Drift Detection in Robust Machine Learning Systems

Drift detection is critical for long-term machine learning success. As real-world data changes over time, models face performance degradation. Robust ML systems require mechanisms to identify when input data or target patterns shift, enabling timely model retraining. This proactive approach prevents accuracy decline and maintains reliability in production environments.