Model drift and decay are concepts that describe the process during which the performance of a model deployed to production degrades on new, unseen data or the underlying assumptions about the data change.
These are important metrics to track once models are deployed to production. Models must be regularly re-trained on new data. This is referred to as refitting the model. This can be done either on a periodic basis, or, in an ideal scenario, retraining can be triggered when the performance of the model degrades below a certain pre-defined threshold.