Model Drift
The degradation of an AI model's predictive performance over time as real-world data begins to differ from its training data.
Model Drift (also known as Concept Drift or Data Drift) is a phenomenon where a machine learning model's performance degrades over time because the real-world environment it operates in changes.
Models are trained on historical data. If the statistical properties of the incoming live data begin to shift—due to changing consumer behavior, economic shifts, or new language trends—the model's predictions will become less accurate.
Managing model drift is a critical component of AI operations (MLOps). It requires continuous monitoring, anomaly detection, and periodic retraining or fine-tuning of the model to ensure it remains aligned with current realities.