When the statistical properties of input data change over time compared to the training data, potentially degrading model performance. Like a recipe not working well when ingredient quality changes.
A shopping recommendation model experiences data drift when customer behavior shifts during holidays, requiring monitoring and potential retraining.
These services offer tools to monitor and manage data drift in machine learning models, ensuring models remain accurate as input data changes over time.