Model Drift

Category: science

The decay of a model’s predictive accuracy over time due to changes in real-world data.

The world changes; your data should too. A model built on 2024 economic data will fail in 2026. Drift is the "hidden expiration date" of AI. If you don’t re-train regularly, the model becomes a liability as its accuracy quietly slips away.

Common Examples

• We detected significant model drift in our fraud detection system, likely due to shifts in customer behavior since the last update.
• Automated monitoring for model drift is essential for any production environment where performance decay can lead to direct revenue loss.