1. Serverless ML Model Hosting using AWS API Gateway

    Python

    To host a serverless Machine Learning (ML) model using AWS API Gateway, you would typically have an ML model hosted as an AWS Lambda function that can run your inference code. The API Gateway acts as the front door to your Lambda function, allowing clients to invoke your ML model over HTTPS.

    Below, we will go through the steps of creating an API Gateway and integrating it with an AWS Lambda function, which would host our ML model. The Lambda function will be triggered whenever the API Gateway receives a request. For simplicity, we're not including the actual ML model or Lambda function code, but assuming you have a Lambda function ready to be invoked.

    Here's the step-by-step breakdown:

    1. Create an AWS Lambda function with the appropriate runtime (e.g., Python for a Python-based ML model). The function includes the inference code for your ML model. Make sure the function has an appropriate IAM role with permissions to be invoked by API Gateway.

    2. Create an API Gateway instance. You can choose to create a REST or HTTP API depending on your needs. In this case, we'll assume a REST API, which offers more features like different types of API authorizers.

    3. Create a new API Gateway resource that represents the endpoint for your ML model. This is the URL path clients will access.

    4. Create a new API Gateway method for your resource, typically a POST method to send inference data to the ML model.

    5. Create an AWS Lambda integration so that when the API Gateway method is called, it triggers your Lambda function.

    6. Deploy your API to make it publicly accessible. This involves creating a new deployment and a stage (e.g., 'prod' for production).

    7. Finally, export the URL of your API stage so you can access it.

    Below is the Pulumi program written in Python that sets up this infrastructure:

    import pulumi import pulumi_aws as aws # Assume we already have a Lambda function defined for the ML model ml_model_lambda = aws.lambda_.Function("mlModelLambda", # ... Lambda configuration including the runtime, handler, role, and source code ... ) # Create an AWS API Gateway REST API to expose the Lambda function api = aws.apigateway.RestApi("mlModelApi", description="API for ML model hosting") # Create a resource representing the endpoint resource = aws.apigateway.Resource("mlModelResource", parent_id=api.root_resource_id, path_part="model", # The URL path for accessing the ML model rest_api=api) # Create a method for the resource. Here we use POST to send inference data. method = aws.apigateway.Method("mlModelMethod", http_method="POST", authorization="NONE", # Use an appropriate authorization strategy resource_id=resource.id, rest_api=api, request_parameters={ "method.request.header.Content-Type": True }) # Integrate the method with the Lambda function integration = aws.apigateway.Integration("mlModelIntegration", http_method=method.http_method, resource_id=resource.id, rest_api=api, integration_http_method="POST", type="AWS_PROXY", # Use AWS_PROXY for Lambda proxy integration uri=ml_model_lambda.invoke_arn) # Deploy the API deployment = aws.apigateway.Deployment("mlModelDeployment", rest_api=api, stage_name="prod", # Create a 'prod' stage for production depends_on=[method]) # Ensure the method is set up before deploying # Expose the URL of the deployed API invoke_url = pulumi.Output.concat("https://", api.id, ".execute-api.", aws.config.region, ".amazonaws.com/prod") pulumi.export("invoke_url", invoke_url)

    This program does the following:

    • Defines a Lambda resource (omitted configuration for brevity) to host the ML inference code.
    • Constructs an API Gateway REST API to serve as the front door for requests.
    • Sets up an API endpoint at the path /model to receive inference requests.
    • Configures a POST method allowing clients to submit inference data.
    • Creates an integration between the API method and the Lambda function using the AWS_PROXY type for streamlined request-response flow.
    • Deploys the API Gateway, making it publicly accessible on the 'prod' stage.
    • Exports the URL endpoint of the API so you can start performing inferences.

    Remember to replace the placeholder comments in the Lambda function configuration with actual values, and also ensure the Lambda function is provided with suitable IAM permissions.


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