How does feature drift detection relate to bias and variance monitoring in production?

Feature drift increases both bias and variance by shifting input distributions away from training conditions.

Feature drift occurs when the statistical properties of input data change over time. This breaks the assumption that training and inference distributions are identical. As a result, model bias increases because learned patterns no longer match reality, and variance increases because predictions become unstable under shifting inputs.

Monitoring systems use statistical tests like KL divergence, PSI (Population Stability Index), and Wasserstein distance to detect drift. When drift is detected, retraining pipelines are triggered to restore alignment.…