Name
Abstract | ||
|---|---|---|
Deployment of machine learning (ML) algorithms in production for extended periods of time has uncovered new challenges such as monitoring and management of real-time prediction quality of a model in the absence of labels. However, such tracking is imperative to prevent catastrophic business outcomes resulting from incorrect predictions. The scale of these deployments makes manual monitoring prohibitive, making automated techniques to track and raise alerts imperative. We present a framework, ML Health, for tracking potential drops in the predictive performance of ML models in the absence of labels. The framework employs diagnostic methods to generate alerts for further investigation. We develop one such method to monitor potential problems when production data patterns do not match training data distributions. We demonstrate that our method performs better than standard "distance metrics", such as RMSE, KL-Divergence, and Wasserstein at detecting issues with mismatched data sets. Finally, we present a working system that incorporates the ML Health approach to monitor and manage ML deployments within a realistic full production ML lifecycle. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Learning | Journal |
Volume | Citations | PageRank |
abs/1902.02808 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sindhu Ghanta | 1 | 0 | 0.68 |
| Sriram Subramanian | 2 | 0 | 0.34 |
| Lior Khermosh | 3 | 7 | 0.81 |
| Swaminathan Sundararaman | 4 | 213 | 13.56 |
| Harshil Shah | 5 | 4 | 2.43 |
| Yakov Goldberg | 6 | 0 | 0.34 |
| Drew S. Roselli | 7 | 720 | 129.17 |
| Nisha Talagala | 8 | 148 | 10.05 |