1. S3 Object Creation as an ML Data Ingestion Signal


    To use S3 object creation as a signal for data ingestion in a machine learning workflow, you'll need to complete a few steps:

    1. Create an S3 Bucket: This will be the storage location for your ML data. Whenever a new object is created in this bucket, it can trigger an event.

    2. Create an S3 Bucket Notification: Apply a notification configuration to your S3 bucket so that it publishes events when new objects are created.

    3. Set Up a Lambda Function: This function will serve as an event handler. It will be triggered by the S3 bucket notification and can perform further actions such as data preprocessing or starting an ML job.

    4. Grant Necessary Permissions: Make sure your Lambda function has the required permissions to access S3 and perform necessary actions with the ML service.

    Below is the Pulumi Python program that sets up an AWS S3 bucket with Lambda notifications for new object creation, which can be used as a signal for data ingestion in an ML pipeline:

    import json import pulumi import pulumi_aws as aws # Create an S3 bucket that will store your ML data. ml_data_bucket = aws.s3.Bucket("mlDataBucket") # The Lambda function that will be triggered when a new object is created in the S3 bucket. # Replace the inline code with your specific ML ingestion code. ml_data_ingestion_lambda = aws.lambda_.Function("mlDataIngestionLambda", runtime="python3.8", handler="lambda_function.handler", role=aws.iam.Role("lambdaRole", assume_role_policy=json.dumps({ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": { "Service": "lambda.amazonaws.com", }, }], })).arn, code=pulumi.FileArchive("./lambda_code.zip") ) # Give the Lambda function permission to be invoked by S3. lambda_permission = aws.lambda_.Permission("lambdaPermission", action="lambda:InvokeFunction", function=ml_data_ingestion_lambda.arn, principal="s3.amazonaws.com", source_arn=ml_data_bucket.arn, source_account=pulumi.config.require("aws:accountId") ) # Set up the S3 bucket notification for object creation events, triggering the Lambda function. bucket_notification = aws.s3.BucketNotification("bucketNotification", bucket=ml_data_bucket.id, lambda_functions=[aws.s3.BucketNotificationLambdaFunctionArgs( lambda_function_arn=ml_data_ingestion_lambda.arn, events=["s3:ObjectCreated:*"], )] ) # Export the name of the bucket and the ARN of the Lambda function. pulumi.export('ml_data_bucket_name', ml_data_bucket.id) pulumi.export('ml_data_ingestion_lambda_arn', ml_data_ingestion_lambda.arn)

    Here is a step-by-step explanation of the program:

    1. The mlDataBucket resource is an S3 bucket where your data files will be uploaded.

    2. The mlDataIngestionLambda is an AWS Lambda function that will be triggered on the object creation event in the S3 bucket. Replace the inline code ("./lambda_code.zip") with your actual ML ingestion or preprocessing code.

    3. The lambdaRole is an IAM role with a trust policy that allows Lambda to assume the role. The role should include permissions policies that allow access to necessary AWS services.

    4. The lambdaPermission grants S3 the permission to invoke the Lambda function.

    5. The bucketNotification configures the S3 bucket to send event notifications for object creation, which triggers the specified Lambda function.

    Remember to replace "./lambda_code.zip" with the path to the zip file containing your actual Lambda function code.

    Lastly, export statements at the end of the program are used to output the S3 Bucket name and the Lambda function ARN which can be used in other parts of your infrastructure or for easy reference.

    To proceed, you will need to set up your Pulumi stack and AWS credentials correctly, adjust the Lambda function code to perform your desired ML data ingestion task, and then run pulumi up to deploy the resources defined in the program.