Data Drift Detection

From The Foundation for Best Practices in Machine Learning

Data Drift Detection


Define and deploy monitoring metrics and thresholds for detecting sudden and/or gradual, short term and/or long term changes in data distributions, giving priority to those that can detect past observed changes. (See Section 12.2.1. - Missing and Bad Data Handling for further information). Document and assess distribution families, statistical moments, similarity measures, trends and seasonalities.


To (a) prevent predictions from diverging from training data and/or Product Definitions by assessing whether production data is representative of older data; and (b) highlight associated risks that might occur in the Product Lifecycle.

Additional Information