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Ease.ML: A Lifecycle Management System for Machine Learning
Abstract
We present Ease.ML, a lifecycle management system for machine learning (ML). Unlike many existing works, which focus on improving individual steps during the lifecycle of ML application development, Ease.ML focuses on managing and automating the entire lifecycle itself. We present user scenarios that have motivated the development of Ease.ML, the eight-step Ease.ML process that covers the lifecycle of ML application development; the foundation of Ease.ML in terms of a probabilistic database model and its connection to information theory; and our lessons learned, which hopefully can inspire future research.
Citation
@inproceedings{cidr/2021/cidr2021_paper26,
author = {Leonel Aguilar and
David Dao and
Shaoduo Gan and
Nezihe Merve Gurel and
Nora Hollenstein and
Jiawei Jiang and
Bojan Karlas and
Thomas Lemmin and
Tian Li and
Yang Li and
Susie Rao and
Johannes Rausch and
Cedric Renggli and
Luka Rimanic and
Maurice Weber and
Shuai Zhang and
Zhikuan Zhao and
Kevin Schawinski and
Wentao Wu and
Ce Zhang},
title = {Ease.ML: A Lifecycle Management System for Machine Learning},
booktitle = {Proceedings of the 11th Conference on Innovative Data Systems Research, CIDR 2021},
publisher = {www.cidrdb.org},
year = {2021},
series = {CIDR 2021},
url = {https://cidr.org/temp-website/papers/2021/cidr2021_paper26.pdf},
location = {Virtual Event}
}