1. Serverless AI Feature Extraction with AWS OAM


    To implement serverless AI feature extraction with AWS, we can use a combination of AWS services. One core service will be AWS SageMaker for machine learning model training and deployment. Then, we'll use AWS Lambda functions to run our serverless code triggered by events or HTTP requests. AWS Step Functions can orchestrate the workflow between these services.

    To achieve serverless feature extraction, you can create a pipeline that:

    1. Receives input data.
    2. Passes the data to a feature extraction model in SageMaker.
    3. Stores the extracted features for future use, possibly in an Amazon S3 bucket.

    Here's how you might set up such a pipeline using Pulumi:

    import pulumi import pulumi_aws as aws # Create an IAM role that AWS SageMaker can assume sagemaker_role = aws.iam.Role("sagemakerRole", assume_role_policy=json.dumps({ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": { "Service": "sagemaker.amazonaws.com" }, }], }) ) # Attach policies to the role for the necessary permissions sagemaker_policy_attachment = aws.iam.RolePolicyAttachment("sagemakerPolicyAttachment", role=sagemaker_role.name, policy_arn=aws.iam.ManagedPolicies.AmazonSageMakerFullAccess.value ) # Define an AWS Lambda function that triggers SageMaker for feature extraction feature_extraction_lambda = aws.lambda_.Function("featureExtractionLambda", runtime=aws.lambda_.Runtime.PYTHON_3_8.value, role=sagemaker_role.arn, handler="index.handler", # Assumes a handler function defined in index.py code=pulumi.FileArchive("./function.zip") # Assumes you have a .zip file with your function code ) # Define a Step Function (state machine) to orchestrate the feature extraction process state_machine_definition = """ { "Comment": "A state machine that orchestrates the serverless AI feature extraction.", "StartAt": "TriggerFeatureExtraction", "States": { "TriggerFeatureExtraction": { "Type": "Task", "Resource": "${feature_extraction_lambda.arn}", "End": true } } } """ feature_extraction_state_machine = aws.sfn.StateMachine("featureExtractionStateMachine", role_arn=sagemaker_role.arn, definition=state_machine_definition ) # Export the ARN of the Lambda function and the State Machine to access it in other place pulumi.export("lambda_arn", feature_extraction_lambda.arn) pulumi.export("state_machine_arn", feature_extraction_state_machine.id)

    This Pulumi program creates an AWS IAM role with the AmazonSageMakerFullAccess policy attached, an AWS Lambda function for invoking the feature extraction, and an AWS Step Functions state machine to orchestrate the feature extraction process.


    1. aws.iam.Role: This resource is an IAM Role that will be assumed by SageMaker. This role will need the necessary permissions to interact with other AWS services that are part of the pipeline.

    2. aws.iam.RolePolicyAttachment: This attaches a policy to the role that grants SageMaker full access to its services. This is required for SageMaker to operate correctly.

    3. aws.lambda_.Function: Here we define an AWS Lambda function. The runtime specifies the programming language (Python 3.8) and code is a zipped file containing the Lambda function.

    4. aws.sfn.StateMachine: We define a Step Functions state machine. The definition is a JSON string that outlines the state machine, which in this case, has a single task state that triggers the Lambda function responsible for feature extraction.

    What's Next?

    • Lambda Function Code: You'd need to include the Python code that the Lambda would run within index.py in the zipped file. This code would leverage the AWS SDK to interact with SageMaker for the actual feature extraction.
    • SageMaker Model: The Lambda function code would need to reference a previously trained SageMaker model designed for feature extraction.
    • State Machine Definition: The state machine here is simple for clarity, but it could be extended to handle different states, inputs, and outputs as required by your feature extraction logic and workflow.

    This setup will get you started on serverless AI feature extraction using AWS and Pulumi. As you build out your feature extraction pipeline, you may need to adjust roles, policies, and AWS service configurations to fit your specific use case.