Model performance degradation usually happens due to data drift, concept drift, or changing user behavior after deployment.

A common approach is to monitor model metrics and input data distributions continuously. When performance drops below a defined threshold, teams typically trigger model retraining with newer data. Some systems also include drift detection and human review loops to identify issues early.

In practice, treating models as continuously monitored systems rather than one-time deployments is key to maintaining performance.