Data drift is a change in model input data that leads to model performance degradation. Monitoring data drift helps detect these model performance issues.
Causes of data drift include:
- Upstream process changes, such as a sensor being replaced that changes the units of measurement from inches to centimeters.
- Data quality issues, such as a broken sensor always reading 0.
- Natural drift in the data, such as mean temperature changing with the seasons.
- Change in relation between features, or covariate shift.
If interested, learn and read more about these concepts in our articles: