How does concept drift affect data mining models in production?

Concept drift causes model performance to degrade over time as data distributions change.

In real-world systems, data distributions evolve due to seasonality, user behavior changes, or external events. Models trained on historical data become outdated. Drift can be sudden, gradual, or recurring. Continuous monitoring and retraining pipelines are required to maintain accuracy in production data mining systems.