1. Custom Machine Learning Model Deployment with AWS API Gateway


    To deploy a custom Machine Learning (ML) model using AWS API Gateway, you would typically have a trained model ready for inference, packaged in a containerized environment such as Docker, and deployed to an AWS service capable of hosting containers such as AWS Lambda or AWS SageMaker.

    The AWS API Gateway serves as the entry point for client applications to send requests to invoke the hosted model and receive predictions. It can handle HTTP/S requests, inspect and modify them as needed, and then route them to the backend service where the ML model is running.

    Below, I will provide you with a Pulumi program written in Python that sets up an AWS API Gateway to act as the interface for a Lambda function, which in turn can be used to run predictions using your ML model. The Lambda function itself is assumed to be a pre-packaged Docker image hosted on AWS Elastic Container Registry (ECR), which contains your model and code needed to perform inference.

    Here's a step-by-step explanation of what we are going to do in the Pulumi program:

    1. Create an AWS ECR repository to store your container image.
    2. Build and push the Docker image containing the ML model to the ECR repository.
    3. Create an AWS Lambda function with the container image from the ECR.
    4. Set up AWS API Gateway to create an HTTP endpoint.
    5. Deploy the API and configure it to route requests to the Lambda function.

    Here is the program that does the steps above:

    import pulumi import pulumi_aws as aws # Step 1: Create an ECR repository to store your container image. ecr_repo = aws.ecr.Repository("ml_model_repo") # Step 2: Assuming the Docker image containing the ML model is ready to be pushed. # This is just a placeholder for the actual command to push your Docker image to ECR, you would typically use a CI/CD system to build and push the Docker image docker_image_name = f"{ecr_repo.repository_url}:latest" # An example command would be: # `docker push {docker_image_name}` # Step 3: Create a Lambda function with the container image from ECR. ml_model_lambda = aws.lambda_.Function("mlModelLambda", package_type="Image", image_uri=docker_image_name, role=some_lambda_role.arn # The ARN for the IAM role with permissions for Lambda to access necessary services like ECR, S3, etc. ) # The Lambda execution role should have policies that allow it to pull images from ECR and log to CloudWatch, you can attach more policies as per your model's needs. # Step 4: Set up API Gateway to create an HTTP endpoint. api_gateway = aws.apigatewayv2.Api("mlModelApi", protocol_type="HTTP" ) # Step 5: Create a route that integrates with the Lambda function. integration = aws.apigatewayv2.Integration("mlModelIntegration", api_id=api_gateway.id, integration_type="AWS_PROXY", # This integration type allows for the incoming request to be passed through to the Lambda function as is. integration_uri=ml_model_lambda.invoke_arn # This is the ARN to invoke the Lambda function. ) # Create a route for POST requests (assuming that model inference uses POST with input data in the request body). route = aws.apigatewayv2.Route("mlModelRoute", api_id=api_gateway.id, route_key="POST /predict", # This route activates for POST requests to the /predict URL. target=pulumi.Output.concat("integrations/", integration.id) # The target for this route is our integration. ) # Deploy the API. deployment = aws.apigatewayv2.Deployment("mlModelDeployment", api_id=api_gateway.id, # `triggers` can auto-deploy the API when changes occur. Here we deploy immediately. triggers={ "redeployment": str(pulumi.Output.all(route.id, integration.id)) # Deploys when the route or integration changes. } ) # Define a stage where the deployment is accessible. stage = aws.apigatewayv2.Stage("mlModelStage", api_id=api_gateway.id, deployment_id=deployment.id, name="prod", # By convention, a production stage. auto_deploy=True, ) # Export the HTTP URL of the API Gateway to access our ML model. pulumi.export("api_url", api_gateway.api_endpoint.apply(lambda endpoint: f"{endpoint}/{stage.name}/predict"))

    In this program:

    • An ECR repository (aws.ecr.Repository) is where Docker images are stored.
    • A Lambda function (aws.lambda_.Function) is where the Docker container will run. Make sure you have the image URI and the correct IAM role with the necessary permissions.
    • An API Gateway (aws.apigatewayv2.Api) is created to handle HTTP traffic. Here I chose HTTP as the protocol type for simplicity; REST is also commonly used.
    • An Integration (aws.apigatewayv2.Integration) is needed for linking the API Gateway route to the Lambda function.
    • A Route (aws.apigatewayv2.Route) is defined for POST requests which could be the type of request used for model prediction.
    • Finally, you deploy (aws.apigatewayv2.Deployment) the API and export the final API URL.

    Please note that the code sample provided assumes you have prepared your ML model inside a Docker image that's compatible with AWS Lambda, and that you have the necessary IAM roles and permissions set up for the Lambda function. You will need to upload your Docker container to the ECR repository separately using a CI/CD pipeline or manually via the command line. Additionally, you might need to adjust the API Gateway for specific resource paths, methods, authorization, or request validation depending on your actual use case for the ML model.