Data Drift Monitoring

A set of techniques for detecting statistical distribution shifts in input data fed to a deployed machine learning model relative to its training data over time. Common methods include PSI, KS tests, and JS divergence, and are used to indicate when a model may be operating outside the coverage of its training set.