Applying DevOps Practices of Continuous Automation for Machine Learning
Applying DevOps Practices of Continuous Automation for Machine Learning
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DOI：
10.3390/info11070363
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发表时间：
2020-07-01
期刊：
影响因子：
3.1
通讯作者：
Apostolopoulos, Charalampos
中科院分区：
文献类型：
--
作者：
Karamitsos, Ioannis;Albarhami, Saeed;Apostolopoulos, Charalampos
本文提出了机器学习应用的DevOps实践，将开发环境和运行环境无缝集成。在实验阶段，机器学习的开发和部署过程似乎很容易。然而，如果不仔细设计，部署和使用这样的模型可能会导致复杂且耗时的方法，可能需要大量且昂贵的维护、改进和监视工作。本文介绍了如何应用持续集成（CI）和持续交付（CD）原则、实践和工具，从而最大限度地减少浪费，支持快速反馈循环，探索隐藏的技术债务，改进价值交付和维护，并改进现实世界机器学习应用程序的操作功能。
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.