Data Drift Detection
Production data quality monitoring
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My production model predicts [TARGET_VARIABLE] and I suspect data drift. The training data dates from [TRAINING_PERIOD] and the current data from [CURRENT_PERIOD]. Propose a methodology to detect and quantify drift on the features [FEATURE_LIST] and on the prediction distribution.
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Pour un diagnostic plus fin, quantifie le drift feature par feature avec le PSI (ou le test de Kolmogorov-Smirnov), puis agrège avec une distance globale. Fixe un seuil d’alerte (ex. PSI > 0.1) et identifie les features les plus influentes via l’importance du modèle. N’oublie pas le drift conceptuel : compare l’erreur du modèle sur un échantillon labellisé récent.
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