Data Drift
A change in the statistical properties or distribution of input data over time that can reduce the accuracy of machine learning models.
What is Data Drift?
Data drift occurs when the data a model encounters in production begins to differ from the data used during training. These changes may result from evolving user behavior, market conditions, seasonal patterns, new data sources, or changes in the operating environment. As input patterns shift, the model may become less accurate because its learned relationships no longer reflect current data.
Why is Data Drift Important?
A model that performs well at deployment may become less reliable as its input data changes. Monitoring data drift helps teams identify these changes early, investigate potential performance degradation, and determine whether models need to be updated, retrained, or recalibrated.
Common use cases
Data drift monitoring is commonly used in fraud detection, credit scoring, recommendation systems, forecasting, healthcare AI, and production machine learning systems.