "ml.t3.medium",
   "kernelspec": {
-   "display_name": "Python 3.9.4 64-bit ('python@3.9')",
-   "name": "python394jvsc74a57bd0ac2eaa0ea0ebeafcc7822e65e46aa9d4f966f30b695406963e145ea4a91cd4fc"
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
-   "version": ""
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.14"
   },
   "metadata": {
    "interpreter": {
@@ -837,4 +941,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file