1. Edge Caching for Low-Latency AI Model Inferences


    To achieve low-latency AI model inferences with edge caching, you can use cloud services that provide edge locations near your users where your AI models and inference logic can be deployed. This way, inferences are made closer to the user, which reduces latency.

    The example program below demonstrates how to set up an edge caching service using Google Cloud's Network Services Edge Cache. This service allows you to deploy and manage applications at the network edge to reduce latency and improve the performance of your AI inference applications.

    In this program, we are defining an EdgeCacheService which allows your application to serve content from the edge of Google's network, and EdgeCacheOrigin, which represents the origin of your content, like an AI model inference API, that will be cached at the edge.

    Here's how it might look using Pulumi with Python:

    import pulumi import pulumi_gcp as gcp # Define the EdgeCacheOrigin. # This is where your inference service API is located. # For demonstration, we're using a fictitious address and port. edge_cache_origin = gcp.networkservices.EdgeCacheOrigin("my-edge-cache-origin", origin_address="", # Replace with your AI Inference API's address port=80, # Replace with the port your AI Inference API listens on ) # Define the EdgeCacheService. # This service manages the caching of returned inferences close to the users. edge_cache_service = gcp.networkservices.EdgeCacheService("my-edge-cache-service", routing={ "hostRules": [ # You'll specify the host(s) that correspond to your service here. # Users will access your service through these hostnames. { "hosts": ["inference.mycompany.com"], "pathMatcher": "path-matcher-1", }, ], "pathMatchers": [ { "name": "path-matcher-1", "routeRules": [ { "priority": "1", # Traffic intended for these paths will be handled by this rule. # Adjust according to the paths your inference service provides. "matchRules": [ { "prefixMatch": "/v1/models", }, ], # This routeAction will define caching behavior. "routeAction": { "cdnPolicy": { # You may adjust the cache settings depending on how # frequently your AI models update and how dynamic the # content is. For AI inference, you typically want to # customize this for your specific use case. "cacheMode": "CACHE_ALL_STATIC", # Can be changed as per the requirements "defaultTtl": "3600s", # Default time-to-live for cached entries (1 hour). }, }, }, ], }, ], }, edge_cache_origin= edge_cache_origin.id, ) # Export the edge cache origin and service URLs pulumi.export("edge_cache_origin", edge_cache_origin.origin_address) pulumi.export("edge_cache_service", edge_cache_service.name)

    In this brief example:

    • We create an EdgeCacheOrigin which represents your backend service. This service ought to be your AI model's inference endpoint. For the origin_address, use the IP address or fully qualified domain name (FQDN) where the inference services are hosted.

    • We then define an EdgeCacheService that uses the EdgeCacheOrigin we created. Here we set up a basic caching strategy with a defaultTtl which determines the amount of time the response stays in the cache. The hosts and prefixMatch values need to be tailored to match the hosts and path patterns that your inference service uses.

    Remember to replace placeholder values with those that are relevant to your actual service. The TTL, cache mode, and other cache parameters should be set according to your inference service's requirements and how often your AI models change.

    You would deploy this Pulumi program via the command line using the Pulumi CLI, and it's assumed that your Pulumi stack is already set up with the correct GCP project and credentials. Once deployed, the Pulumi CLI will provide outputs for the edge cache origin and service URLs that you can use to integrate with your frontend or client applications that need to make use of the inference service.