Concept Drift Fundamentals
Ketan Sanjay Desale · 2025
In this chapter, we present the basic concepts of concept drift in AI systems. This essentially forms the overview of how changing patterns in data influence model performance and we start with what concept drift is and how it is relevant in dynamic environments wherein AI models must learn to adjust shifts in data distributions over time. The chapter focuses attention on several detection methods for identifying concept drift, specifically statistical tests, machine learning-based approaches and monitoring techniques. Indeed, tracking real-time changes in a competitive business environment is important to keep a model accurate and relevant. These case studies in finance, healthcare and natural language processing showcase this application. These examples are analyzed in the chapter as illustrations of the practical challenges of managing concept drift and the very real implications of not updating AI models when data changes. This foundation, laid here, provides a starting point to explore how drift can be reduced or mitigated further in the following chapters.