TY - GEN
T1 - Apache submarine
T2 - 2nd European Workshop on Machine Learning and Systems, EuroMLSys 2022, in conjunction with ACM EuroSys 2022
AU - Chen, Kai Hsun
AU - Su, Huan Ping
AU - Chuang, Wei Chiu
AU - Hsiao, Hung Chang
AU - Tan, Wangda
AU - Tang, Zhankun
AU - Liu, Xun
AU - Liang, Yanbo
AU - Lo, Wen Chih
AU - Ji, Wanqiang
AU - Hsu, Byron
AU - Hu, Keqiu
AU - Jian, Huiyang
AU - Zhou, Quan
AU - Wang, Chien Min
N1 - Funding Information:
Hung-Chang Hsiao was partly supported by the Intelligent Manufacturing Research Center (iMRC) from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE), and by Ministry of Science and Technology under Grant MOST 110-2218-E-006-027.
Publisher Copyright:
© 2022 Owner/Author.
PY - 2022/4/5
Y1 - 2022/4/5
N2 - 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 [24] to improve their productivity. However, existing machine-learning platforms are ill-equipped to address the "Machine Learning tech debts"[36] 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, and takes all infrastructure administrators, expert data scientists, and citizen data scientists into consideration. Submarine has been widely used in many technology companies, including Ke.com and LinkedIn. We present two use cases in Section 5.
AB - 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 [24] to improve their productivity. However, existing machine-learning platforms are ill-equipped to address the "Machine Learning tech debts"[36] 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, and takes all infrastructure administrators, expert data scientists, and citizen data scientists into consideration. Submarine has been widely used in many technology companies, including Ke.com and LinkedIn. We present two use cases in Section 5.
UR - http://www.scopus.com/inward/record.url?scp=85128402140&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85128402140&partnerID=8YFLogxK
U2 - 10.1145/3517207.3526984
DO - 10.1145/3517207.3526984
M3 - Conference contribution
AN - SCOPUS:85128402140
T3 - EuroMLSys 2022 - Proceedings of the 2nd European Workshop on Machine Learning and Systems
SP - 101
EP - 108
BT - EuroMLSys 2022 - Proceedings of the 2nd European Workshop on Machine Learning and Systems
PB - Association for Computing Machinery, Inc
Y2 - 5 April 2022 through 8 April 2022
ER -