Continuous Drift Detection for Production Machine Learning Pipelines

Abstract

Machine learning models degrade silently as production data diverges from training distributions. We present a lightweight, streaming drift-detection framework that monitors feature and prediction distributions in real time and triggers automated retraining. Evaluated across three industrial datasets, the approach reduced undetected performance regressions by 71% while adding under 4% inference overhead, offering a practical path to reliable MLOps at scale.

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