What is Data Drift in Machine Learning

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: 

• Concept drift in machine learning 101 
• 8 Concept Drift Detection Methods.