1. Real-time Inference Monitoring using CloudWatch EventRule


    To set up real-time inference monitoring using AWS CloudWatch EventRule, we'll create an Event Rule that triggers when certain inference or prediction thresholds are met or any operational events occur that we're interested in monitoring. This Event Rule can then direct the event information to various targets for further processing or alerting, such as a Lambda function to log or handle the event, an SNS topic for sending notifications, or a Kinesis stream for real-time data processing.

    Here's how you can implement this monitoring using Pulumi in Python:

    1. Create a CloudWatch Event Rule: You define the event pattern or schedule for when you want your rule to trigger. This could be based on the output of your machine learning model, inference latency, error rates, or other operational metrics.

    2. Set up Event Targets: Once the rule is triggered, it can forward the data to specified targets. You might use AWS Lambda for additional processing or invoke an AWS SNS topic to notify stakeholders.

    3. Create necessary IAM Roles: To allow CloudWatch to send events to the targets, you might need to create IAM roles with the necessary permissions.

    Now, let's write some code to create this setup:

    import pulumi import pulumi_aws as aws # Assuming you already have an existing AWS Lambda function for processing the events # retrieve its details as follows: lambda_function = aws.lambda_.Function.get('my_lambda_function', 'existing-lambda-function-name') # Create an IAM role for the Event Rule event_rule_role = aws.iam.Role('event-rule-role', assume_role_policy={ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": { "Service": "events.amazonaws.com" } }] }) # Attach a policy to the role to allow it to invoke the Lambda function policy_attachment = aws.iam.RolePolicyAttachment('event-rule-policy-attachment', role=event_rule_role.name, policy_arn=aws.iam.ManagedPolicy.AWS_LAMBDA_INVOKE_FULL_ACCESS) # Define the CloudWatch Event Rule for real-time inference monitoring event_rule = aws.cloudwatch.EventRule('monitoring-event-rule', event_pattern="""{ "source": ["aws.sagemaker"], "detail-type": ["SageMaker Model Inference"], "detail": { "InferenceAttributes": { "InferenceId": ["my-inference-model-id"] } } }""", role_arn=event_rule_role.arn) # Define the target for the event, such as an AWS Lambda function event_target = aws.cloudwatch.EventTarget('monitoring-event-target', rule=event_rule.name, arn=lambda_function.arn, role_arn=event_rule_role.arn) # Export the rule name and ARN as outputs pulumi.export('event_rule_name', event_rule.name) pulumi.export('event_rule_arn', event_rule.arn)

    In the code above:

    • We first retrieve an existing Lambda function by name, which we assume will process the CloudWatch events.
    • We create an EventRuleRole with a trust relationship policy that allows CloudWatch to assume this role.
    • We then attach a policy that allows this role to invoke Lambda functions.
    • We define a EventRule with a specific event pattern. This pattern depends on the inference results, and you would customize it based on your specific use case (e.g., thresholds you want to monitor).
    • We create an EventTarget that connects the Event Rule to the Lambda function, setting up a real-time link between an inference event and processing action.
    • Finally, we export the event rule's information, such as its name and ARN, for additional references in the stack or externally.

    This Pulumi code sets up a CloudWatch Event Rule to monitor machine learning inferences made by SageMaker. The event pattern filters for specific inference events (SageMaker Model Inference) and targets an AWS Lambda function for processing. With this setup, any time an inference that matches the specified pattern occurs, the rule triggers the lambda function to process the event immediately, facilitating real-time monitoring and potential alerting or intervention.