MLOps从业者在谈论什么问题？Stack Overflow论坛和GitHub项目讨论的研究

IF 4.3 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information and Software Technology Pub Date : 2025-05-14 DOI:10.1016/j.infsof.2025.107768

Yang Zhang, Yiwen Wu, Tao Wang, Bo Ding, Huaimin Wang

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引用次数: 0

摘要

背景：机器学习操作（MLOps）已经成为解决设计和维护高效机器学习应用程序挑战的关键技术。MLOps的广泛采用使得确定MLOps从业者面临的问题至关重要。然而，这方面的研究相对较少。目的：填补这一研究空白，了解MLOps从业者的兴趣和遇到的困难。方法：我们从在线Q&；A论坛、Stack Overflow和GitHub项目中挖掘讨论数据，分析6345篇帖子和2103个问题。结果：我们在实践中构建了第一个MLOps问题的分类，包括5类19个主题。我们还研究了这些主题的演变和特征（难度和情感），提炼了12个针对不同MLOps问题的常见解，并设计了MLOps- ket知识探索工具。结论：我们发现从业者在执行MLOps实践时面临着各种各样的挑战，并且他们讨论的焦点随着时间的推移而变化。我们的研究对MLOps研究和开发社区做出了贡献，为不同的受众提供了启示，并为未来相关技术和工具的支持提供了指导。

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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What problems are MLOps practitioners talking about? A study of discussions in Stack Overflow forum and GitHub projects

Context:

Machine Learning Operations (MLOps) has emerged as a crucial technology for addressing the challenges of designing and maintaining productive ML applications. The widespread adoption of MLOps makes it essential to identify the problems faced by MLOps practitioners. However, there has been relatively little research in this area.

Objectives:

To fill this research gap and gain an understanding of the interests and difficulties encountered by MLOps practitioners.

Methods:

We mine discussion data from the online Q&A forum, Stack Overflow, and GitHub projects, and analyze 6345 posts and 2103 issues.

Results:

We construct the first taxonomy of MLOps problems in practice, consisting of 5 categories and 19 topics. We also investigate the evolution and characteristics (difficulty and sentiment) of these topics, distill 12 frequent solutions for different MLOps problems, and design an MLOps knowledge exploration tool, MLOps-KET.

Conclusion:

We find that practitioners face diverse challenges when performing MLOps practices and that the focus of their discussions changed over time. Our study contributes to the MLOps research and development community by providing implications for different audiences and guidance for future support of relevant techniques and tools.

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来源期刊

Information and Software Technology 工程技术-计算机：软件工程

CiteScore

9.10

自引率

7.70%

发文量

164

审稿时长

9.6 weeks

期刊介绍： Information and Software Technology is the international archival journal focusing on research and experience that contributes to the improvement of software development practices. The journal''s scope includes methods and techniques to better engineer software and manage its development. Articles submitted for review should have a clear component of software engineering or address ways to improve the engineering and management of software development. Areas covered by the journal include: • Software management, quality and metrics, • Software processes, • Software architecture, modelling, specification, design and programming • Functional and non-functional software requirements • Software testing and verification & validation • Empirical studies of all aspects of engineering and managing software development Short Communications is a new section dedicated to short papers addressing new ideas, controversial opinions, "Negative" results and much more. Read the Guide for authors for more information. The journal encourages and welcomes submissions of systematic literature studies (reviews and maps) within the scope of the journal. Information and Software Technology is the premiere outlet for systematic literature studies in software engineering.