Survival Analysis for Data Drift and ML Reliability

Machine learning model degradation can be treated as a time-to-failure problem using concepts from survival analysis and reliability engineering. This approach moves beyond simple binary monitoring to quantify how long a model remains dependable after deployment. Key tools like the Weibull distribution, survival curves, and hazard functions are used to model the impact of data drift on a model's lifespan. The article demonstrates these methods using a simulated drift-adjusted dataset and a real-world clinical dataset to provide a principled way to manage retraining schedules and long-term maintenance.