Apache Submarine: A Unified Machine Learning Platform Made Simple
This work addresses the need for a more flexible and user-friendly machine learning platform to enhance productivity across diverse user groups, though it appears incremental as it builds on existing platform concepts.
The paper tackles the problem of existing machine learning platforms being inadequate for addressing 'Machine Learning tech debts' like glue code and reproducibility, and proposes Submarine as a unified platform to improve productivity for infrastructure administrators, expert data scientists, and citizen data scientists.
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.