Apache Submarine: A Unified Machine Learning Platform Made Simple

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.

Automated summary

With the increasing use of machine learning, the need for a machine learning platform that caters to both infrastructure administrators and users, including expert data scientists and citizen data scientists, has become crucial. However, existing platforms are insufficient in addressing machine learning tech debts such as glue code, reproducibility, and portability. Additionally, these platforms only focus on expert data scientists, making them inflexible for infrastructure administrators and non-user-friendly for citizen data scientists. To address these issues, we propose Submarine, a unified machine learning platform that considers the needs of infrastructure administrators, expert data scientists, and citizen data scientists. Submarine has already been widely adopted in various technology companies, including Ke.com and LinkedIn. Two use cases are presented in Section 5.