1. Serverless AI Model Deployment using AWS Lambda with AWS API Gateway


    Deploying a serverless AI model on AWS involves several key steps. We'll be creating an AWS Lambda function to host our AI model. AWS Lambda allows us to run code without provisioning or managing servers. For our API, we will use AWS API Gateway, which will act as a front door to our Lambda function, managing the HTTP requests and routing them to the appropriate Lambda function.

    We will do the following:

    1. Define an AWS Lambda function, where our AI model code will reside.
    2. Define an API Gateway REST API to trigger the Lambda function.
    3. Grant necessary permissions for API Gateway to invoke the Lambda function.
    4. Create a deployment to enable the REST API endpoints.
    5. Optionally, we can map a custom domain to our REST API for a user-friendly URL.

    Below is the Pulumi program in Python that creates these resources:

    import pulumi import pulumi_aws as aws # Define an AWS Lambda function to deploy the AI Model. ai_model_lambda = aws.lambda_.Function("aiModelLambda", # You can provide your AI model code as a ZIP file that you can upload to AWS Lambda. code=pulumi.FileArchive("./path_to_your_ai_model_code.zip"), # Assign the handler that will be invoked by AWS Lambda, for Python it is usually in the format 'file_name.method_name'. handler="your_handler_file.lambda_handler", # Set the runtime to Python, adjust the version as necessary for your AI model. runtime="python3.8", role=your_lambda_execution_role.arn # Make sure the execution role has permissions for necessary AWS services. ) # Define the AWS API Gateway to trigger the Lambda function. api_gateway = aws.apigatewayv2.Api("apiGateway", protocol_type="HTTP", # "HTTP" or "WEBSOCKET", choose according to your needs. route_selection_expression="$request.method $request.path", # Define how routes are matched. ) # Integrate the AWS Lambda function with AWS API Gateway. integration = aws.apigatewayv2.Integration("integration", api_id=api_gateway.id, integration_type="AWS_PROXY", # Use AWS_PROXY for Lambda function integration. integration_uri=ai_model_lambda.invoke_arn, # The ARN of the Lambda function to integrate with. ) # Define the route for the API Gateway, how HTTP requests trigger the Lambda function. route = aws.apigatewayv2.Route("route", api_id=api_gateway.id, route_key="POST /invoke", # Can be customized to fit your API structure, e.g., "GET /model/predict". target=pulumi.Output.concat("integrations/", integration.id), # Connect the integration with this route. ) # Give necessary permissions for API Gateway to invoke the Lambda function. permission = aws.lambda_.Permission("permission", action="lambda:InvokeFunction", # Permission to invoke the function. principal="apigateway.amazonaws.com", # API Gateway service principal. function=ai_model_lambda.name, source_arn=pulumi.Output.all(api_gateway.execution_arn, route.key).apply( lambda args: f"{args[0]}/authorizers/*" ), ) # Deploy the API Gateway so that it's publicly accessible. deployment = aws.apigatewayv2.Deployment("deployment", api_id=api_gateway.id, # It is recommended to add an explicit dependency on the route, to ensure it is created before deployment. opts=pulumi.ResourceOptions(depends_on=[route]), ) # Create a stage, which is a snapshot of the deployment. This is the public URL end-users can access. stage = aws.apigatewayv2.Stage("stage", api_id=api_gateway.id, deployment_id=deployment.id, name="prod", # The name of the stage, 'prod' is commonly used for production. ) # Optionally, if you want to use a custom domain, you would define it as follows: # domain_name = aws.apigateway.DomainName("domainName", # domain_name="api.mydomain.com", # certificate_arn=your_certificate_arn, # ARN to the SSL certificate for the custom domain. # endpoint_configuration={ # "types": "REGIONAL", # }, # ) # Export the HTTP endpoint of the API Gateway so that you can make requests to it. pulumi.export("invoke_url", pulumi.Output.concat(api_gateway.api_endpoint, stage.name, "/invoke"))

    Ensure you replace placeholders like "./path_to_your_ai_model_code.zip" with the actual path to your Lambda deployment package, the handler with the correct module and method for your Lambda function (e.g., "app.lambda_handler"), and the your_lambda_execution_role with your specific role that has permission to execute Lambda functions.

    This program defines the AWS resources needed to deploy and expose an AI model as a REST API. Pulumi enables you to define these resources in a concise, descriptive, and version-controllable manner. The resources are created in a logical order of dependencies, and the export at the end retrieves the URL endpoint, which you can use to interact with your AI model via HTTP requests.

    Remember that this is a basic setup, and depending on the complexity and requirements of your AI model and its API, you might need to configure additional properties for each resource, add other resources like logging, or refine the access permissions.