How does real-time feature computation latency affect bias and variance in streaming ML systems?

Latency in feature computation introduces bias due to stale features and increases variance due to inconsistent feature freshness.

In streaming ML systems, features are computed in real time from event streams. If computation is delayed, models may consume outdated features, introducing systematic bias because predictions are based on stale context.

Variance increases when feature freshness is inconsistent across requests, causing similar inputs to produce different outputs depending on timing.

Architectural solutions include stream processing frameworks (Kafka Streams, Flink), event-time processing, and feature materialization layers that ensure consistent feature freshness.