Handle concept drift in models
Maintain model performance over time
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Design a strategy to detect and handle concept drift in a production [classification/regression] model trained on [describe data]. Implement drift detection using [ADWIN/Page-Hinkley/DDM], define retraining triggers, design a champion-challenger framework, and set up monitoring alerts for performance degradation.
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Pensez à distinguer dérive de données (covariate shift) et dérive conceptuelle (changement P(Y|X)) : ADWIN détecte la seconde, mais couplez-le à un test PSI sur les features pour anticiper. Fixez un seuil de déclenchement sur la performance (ex. AUC ou erreur) plutôt que sur la seule échéance temporelle, et loggez les prédictions pour auditer le champion-challenger.
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