Real-Time Detection, Modeling, and Adaptive Retraining for Degrading Machine Learning Systems: A Systematic Review and Operational Architecture

Machine learning (ML) models operating in non-stationary real-world environments suffer from performance degradation due to temporal distribution shifts. This degradation—manifested as covariate shift, prior probability shift, and real concept drift—causessilent model failure, resulting in severe economic loss or unexpected hardware failure. This paper presents a systematic review and operational engineering framework for real-time model degradation measurement, quantitative drift modeling, and automated closed-loop retraining. We formalize the mathematical taxonomy of drift mechanisms, evaluate statistical and information-theoretic detection metrics (including Kolmogorov-Smirnov statistics, Population Stability Index, and Wasserstein distance), and detail an asynchronous streaming MLOps architecture. We examine two end-to-end industrial use cases: an Enterprise Lead Scoring model subject to severe label latency, and an Industrial Predictive Maintenance (PdM) prognostic system monitoring continuous mechanical vibrations. Furthermore, we include interactive browser-renderable vector graphics (via HTML/SVG blocks) illustrating real-time drift transitions, comparative benchmark matrices, cost-benefit models, open research challenges, and 12 formal academic references.