Model Drift

The degradation of an AI model's performance over time as the real world diverges from the data the model was trained on. Data drift means input distributions change; concept drift means the relationship between inputs and outputs changes. A fraud detection model trained on 2023 patterns will miss 2026 fraud techniques. Production AI systems need monitoring, retraining pipelines, and drift detection — the domain of MLOps and model governance.