Model drift is the degradation or change in model performance over time as real-world data, user behavior, or operating conditions diverge from the data used during training or evaluation. Detecting drift is important for deciding when to retrain, recalibrate, or replace a model.
A side-by-side comparison of Data Drift and Model Drift. Understand how changes in input-data distribution differ from changes or degradation in model performance over time.
A side-by-side comparison of Model Drift and Concept Drift. It explains how performance degradation over time differs from a changing statistical relationship between inputs and target outcomes.