Drift detection is the process of identifying meaningful changes in data, system behavior, or machine-learning model performance over time. It helps organizations recognize when the conditions under which a model or system was developed no longer match current conditions, potentially reducing accuracy or reliability.
For example, a demand-forecasting model may become less accurate when customer preferences, seasonal patterns, or market conditions change. Drift detection can compare new data and predictions with historical patterns, flag unusual changes, and prompt teams to investigate or retrain the model. It is widely used in machine learning, fraud detection, predictive maintenance, cybersecurity, and analytics. Related concepts include data drift, concept drift, model monitoring, anomaly detection, and model retraining.
