1. Automating ML Workflow with S3 Events


    To automate a machine learning (ML) workflow using Amazon S3 events, we will use Pulumi to define the infrastructure as code. The workflow will involve creating an Amazon S3 bucket to store our data, a Lambda function to process the data when it gets uploaded to S3, and an Amazon SageMaker pipeline for the ML workflow.

    Here's an outline of the steps we will take:

    1. Create an S3 bucket: This will be the location where we upload our datasets or any data needed for the ML workflow.

    2. Set up a Lambda Function: This function will be triggered by S3 events (e.g., when new data is uploaded to the S3 bucket). It will perform initial processing and invoke the SageMaker pipeline.

    3. Define a SageMaker Pipeline: SageMaker Pipelines help you define, automate, and manage end-to-end ML workflows. Once the data is pre-processed by the Lambda function, the SageMaker Pipeline can take care of training, evaluating, and deploying the ML model.

    4. Set up S3 Event Notification: We'll configure the S3 bucket to send an event notification to our Lambda function whenever new data is uploaded.

    Below is a Pulumi Python program that creates the necessary AWS resources to automate an ML workflow using S3 events. The lambda function provided here is a placeholder; you should replace the lambda_handler code with the actual logic required for pre-processing and triggering the SageMaker pipeline.

    import pulumi import pulumi_aws as aws # Step 1: Create an S3 bucket to store data for ML workflows ml_data_bucket = aws.s3.Bucket("mlDataBucket") # Step 2: Define an AWS Lambda function that will process S3 events # Replace the inline code with your actual processing logic s3_event_processor = aws.lambda_.Function("s3EventProcessor", runtime=aws.lambda_.Runtime.PYTHON3_8, code=pulumi.AssetArchive({ '.': pulumi.FileArchive('./path_to_your_lambda_code_directory') }), handler="lambda_handler.handler", # Assume that 'handler' is defined in 'lambda_handler.py' file within the specified directory role=s3_event_processor_role.arn ) # Make sure to create the Lambda execution role and attach the AWSLambdaExecute policy s3_event_processor_role = aws.iam.Role("s3EventProcessorRole", assume_role_policy=json.dumps({ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": { "Service": "lambda.amazonaws.com" }, }] }) ) s3_event_processor_policy_attachment = aws.iam.RolePolicyAttachment("s3EventProcessorPolicyAttachment", role=s3_event_processor_role.name, policy_arn="arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole" ) # Step 3: Define a SageMaker Pipeline for your ML workflow # Note: Replace the 'pipeline_definition' with your actual SageMaker pipeline definition sagemaker_pipeline = aws.sagemaker.Pipeline("myMlPipeline", role_arn=sagemaker_execution_role.arn, # The ARN of the IAM role to be used by the SageMaker pipeline pipeline_name="MyMlWorkflowPipeline", pipeline_definition={ # Here you would define the actual steps of your ML workflow "Version": "1", "Metadata": {}, "PipelineDefinition": [...] # Your pipeline definition goes here } ) # Define the IAM role for SageMaker execution sagemaker_execution_role = aws.iam.Role("sagemakerExecutionRole", assume_role_policy=json.dumps({ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": { "Service": "sagemaker.amazonaws.com" }, }] }) ) # Attach necessary policies to the SageMaker execution role # Note: You need to attach more policies that allow access to specific services and actions your pipeline needs aws.iam.RolePolicyAttachment("sagemakerExecutionPolicyAttachment", role=sagemaker_execution_role.name, policy_arn="arn:aws:iam::aws:policy/service-role/AmazonSageMakerFullAccess" ) # Step 4: Set up S3 Event to trigger the Lambda function on file upload bucket_notification = aws.s3.BucketNotification("bucketNotification", bucket=ml_data_bucket.id, lambda_functions=[aws.s3.BucketNotificationLambdaFunctionArgs( lambda_function_arn=s3_event_processor.arn, events=["s3:ObjectCreated:*"], filter_prefix="data/", # Assuming we only care about objects in the 'data/' prefix )] ) # Export the names of the resources pulumi.export("bucket_name", ml_data_bucket.id) pulumi.export("lambda_function_name", s3_event_processor.name) pulumi.export("sagemaker_pipeline_name", sagemaker_pipeline.pipeline_name)

    This is a simplified version of what an ML workflow automation might look like. In practice, more details are required, such as specifying exact permissions for IAM roles, the actual processing code in the Lambda function, and the specifics of the SageMaker pipeline definition.

    Make sure to test this in a test environment and validate each step of the ML workflow before moving to production. Adjust your pipeline definition, Lambda function code, IAM policies, and any other AWS services according to your specific workflow requirements.