What is distributed cache coherence in ML classification systems?

Distributed cache coherence ensures consistency of cached features and predictions across multiple nodes.

In large-scale classification systems, caches are distributed across services and regions. Without coherence, different nodes may serve stale or inconsistent data. Cache coherence strategies include invalidation protocols, versioned keys, and event-driven updates. This is critical for maintaining consistency between feature stores, inference services, and downstream systems.