MLOps: A Guide to its Adoption in the Context of Responsible AI

B. M. A. Matsui, D. Goya
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引用次数: 4

Abstract

DevOps practices have increasingly been applied to software development as well as the machine learning lifecycle, in a process known as MLOps. Currently, many professionals have written about this topic, but still few results can be found in the academic and scientific literature on MLOps and how to to implement it effectively. Considering aspects of responsible AI, this number is even lower, opening up a field of research with many possibilities. This article presents five steps to guide the understanding and adoption of MLOps in the context of responsible AI. The study aims to serve as a reference guide for all those who wish to learn more about the topic and intend to implement MLOps practices to develop their systems, following responsible AI principles.CCS CONCEPTS• Software and its engineering → Software creation and management; • Computing methodologies → Machine learning.
MLOps:在负责任的人工智能背景下采用MLOps的指南
DevOps实践越来越多地应用于软件开发以及机器学习生命周期,这一过程被称为MLOps。目前,已经有许多专业人士撰写了这一主题,但在学术和科学文献中,关于MLOps以及如何有效实施MLOps的结果仍然很少。考虑到负责任的人工智能方面,这个数字甚至更低,开辟了一个具有许多可能性的研究领域。本文提出了在负责任的人工智能背景下指导理解和采用mlop的五个步骤。该研究旨在为所有希望更多地了解该主题并打算按照负责任的人工智能原则实施MLOps实践以开发其系统的人提供参考指南。•软件及其工程→软件创建和管理;•计算方法→机器学习。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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