# Model Drift & Decay

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](https://machine-learning.paperspace.com/wiki/metrics-in-machine-learning) 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.