What is model drift compensation strategy in production ML systems?

Model drift compensation adjusts models dynamically to maintain performance under changing data distributions.

Drift compensation goes beyond detection by actively adapting models using techniques like online learning, incremental retraining, reweighting samples, and domain adaptation. It ensures model performance stability even when data distributions shift over time. Systems often combine drift detection with automated retraining pipelines and fallback models for resilience.