Understanding Data Drift and Concept Drift in Machine Learning Systems

Abstract

This comprehensive article examines the critical challenges of data drift and concept drift in machine learning systems deployed across various industries. The article explores how these phenomena affect model performance in production environments, with a particular focus on healthcare, manufacturing, and autonomous systems. The article analyzes different types of drift, including covariate shifts and prior probability shifts, while exploring their manifestations and impacts. Through findings of real-world implementations, the article presents advanced detection methodologies and mitigation strategies, ranging from statistical approaches to sophisticated monitoring frameworks. The investigation extends to emerging technologies in sustainable manufacturing and edge computing environments, offering insights into future developments in drift management. The findings emphasize the importance of proactive drift detection and adaptive model maintenance for ensuring continued system reliability and performance.