Drift Detection in Robust Machine Learning Systems

Machine learning model performance can degrade over time due to drift, which is a change in the underlying data distribution. The two primary types are data drift, a shift in input feature distributions, and concept drift, a change in the relationship between features and the target variable. Drift can occur suddenly, gradually, or in recurring patterns, and identifying it is critical for maintaining robust systems. A common framework for detection involves comparing new data to a reference set using statistical tests to signal significant changes.