Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process
Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process
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DOI：
10.1145/3510003.3510209
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
Nadia Nahar;Shurui Zhou;G. Lewis;Christian Kästner
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文献类型：
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作者：
Nadia Nahar;Shurui Zhou;G. Lewis;Christian Kästner
软件项目中机器学习 (ML) 组件的引入产生了软件工程师与数据科学家和其他专家合作的需求。虽然协作总是充满挑战，但机器学习的探索性模型开发过程、所需的额外技能和知识、测试机器学习系统的困难、持续发展和监控的需要以及公平性和可解释性等非传统质量要求带来了额外的挑战。通过与来自 28 个组织的 45 位从业者的访谈，我们确定了团队在构建机器学习系统并将其部署到生产中时面临的关键协作挑战。我们报告生产机器学习系统开发中的需求、数据和集成的常见协作点，以及相应的团队模式和挑战。我们发现大多数挑战都围绕沟通、文档、工程和流程，并收集建议来应对这些挑战。
The introduction of machine learning (ML) components in software projects has created the need for software engineers to collabo-rate with data scientists and other specialists. While collaboration can always be challenging, ML introduces additional challenges with its exploratory model development process, additional skills and knowledge needed, difficulties testing ML systems, need for continuous evolution and monitoring, and non-traditional quality requirements such as fairness and explainability. Through inter-views with 45 practitioners from 28 organizations, we identified key collaboration challenges that teams face when building and deploying ML systems into production. We report on common col-laboration points in the development of production ML systems for requirements, data, and integration, as well as corresponding team patterns and challenges. We find that most of these challenges center around communication, documentation, engineering, and process, and collect recommendations to address these challenges.