How does distributed inference caching consistency affect bias and variance in global ML systems?

Inconsistent caching across distributed nodes introduces bias through stale predictions and variance through inconsistent responses.

Distributed inference systems often use caching layers to reduce latency and compute costs. However, in multi-region or multi-node deployments, cache inconsistency can arise due to replication delays or invalidation lag.

This leads to bias when stale cached predictions are served after model updates. Variance increases when different nodes return different cached results for identical inputs.

Architectural solutions include centralized cache invalidation, versioned caching keys, and time-aware TTL strategies to maintain consistency across distributed systems.