Applying DevOps Practices of Continuous Automation for Machine Learning

Information

Ioannis Karamitsos¹  Saeed Albarhami¹  Charalampos Apostolopoulos² 

[1] Department of Computing, Rochester Institute of Technology, Dubai Campus, Dubai 341055, UAE;Department of Management Science, University of Strathclyde Business School, Glasgow G1 1XQ, UK;

关键词: CRISP-DM;    CI;    CD;    DevOps;    machine learning;    pipeline;   

DOI  :  10.3390/info11070363

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown