Data Drift
A change in the statistical properties of the input data over time compared to the data the model was trained on. When data drifts, model predictions become less reliable.
Why It Matters
Data drift is the #1 reason production models degrade. Detecting it early prevents months of poor predictions before anyone notices.
Example
A credit scoring model trained on pre-pandemic data encountering post-pandemic spending patterns — remote work, less travel, more online shopping — that differ from training data.
Think of it like...
Like using a map from 10 years ago to navigate a city where new roads have been built and old ones closed — the guidance becomes less reliable as the world changes.
Related Terms
Concept Drift
A change in the underlying relationship between inputs and outputs over time. Unlike data drift, concept drift means the rules of the game have changed, not just the distribution of inputs.
Model Monitoring
The practice of continuously tracking an ML model's performance, predictions, and input data in production to detect degradation, drift, or anomalies after deployment.
Model Drift
The gradual degradation of a model's predictive performance over time as the real-world environment changes. Model drift can be caused by data drift, concept drift, or both.
Retraining
The process of training a model again on updated data to restore or improve its performance. Retraining addresses model drift and incorporates new patterns the original model did not learn.