Abstract

Machine learning (ML) models deployed in production environments often experience performance degradation over time due to shifts in data distributions, changes in feature relationships, and evolving problem domains. These issues, commonly referred to as data drift, concept drift, and feature drift, necessitate systematic monitoring and intervention to maintain model accuracy and reliability.
This paper presents a structured framework for end-to-end model lifecycle management, incorporating drift detection, root cause analysis (RCA), and continuous retraining. Various techniques for detecting data distribution shifts are examined, including statistical methods and performance-based monitoring. Methods for root cause analysis are also discussed, focusing on approaches for identifying sources of degradation in model predictions. Strategies for continuous retraining are outlined, covering both scheduled and adaptive retraining mechanisms to mitigate the effects of drift while ensuring stability across multiple update cycles.
By integrating drift detection, RCA, and retraining into a unified lifecycle management process, this framework provides a systematic approach to maintaining ML models in dynamic environments. The proposed methodology ensures that models remain accurate, interpretable, and robust over extended periods of deployment.