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Actively monitoring machine learning models during production operationshelps ensure prediction quality and detection and remediation of unexpected orundesired conditions. Monitoring models already deployed in big dataenvironments brings the additional challenges of adding monitoring in parallelto the existing modelling workflow and controlling resource requirements. Inthis paper, we describe (1) a framework for monitoring machine learning models;and, (2) its implementation for a big data supply chain application. We use ourimplementation to study drift in model features, predictions, and performanceon three real data sets. We compare hypothesis test and information theoreticapproaches to drift detection in features and predictions using theKolmogorov-Smirnov distance and Bhattacharyya coefficient. Results showed thatmodel performance was stable over the evaluation period. Features andpredictions showed statistically significant drifts; however, these drifts werenot linked to changes in model performance during the time of our study.

A monitoring framework for deployed machine learning models with supply chain examples