Model Drift Detection and Automated Retraining in Production ML System

This paper presents DRIFT-ACT (Drift-Aware Continuous Training), a practical and modular framework designed to tackle the persistent challenge of model performance degradation in real-world AI systems. DRIFT-ACT combines real-time drift detection using lightweight statistical monitoring techniques with automated retraining pipelines powered by MLOps tools. Instead of modifying the core model architecture, DRIFT-ACT operates as a thin, flexible layer on top of existing systems—monitoring, detecting, and adapting to shifts in data or concept distributions without human intervention. Tested across synthetic and real-world production scenarios, DRIFT-ACT significantly improves model robustness over time, reduces manual maintenance effort, and ensures sustained performance consistency. Beyond the technical solution, this work serves as a practical blueprint for deploying resilient ML systems in dynamic, high-impact environments.