1. Managing Machine Learning Workflow Triggers with AWS SNS


    AWS Simple Notification Service (SNS) is a managed service that provides message delivery from publishers to subscribers (also known as producers and consumers). It enables you to decouple microservices, distributed systems, and serverless applications. In terms of a machine learning (ML) workflow, SNS can be used to trigger processes like data collection, model training, model evaluation, and batch predictions.

    Considering a machine learning workflow trigger, you can use SNS to notify interested parties or systems when an event occurs. For instance, when new data arrives and is stored in an S3 bucket, an event notification can trigger an SNS topic, which in turn can invoke an AWS Lambda function to preprocess the data and start the training process.

    Below is a basic Pulumi program in Python that sets up an SNS topic and a Lambda function. The Lambda function is triggered by messages published to the SNS topic, representing a simple event-driven ML workflow.

    import pulumi import pulumi_aws as aws # Define an SNS topic that will trigger the ML workflow ml_workflow_topic = aws.sns.Topic("mlWorkflowTopic", display_name="ML Workflow Trigger Topic") # Define the IAM role that the Lambda function will assume lambda_role = aws.iam.Role("lambdaRole", assume_role_policy="""{ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Principal": { "Service": "lambda.amazonaws.com" }, "Effect": "Allow", "Sid": "" }] }""") # Attach the AWS Lambda basic execution role policy to the role lambda_execution_policy_attachment = aws.iam.RolePolicyAttachment("lambdaExecutionPolicyAttachment", role=lambda_role.name, policy_arn="arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole") # Define a Lambda function that will handle ML workflow triggers ml_workflow_handler = aws.lambda_.Function("mlWorkflowHandler", code=pulumi.AssetArchive({ ".": pulumi.FileArchive("./lambda") # The directory containing your Lambda code }), role=lambda_role.arn, handler="ml_workflow_handler.handler", # The name of the function handler runtime="python3.8") # The runtime for the Lambda function # Grant the SNS topic permission to invoke the Lambda function sns_invoke_permission = aws.lambda_.Permission("snsInvokePermission", action="lambda:InvokeFunction", function=ml_workflow_handler.name, principal="sns.amazonaws.com", source_arn=ml_workflow_topic.arn) # Subscribe the Lambda function to the SNS topic ml_topic_subscription = aws.sns.TopicSubscription("mlTopicSubscription", topic=ml_workflow_topic.arn, protocol="lambda", endpoint=ml_workflow_handler.arn) # Export the SNS topic ARN and Lambda function ARN so that they can be referenced outside the program pulumi.export("sns_topic_arn", ml_workflow_topic.arn) pulumi.export("lambda_function_arn", ml_workflow_handler.arn)

    In this program:

    • aws.sns.Topic creates a new SNS topic that acts as a central pub/sub channel.
    • aws.iam.Role and aws.iam.RolePolicyAttachment define an IAM role and attach the necessary policy that allows the Lambda function to be executed and to write logs to CloudWatch.
    • aws.lambda_.Function sets up a Lambda function that will handle events. This function’s source code needs to be provided in a directory named lambda, and it should contain a handler function named handler.
    • aws.lambda_.Permission grants SNS permission to invoke the Lambda function.
    • aws.sns.TopicSubscription subscribes the Lambda function to the SNS topic.

    Remember to replace the ./lambda with the path to your Lambda function code and to update the handler and runtime if needed.

    With this setup, every time a message is published to the SNS topic mlWorkflowTopic, the mlWorkflowHandler Lambda function will be invoked, triggering your machine learning workflow. You could further expand this by integrating other AWS services like S3 for data storage or AWS Step Functions for workflow orchestration.