1. Automated Machine Learning Workflow Triggers with Lambda


    To set up an automated Machine Learning (ML) workflow with triggers using AWS Lambda, you will need:

    1. AWS Lambda Function: To execute your ML code. You can write this function directly in Python, Node.js, or any supported AWS Lambda runtime. The function will be triggered by an event source.

    2. Event Source: Something that will invoke the Lambda function. Event sources can vary based on your workflow - they might include modifications to files in an S3 bucket, updates to a DynamoDB table, or a schedule that runs your Lambda function at regular intervals using Amazon CloudWatch Events.

    3. IAM Role: AWS Lambda needs permissions to access other AWS resources. You create an IAM role with policies that grant the necessary permissions and assign the role to your Lambda function.

    4. Other AWS resources: Depending on your ML workflow, you might need S3 buckets for storing data, an Amazon ECR repository for Docker images, DynamoDB tables for metadata, or Amazon SNS topics for notifications.

    Let’s write a Pulumi program in Python that includes:

    • A Lambda function triggered on a schedule (using CloudWatch Events).
    • All necessary IAM roles and policies.
    • An S3 bucket to store ML workflow data.

    This example assumes you have the AWS CLI configured with sufficient permissions and have the ML code or Docker image ready to be deployed to Lambda.

    import pulumi import pulumi_aws as aws # Create an IAM role that will be used by your Lambda Function lambda_role = aws.iam.Role("lambdaRole", assume_role_policy="""{ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": { "Service": "lambda.amazonaws.com" } }] }""") # Attach the AWS managed LambdaBasicExecutionRole which grants permissions to create Log Streams and Log to CloudWatch policy_attachment = aws.iam.RolePolicyAttachment("lambdaPolicyAttachment", role=lambda_role.id, policy_arn=aws.iam.ManagedPolicy.AWS_LAMBDA_BASIC_EXECUTION_ROLE) # Create an S3 Bucket which could be used to store files or the ML model ml_data_bucket = aws.s3.Bucket("mlDataBucket") # Define the Lambda Function # Here you would include the appropriate handler and runtime based on your ML code # This example uses Python 3.8 ml_lambda_function = aws.lambda_.Function("mlLambdaFunction", role=lambda_role.arn, handler="index.handler", runtime="python3.8", memory_size=512, timeout=60, code=pulumi.FileArchive("./my_ml_lambda_code")) # Create a CloudWatch Event Rule that triggers every day # You can adjust this to be more or less frequent per the needs of your ML workflow daily_schedule = aws.cloudwatch.EventRule("dailySchedule", schedule_expression="rate(1 day)") # Set the Lambda Function as the target of the CloudWatch Event Rule # This will cause the function to be invoked in line with the schedule you've defined event_target = aws.cloudwatch.EventTarget("eventTarget", rule=daily_schedule.name, arn=ml_lambda_function.arn) # Give CloudWatch Events permission to invoke the Lambda Function lambda_permission = aws.lambda_.Permission("lambdaPermission", action="lambda:InvokeFunction", principal="events.amazonaws.com", source_arn=daily_schedule.arn, function=ml_lambda_function.name) # Export the URLs pulumi.export("ml_data_bucket_url", ml_data_bucket.website_endpoint) pulumi.export("ml_lambda_function_name", ml_lambda_function.name)

    In the code above:

    • We created an IAM role and a policy attachment for the AWS Lambda that has the necessary permissions to execute and write logs to CloudWatch.
    • We established an S3 bucket to store any data that our ML workflow generates or requires.
    • We defined a new AWS Lambda function that will handle our ML tasks, specifying the necessary runtime and pointing to a directory with our code (referred to here as ./my_ml_lambda_code). Your actual code should be placed in this directory.
    • We set up a CloudWatch Event Rule to trigger our Lambda on a daily basis.
    • We provided permissions for CloudWatch Events to invoke the Lambda function.

    Don't forget to replace "./my_ml_lambda_code" with the path to your Lambda function's code and ensure the handler matches your configuration (e.g., "index.handler" should correspond to the entry point in your code).

    The pulumi.export statements at the end of the script give you the output of the S3 bucket's URL for the ML data you're storing and the name of the Lambda function, which you can use to monitor or manage the function later on.

    Please adjust the memory size, timeout, and other parameters to match the needs of your ML workload. If your ML model requires more resources, consider using AWS Lambda's container image support to deploy large models that can't be packaged into standard Lambda deployment packages.