1. Automated Model Retraining with CloudWatch EventRule


    To create an automated model retraining workflow with AWS services, you will need to set up a CloudWatch Event Rule to trigger the retraining process on a schedule that you specify. In addition to the Event Rule, you will have additional AWS services interacting in the entire workflow, such as AWS SageMaker for model retraining, and AWS Lambda or AWS Step Functions to orchestrate the retraining process.

    Here's how it generally works:

    1. CloudWatch EventRule: This will act as the scheduler that triggers the retraining workflow. You can specify a scheduleExpression for fine-grained control over the timing of your retraining jobs.

    2. CloudWatch EventTarget: This will capture the event from the EventRule and trigger an AWS Lambda function or a Step Functions state machine that will handle the retraining job.

    3. AWS Lambda / AWS Step Functions: These services act as the orchestrators that will initiate the retraining job. Lambda can directly call the SageMaker APIs to start retraining, while Step Functions can manage more complex workflows.

    4. AWS SageMaker: This service is used to retrain your machine learning model. It provides managed environments for running various machine learning frameworks and algorithms.

    5. Notification / Monitoring (optional): After initiating retraining, you can also set up notifications or monitoring, for example, by creating SNS topics to get notified when the retraining job starts or finishes.

    Let's start creating the Pulumi program for setting up the automated retraining with AWS CloudWatch and SageMaker.

    import pulumi import pulumi_aws as aws # Define the CloudWatch Event Rule that triggers on a schedule # Here we're setting a cron job to trigger every day at 6am (UTC) # Detailed information on scheduleExpression syntax is available at: # https://docs.aws.amazon.com/AmazonCloudWatch/latest/events/ScheduledEvents.html event_rule = aws.cloudwatch.EventRule("retraining_scheduler", schedule_expression="cron(0 6 * * ? *)", description="Triggers retraining of the ML model daily at 6am UTC") # Define the Lambda function that this Event Rule will trigger # This example assumes that the Lambda function is defined separately # Replace `your_lambda_function_arn` with the ARN of the Lambda function event_target = aws.cloudwatch.EventTarget("retraining_target", rule=event_rule.name, arn="your_lambda_function_arn") # Assume that the Lambda function triggers SageMaker Training Job through the SDK # No additional pulumi resource is required here, but it's vital the Lambda has the right IAM permissions # (Optional) Define the SNS topic for sending notifications sns_topic = aws.sns.Topic("retraining_notifications") # (Optional) Set subscriptions or alarms for the SNS topic # You might want to add an Email subscription to alert you when the training starts/ends email_subscription = aws.sns.TopicSubscription("retraining_subscription", topic=sns_topic.arn, protocol="email", endpoint="example@email.com") # Replace this with your actual email # Export the names or ARNs of the resources that were created pulumi.export("event_rule_name", event_rule.name) pulumi.export("event_target_id", event_target.id) pulumi.export("sns_topic_arn", sns_topic.arn)

    Explanation of the code:

    • We begin by importing the Pulumi SDK for AWS.
    • We create an EventRule which is a CloudWatch resource. This rule triggers on a schedule defined by a cron expression which you can adjust according to your needs. Here, we have set it to run daily at 6 AM UTC.
    • An EventTarget is associated with the EventRule. The target specifies the ARN of the Lambda function that will execute when the event rule is triggered. Ensure that your Lambda function is capable of initiating the SageMaker training job.
    • We include an optional SNS topic for sending notifications regarding the retraining jobs. With SNS, you can add subscriptions such as emails, SMS, or push notifications.
    • We then export some identifiers of the created resources. These can be useful for referencing in other parts of your infrastructure.

    Remember to replace placeholders like your_lambda_function_arn with the actual ARN of your Lambda function, and example@email.com with your notification email.

    Setting up AWS Lambda and SageMaker requires a more in-depth setup, including permissions and configurations which should be secured and managed ideally within the same Pulumi program or via the AWS console/SDKs. Make sure the Lambda function has the necessary IAM permissions to trigger SageMaker jobs, and also handle any necessary setup/configuration for the machine learning environment.