Concept Drift

A phenomenon where the statistical relationship between input data and the target variable changes over time, causing a deployed model's performance to degrade. For example, a fraud detection model trained on pre-pandemic spending patterns may become less accurate as consumer behavior shifts. Detecting and responding to concept drift is a core part of maintaining AI systems in production.
See also: Model Monitoring, batch learning, online learning.