Data drift is a change over time in the statistical properties or distribution of input data used by a model. It can make training data less representative of real-world conditions and may reduce model performance unless monitored, investigated, and corrected.
Data drift occurs when the training data no longer is representative of reality. User behavior, data collection, and the data environment can change at any time, and this can lead to a reduction in model performance.
When the statistical properties of distribution of data changes over time, impacting the performance of the model.
A side-by-side comparison of Data Drift and Model Drift. Understand how changes in input-data distribution differ from changes or degradation in model performance over time.
A side-by-side comparison of Data Drift and Concept Drift. Understand whether a model problem comes from changing input distributions or changing relationships between inputs and outcomes.