1. Global CDN Caching for AI Model Serving


    Content Delivery Networks (CDNs) are a key component in delivering content swiftly across the globe. They work by caching content in multiple geographical locations, known as Points of Presence (PoPs). When a user requests content, the CDN delivers it from the PoP closest to the user, reducing latency and load on the origin servers.

    In your case, you might want to distribute AI model predictions, which are relatively static if predictions are precomputed or your models don't change often. This can help you scale to a global audience by reducing the latency of model predictions.

    To serve AI models via a Global CDN, we should :

    1. Host the AI model on a scalable, accessible server.
    2. Cache the model's output or predictions at edge locations using a CDN service.

    Let's take an example with Google Cloud Platform (GCP), where we will use a backend service to host the AI model, and Cloud CDN to cache and serve the data.

    We'll use two primary resources for this:

    • google-native.compute/v1.BackendService: To specify a group of backends that can serve traffic. This backend service will serve as the origin for our CDN, where we'll deploy our AI model. The backend service configuration includes session affinity settings, health checks, and other features to ensure traffic is efficiently managed.
    • google-native.compute/v1.UrlMap: To define the mapping of URLs to backend services. This is where we tell the CDN how to route requests, and where to cache content.

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

    import pulumi import pulumi_google_native.compute.v1 as compute # Instantiate a backend service for our AI model serving. ai_model_backend = compute.BackendService("aiModelBackendService", project="your-google-cloud-project", # Replace with your GCP project ID description="A backend service to serve AI models", backends=[{ 'group': 'path-to-your-instance-group-or-network-endpoint-group' # Define the backend group serving the traffic }], enable_c_d_n=True, # Enable CDN for the backend service health_checks=['path-to-your-health-check'], # Reference to health checks to know the backend service health protocol="HTTP", # The protocol used to communicate with backends. AI models typically use HTTP/HTTPS load_balancing_scheme="EXTERNAL", # Indicates that the backend service will be used with external traffic cdn_policy={ 'cacheMode': "CACHE_ALL_STATIC", # Caching policy which defines the cache behavior } ) # Instantiate a URL map to direct incoming requests to the backend service. url_map = compute.UrlMap("aiModelUrlMap", project="your-google-cloud-project", # Replace with your GCP project ID description="URL map for AI model CDN", default_service=ai_model_backend.self_link, # Link to the backend service ) # Export the URLs of the backend service and URL map for reference. pulumi.export("backend_service_url", ai_model_backend.self_link) pulumi.export("url_map_url", url_map.self_link)

    In this program:

    • We have a backend service aiModelBackendService which is where our AI model is deployed.
    • The backend service is enabled for CDN by setting the enable_c_d_n flag to True, and the cdn_policy determines the caching behavior.
    • We then declare a URL map aiModelUrlMap which uses the backend service as its default service.
    • Finally, we export the URLs for both the backend service and URL map for easy reference.

    This basic setup will create infrastructure on GCP that you can use to serve your AI models globally with low latency. To complete this, you'll need to have your AI model served on an instance or a container, and the instance or container group must be specified in the backend service configuration. The health checks should also be appropriately set up to ensure the backend service remains healthy.

    You will also need to handle cache invalidation and updating the CDN cache when your AI model updates or when new predictions are made. This is outside the scope of the above script but is important to consider when serving dynamic content such as AI model predictions.