How does model retraining strategy affect bias-variance tradeoff in production ML systems?

Retraining frequency controls how quickly bias is reduced from drift, but excessive retraining can increase variance and instability.

Retraining strategy is a core architectural decision in production ML systems. If retraining is too infrequent, the model becomes stale and accumulates bias due to data drift. If retraining is too frequent, the model may overfit short-term noise in recent data, increasing variance.

Modern systems use multiple retraining strategies:

Each strategy interacts differently with bias-variance dynamics. Drift-triggered systems are adaptive but can be unstable.…