Apache Submarine: A Unified Machine Learning Platform Made Simple
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 35 references that are in the map. First posted on arXiv 22 Aug 2021.
Builds on
8 of 35 references.
References · 8
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Citing papers per year: 2022: 1.
1 paper.
- 2022Management of Machine Learning Lifecycle Artifacts: A Surveycited by 13
- 2022Management of Machine Learning Lifecycle Artifacts: A Survey21 Oct 2022