As machine learning is applied more widely, it is necessary to have a machine learning platform for both infrastructure administrators and users including expert data scientists and citizen data scientists to improve their productivity. However, existing machine learning platforms are ill-equipped to address the "Machine Learning tech debts" such as glue code, reproducibility, and portability. Furthermore, existing platforms only take expert data scientists into consideration, and thus they are inflexible for infrastructure administrators and non-user-friendly for citizen data scientists. We propose Submarine, a unified machine learning platform, to address the challenges.
@article{arxiv.2108.09615,
title = {Apache Submarine: A Unified Machine Learning Platform Made Simple},
author = {Kai-Hsun Chen and Huan-Ping Su and Wei-Chiu Chuang and Hung-Chang Hsiao and Wangda Tan and Zhankun Tang and Xun Liu and Yanbo Liang and Wen-Chih Lo and Wanqiang Ji and Byron Hsu and Keqiu Hu and HuiYang Jian and Quan Zhou and Chien-Min Wang},
journal= {arXiv preprint arXiv:2108.09615},
year = {2021}
}