Reliably Detecting Model Failures in Deployment Without Labels
This addresses a critical operational challenge for maintaining reliable machine learning systems in dynamic real-world environments, particularly in high-stakes applications like medicine.
The paper tackles the problem of detecting when machine learning models need retraining due to data distribution shifts in deployment without requiring labels, proposing D3M which achieves low false positive rates under non-deteriorating shifts and provides theoretical guarantees for detecting deteriorating shifts.
The distribution of data changes over time; models operating in dynamic environments need retraining. But knowing when to retrain, without access to labels, is an open challenge since some, but not all shifts degrade model performance. This paper formalizes and addresses the problem of post-deployment deterioration (PDD) monitoring. We propose D3M, a practical and efficient monitoring algorithm based on the disagreement of predictive models, achieving low false positive rates under non-deteriorating shifts and provides sample complexity bounds for high true positive rates under deteriorating shifts. Empirical results on both standard benchmark and a real-world large-scale internal medicine dataset demonstrate the effectiveness of the framework and highlight its viability as an alert mechanism for high-stakes machine learning pipelines.