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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: 

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