Applying DevOps Practices of Continuous Automation for Machine Learning

doi:10.3390/info11070363

Applying DevOps Practices of Continuous Automation for Machine Learning

2020-07-13

0

OA

AI

DOI：10.3390/info11070363

原文链接

查看原文

摘要

En 中文

This paper proposes DevOps practices for machine learning application, integrating both the development and operation environment seamlessly. The machine learning processes of development and deployment during the experimentation phase may seem easy. However, if not carefully designed, deploying and using such models may lead to a complex, time-consuming approaches which may require significant and costly efforts for maintenance, improvement, and monitoring. This paper presents how to apply continuous integration (CI) and continuous delivery (CD) principles, practices, and tools so as to minimize waste, support rapid feedback loops, explore the hidden technical debt, improve value delivery and maintenance, and improve operational functions for real-world machine learning applications.

Applying DevOps Practices of Continuous Automation for Machine Learning

摘要

AI总结

期刊

机构

引用论文