When is AI retraining necessary and how does MLOps prevent performance degradation?

A model should be retrained when the reality it represents changes, for example through new customer behaviour, pricing structures, products, or external conditions. Significantly shifted data distributions or a measurable drop in performance are also clear indicators of retraining needs.

MLOps prevents silent model degradation by continuously monitoring input data and prediction quality. Deviations are detected and documented automatically and can trigger defined processes such as a retraining pipeline. This keeps the model up to date and prevents unnoticed loss of effectiveness.