How to Catch Data Drift When Every Feature Looks Normal

Standard data drift monitoring often fails by only checking individual feature distributions, which can miss crucial shifts in the relationships between features. A more robust method is adversarial validation, which involves training a classifier to distinguish between training data and new production data. If the model can successfully separate the two datasets with an AUC score significantly above 0.5, it signals a drift in their joint distribution. This technique effectively detects subtle, multivariate changes that univariate statistical checks would otherwise miss, preventing quiet degradation of model performance. The process is demonstrated with scikit-learn code for building an `AdversarialDriftDetector`.