What is tail latency optimization in ML serving systems?

Tail latency optimization reduces worst-case response times in ML systems.

Tail latency is influenced by queueing delays, GC pauses, network jitter, and resource contention. Techniques include request hedging, caching, load balancing, and isolation of noisy neighbors. It is critical for user-facing ML APIs.