What is caching strategy in classification inference systems?

Caching stores previously computed classification results or features to reduce inference latency.

Caching can occur at multiple layers: feature caching, prediction caching, and embedding caching. It reduces repeated computation for identical or similar inputs. However, cache invalidation becomes critical when models or data change. Systems use TTL-based caching or versioned keys to ensure correctness.