一句话：通过访谈 45 名从业者，揭示生产级 ML 系统开发中软件工程师与数据科学家协作的关键挑战、协作点与团队模式。
软件项目中引入 ML 组件后，软件工程师需与数据科学家等专家协作。ML 的探索式模型开发、额外技能需求、测试困难、持续演进与监控、公平性和可解释性等非传统质量要求，使协作面临新挑战。作者访谈了来自 28 个组织的 45 名从业者，研究生产级 ML 系统构建与部署中的协作。结果识别出需求、数据和集成等常见协作点，以及相应团队模式与挑战，并发现挑战主要集中在沟通、文档、工程和流程四个方面，最后整理了应对建议。
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.