DriftGuard: Detecting Data Drift Early with PSI on Amazon SageMaker

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A developer built DriftGuard, a small MLOps project that uses Python, scikit-learn and the Population Stability Index (PSI) to catch data drift in deployed models. The project trains a model on synthetic data, creates production data with a deliberately shifted feature, and computes PSI scores by comparing binned distributions against the training baseline. PSI results flagged the shifted feature strongly and also marked an unshifted feature as drifting, which the author attributed to the small sample of 100 rows. The author concludes that drift metrics need sufficiently large data windows and should be combined with other statistical tests rather than relying on a single threshold. The full code is published on GitHub.

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