What Is...
Governance Concept
What Is Model Drift?
Model drift is the degradation of an AI model's performance over time as the world changes and real-world data diverges from the training data. It is inevitable — and without monitoring, invisible until it causes harm.
Types of model drift
Data drift
The statistical distribution of input data changes over time — the model receives inputs it was not trained on.
Concept drift
The underlying relationship between inputs and the correct output changes — the world has changed in ways the model cannot detect.
Upstream data drift
A change in a data pipeline or data source changes the data quality or distribution before it reaches the model.
Regulatory implication
EU AI Act Article 72 requires post-market monitoring for high-risk AI — monitoring for model drift is a compliance obligation, not merely a best practice.