Ease.ML: A Lifecycle Management System for MLDev and MLOps
Summary: Ease.ML automates the entire MLDev/MLOps lifecycle (an eight-step process) rather than improving isolated stages, driven by practical user scenarios. Unique foundation: a probabilistic-database model with information-theoretic grounding to manage cross-stage uncertainty for end-to-end automation. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Leonel Aguilar (ETH Zurich)
- 2. David Dao (ETH Zurich)
- 3. Shaoduo Gan (ETH Zurich)
- 4. Nezihe Merve Gurel (ETH Zurich)
- 5. Nora Hollenstein (ETH Zurich)
- 6. Jiawei Jiang (ETH Zurich)
- 7. Bojan Karlas (ETH Zurich)
- 8. Thomas Lemmin (ETH Zurich)
- 9. Tian Li (Carnegie Mellon University)
- 10. Yang Li (ETH Zurich; Peking University)
- 11. Susie Rao (ETH Zurich)
- 12. Johannes Rausch (ETH Zurich)
- 13. Cedric Renggli (ETH Zurich)
- 14. Luka Rimanic (ETH Zurich)
- 15. Maurice Weber (ETH Zurich)
- 16. Shuai Zhang (ETH Zurich)
- 17. Zhikuan Zhao (ETH Zurich)
- 18. Kevin Schawinski (Modulos AG)
- 19. Wentao Wu (Microsoft)
- 20. Ce Zhang (ETH Zurich)
BibTeX Citation
@inproceedings{aguilar_cidr21,
address = {Amsterdam, Netherlands},
series = {{CIDR} '21},
title = {{Ease.ML: A Lifecycle Management System for MLDev and MLOps}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Aguilar, Leonel and Dao, David and Gan, Shaoduo and Gurel, Nezihe Merve and Hollenstein, Nora and Jiang, Jiawei and Karlas, Bojan and Lemmin, Thomas and Li, Tian and Li, Yang and Rao, Susie and Rausch, Johannes and Renggli, Cedric and Rimanic, Luka and Weber, Maurice and Zhang, Shuai and Zhao, Zhikuan and Schawinski, Kevin and Wu, Wentao and Zhang, Ce},
year = {2021}
}
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