1. API Gateway Stage Variables for AI Model Versions


    In AWS API Gateway, stage variables can be used to manage different configurations across your deployment stages (for example, dev, staging, and production). They are key-value pairs that can be accessed within your API Gateway setup, enabling you to customize behavior for different versions of your API, such as directing traffic to different backend endpoints based on the stage.

    To set up stage variables in an API Gateway for different AI model versions, you can define a stage for each model version and then assign stage variables accordingly. This can help in scenarios where you might have several versions of an AI model and you want to dynamically point to the correct model version without making code changes. This is especially useful in blue/green deployment scenarios or in A/B testing.

    Below, I'll provide you with a Pulumi program that creates an AWS API Gateway (v2) with two stages for two different versions of an AI model. Each stage will have corresponding stage variables to point to the relevant AI model endpoint.

    import pulumi import pulumi_aws as aws # Create an API object to hold your resources api = aws.apigatewayv2.Api("myApi", protocol_type="HTTP", # Define the protocol type (HTTP or WEBSOCKET) route_selection_expression="$request.method $request.path", ) # Specifying the actual deployment of the gateway. # This might refer to a configuration that includes routes, integration, etc. deployment = aws.apigatewayv2.Deployment("myApiDeployment", api_id=api.id, # Deployment descriptions allow you to capture the current state/config of the deployment. description="Initial deployment", ) # Creating a stage for AI Model v1 stage_v1 = aws.apigatewayv2.Stage("myApiStageV1", api_id=api.id, name="v1", deployment_id=deployment.id, # Using the 'stage_variables' to setup variables that can be used in the integration transforms and routing stage_variables={ "modelVersion": "v1", "modelEndpoint": "https://example.com/ai/v1/inference", # An example endpoint }, auto_deploy=True, ) # Creating a stage for AI Model v2 stage_v2 = aws.apigatewayv2.Stage("myApiStageV2", api_id=api.id, name="v2", deployment_id=deployment.id, stage_variables={ "modelVersion": "v2", "modelEndpoint": "https://example.com/ai/v2/inference", # An example endpoint }, auto_deploy=True, ) # Exporting the URLs of the stages so they can be accessed pulumi.export('stage_v1_invoke_url', stage_v1.invoke_url) pulumi.export('stage_v2_invoke_url', stage_v2.invoke_url)

    In this program, we define an HTTP API and deploy it with initial settings. We then create two stages, one for each version of our AI model. For each stage, we set different stage variables to point to the respective versioned endpoints of the AI model. The key modelVersion can be used in the API Gateway's route and integration logic to determine the model version, and modelEndpoint is the URL where the specified model version inference service is running.

    When API requests are made to the respective stage URLs, the API gateway can route the requests to different backend services according to the stage's variables.

    Pulumi will manage the state and underlying resources, so changes in configuration can be easily applied and version-controlled through Pulumi's infrastructure as code approach.

    Remember to adjust the example endpoints to the actual endpoints where your AI model versions are deployed. If you have more settings specific to your use case, such as configuring logging or authorizers, you can add them in a similar manner.