1. Automated ML Model Retraining Workflows with AWS Step Functions


    To create an automated ML (Machine Learning) model retraining workflow using AWS Step Functions, we'll utilize multiple AWS services including Step Functions, SageMaker, and Lambda, among others. AWS Step Functions allows you to coordinate multiple AWS services into serverless workflows. SageMaker is a fully managed service that provides the ability to build, train, and deploy machine learning models at scale.

    Here's a high-level overview of what you need to accomplish this:

    1. SageMaker Training Jobs: Create SageMaker training jobs to retrain your ML models. You will define the parameters and supply the data sources for the training process.

    2. Step Functions State Machine: Define a state machine in AWS Step Functions that controls the workflow of the retraining process. This would involve steps like initiating a training job, checking the status of the job, and possibly a decision state that determines whether to redeploy the model based on the new training results.

    3. Lambda Functions (optional): You may need AWS Lambda functions to perform tasks such as data preprocessing before training or to deploy the model after retraining.

    Now let's put together a simple Pulumi program that outlines these resources in Python. Please note that this example will focus on setting up the infrastructure and does not include the actual model training logic or Lambda function code, which would be specific to your ML model and use case.

    import pulumi import pulumi_aws as aws # Define the IAM role for the Step Functions state machine state_machine_role = aws.iam.Role("stateMachineRole", assume_role_policy="""{ "Version": "2012-10-17", "Statement": [ { "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": { "Service": "states.amazonaws.com" } } ] }""" ) # Attach necessary policies to the IAM role state_machine_policy_attach = aws.iam.RolePolicyAttachment("stateMachinePolicyAttach", role=state_machine_role.name, policy_arn=aws.iam.ManagedPolicy.AWS_STEP_FUNCTIONS_FULL_ACCESS, ) # Define the Step Functions state machine # The definition here is a placeholder; you should replace it with your actual state machine definition state_machine = aws.sfn.StateMachine("stateMachine", role_arn=state_machine_role.arn, definition="""{ "Comment": "A placeholder state machine that represents the ML retraining workflow.", "StartAt": "PlaceholderState", "States": { "PlaceholderState": { "Type": "Pass", "Result": "This is a placeholder", "End": true } } }""" ) # Output the ARN of the state machine to be used in the application or for other AWS resources pulumi.export('state_machine_arn', state_machine.id)

    In this program, we:

    • Created an IAM role that the Step Functions state machine will assume. This role needs trust relationships that allow the Step Functions service to assume the role.
    • Attached a managed policy to the role which gives the state machine full access to AWS Step Functions.
    • Created a placeholder Step Functions state machine. In a real-world scenario, the definition field would contain the JSON definition of the state machine, which outlines the states that your model retraining workflow will go through.

    To complete the setup for your specific use case, you would need to flesh out the definition of the aws.sfn.StateMachine resource to include all the necessary states the workflow would need.

    Remember to replace the placeholders and stubs with actual logic applicable to your ML training and deployment processes. The resources above lay down the foundational infrastructure for an ML retraining workflow. The detailed definition of the state machine, data preprocessing, model training, and deployment logic will vary based on the ML framework, model, and business requirements you have.