1. Event-driven Machine Learning Workflows with AWS Lambda


    To create event-driven Machine Learning (ML) workflows with AWS Lambda, we'll use AWS services that complement Lambda to build an end-to-end solution. This solution involves triggering AWS Lambda functions in response to events, which then interact with machine learning services or workflows. AWS provides services like AWS SageMaker for ML model training and deployment, AWS S3 for storage, and AWS Step Functions for serverless orchestration of workflows.

    Here's an example Python Pulumi program that sets up an event-driven ML workflow:

    1. AWS Lambda Function: This will serve as our event handler, responding to triggers (such as S3 events, API Gateway, etc.) and invoking ML workflows or performing computations.
    2. S3 Bucket: This bucket is used for storing data, such as ML model artifacts or data for processing.
    3. SageMaker Model: We'll assume there's an existing ML model built with SageMaker which our Lambda function can use for inference.
    4. AWS Step Functions: Orchestrate multiple Lambda functions or SageMaker jobs as a workflow, which can be triggered by an event.
    5. IAM Roles and Policies: Lambda functions will need permissions to interact with SageMaker, S3, and other AWS services.

    Below is the Pulumi program:

    import pulumi import pulumi_aws as aws # Lambda function to serve as an event handler for ML workflows ml_lambda_role = aws.iam.Role("mlLambdaRole", assume_role_policy="""{ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": { "Service": "lambda.amazonaws.com" } }] }""") ml_lambda_policy = aws.iam.RolePolicy("mlLambdaPolicy", role=ml_lambda_role.id, policy=pulumi.Output.all(ml_lambda_role.arn).apply(lambda arn: f"""{{ "Version": "2012-10-17", "Statement": [ {{ "Effect": "Allow", "Action": "sagemaker:InvokeEndpoint", "Resource": "*" }}, {{ "Effect": "Allow", "Action": "s3:GetObject", "Resource": "arn:aws:s3:::*/*" }}, {{ "Effect": "Allow", "Action": "logs:CreateLogGroup", "Resource": "arn:aws:logs:*:*:*" }}, {{ "Effect": "Allow", "Action": ["logs:CreateLogStream", "logs:PutLogEvents"], "Resource": "arn:aws:logs:*:*:log-group:/aws/lambda/*" }} ] }}""")) ml_lambda_function = aws.lambda_.Function("mlLambdaFunction", role=ml_lambda_role.arn, runtime="python3.8", handler="lambda_function.handler", code=pulumi.AssetArchive({ ".": pulumi.FileArchive("./lambda") })) # S3 Bucket to store data related to ML ml_data_bucket = aws.s3.Bucket("mlDataBucket") # Lambda function triggered by S3 events ml_processing_lambda = aws.lambda_.Function("mlProcessingLambda", role=ml_lambda_role.arn, runtime="python3.8", handler="lambda_function.handler", code=pulumi.AssetArchive({ ".": pulumi.FileArchive("./processing_lambda") }), environment=aws.lambda_.FunctionEnvironmentArgs( variables={ "SAGEMAKER_ENDPOINT": "your-sagemaker-endpoint", # Replace with your SageMaker Endpoint "S3_BUCKET": ml_data_bucket.bucket, } )) s3_event_source = aws.lambda_.EventSourceMapping("s3EventSource", event_source_arn=ml_data_bucket.arn, function_name=ml_processing_lambda.arn, starting_position="LATEST") # Outputs pulumi.export("ml_lambda_function_name", ml_lambda_function.name) pulumi.export("ml_data_bucket_name", ml_data_bucket.bucket)

    In this program:

    • We first create an IAM role (mlLambdaRole) which our Lambda functions will assume to gain the necessary permissions.
    • The next step is to create an IAM policy (mlLambdaPolicy) that grants the Lambda function permissions to invoke SageMaker endpoints, retrieve objects from S3, and create and write to CloudWatch Logs.
    • We define two Lambda functions (mlLambdaFunction and mlProcessingLambda) using runtime Python 3.8. The code is assumed to be in directories ./lambda and ./processing_lambda. These directories contain the Python code for the Lambda functions.
    • An S3 bucket (mlDataBucket) is created for storing data that our ML workflow will use.
    • An Event Source Mapping (s3EventSource) is created which triggers the mlProcessingLambda when new objects are created in the mlDataBucket.

    Remember to replace "your-sagemaker-endpoint" with your actual SageMaker endpoint. Additionally, be sure that the ./lambda and ./processing_lambda directories contain the Lambda function code and any dependencies.

    Exported outputs (pulumi.export) include the Lambda function's name and the S3 bucket's name, which you can use to interact with as needed or for debugging purposes.

    Before deployment with Pulumi, ensure you have AWS credentials configured and that Pulumi is installed and configured to manage your AWS resources. To deploy this stack, you would navigate to your project directory in your terminal, and then run pulumi up. This will prompt you through the deployment process.