1. Automated Machine Learning Workflows with SageMaker Pipelines


    Automated Machine Learning (ML) workflows are essential for streamlining the process of building, training, and deploying machine learning models. AWS SageMaker is a fully managed service that enables data scientists and developers to build and train machine learning models, and to deploy them into production more quickly. SageMaker Pipelines is a specific feature of AWS SageMaker that allows you to define, manage, and automate ML workflows.

    To create and manage a SageMaker Pipeline using Pulumi, you would first need to define the steps that your Machine Learning workflow would consist of, such as data preparation, model training, and model deployment. Once you have those steps defined, you can create a pipeline in SageMaker using Pulumi's AWS SDK.

    Below is a Pulumi program that demonstrates how to create a SageMaker Pipeline using the aws.sagemaker.Pipeline resource. This program outlines the basic setup; however, the particulars of your machine learning workflow, such as the actual steps involved, and the specific parameters of each step, would depend on your use case.

    import pulumi import pulumi_aws as aws # Define an IAM role that will be used by SageMaker to perform different operations within the pipeline. # Please make sure to attach the necessary policies to the role that would allow SageMaker to access necessary services like S3, ECR, etc. sagemaker_role = aws.iam.Role("sageMakerRole", assume_role_policy="""{ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": {"Service": "sagemaker.amazonaws.com"} }] }""" ) # Create a SageMaker Pipeline. # The `pipeline_definition` parameter is where you define the steps of your ML workflow. # It is defined in this example as a placeholder object (json-like structure). sagemaker_pipeline = aws.sagemaker.Pipeline("mlWorkflow", pipeline_name="MyMLPipeline", pipeline_description="My Machine Learning Pipeline", pipeline_display_name="MyMLPipeline", role_arn=sagemaker_role.arn, pipeline_definition={ "PipelineDefinitionBody": """ { "Version": "2021-01-01", "Metadata": { "PipelineName": "MyMLPipeline", "PipelineDescription": "This pipeline processes data and trains a ML model." }, "Steps": [ { "Name": "DataPreprocessingStep", "Type": "Processing", # Define other required fields such as # "Arguments": { ... } }, { "Name": "ModelTrainingStep", "Type": "Training", # Define other required fields such as # "Arguments": { ... } }, { "Name": "ModelEvaluationStep", "Type": "Evaluation", # Define other required fields such as # "Arguments": { ... } } ] } """ } ) # After creating the pipeline, you can start an execution using AWS SDKs or AWS CLI. # For example: # aws sagemaker start-pipeline-execution --pipeline-name MyMLPipeline # Export the name of the pipeline pulumi.export('pipeline_name', sagemaker_pipeline.pipeline_name)

    In the code above, we define a Pulumi program that creates a SageMaker Pipeline. We start by creating an IAM role for SageMaker and specifying an assume role policy that allows SageMaker to perform operations on your behalf. After that, we define the SageMaker Pipeline resource, which includes:

    • pipeline_name: A name for the pipeline.
    • pipeline_description: A description of what your pipeline does.
    • pipeline_display_name: A human-readable name for the pipeline.
    • role_arn: The ARN of the role that we created earlier for SageMaker to use.
    • pipeline_definition: A definition of the steps involved in your ML workflow. This definition should be customized to fit your specific ML workflow.

    This program does not actually start the pipeline; it merely sets it up. To start an execution of the pipeline, you would use the AWS SDK or the AWS CLI, as noted in the comment.

    You can find more information about the SageMaker Pipeline resource and its parameters in the Pulumi AWS SDK documentation.

    Remember that you will need to tailor the above program to your specific use case by defining your ML workflow steps through the pipeline_definition. This will typically involve setting up data sources, data processing steps, model definitions, and specifying the necessary hyperparameters for your algorithms.