The introduction of machine learning (ML) components in software projects has
created the need for software engineers to collaborate 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 interviews 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 collaboration 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.

该研究通过访谈 28 个组织中 45 位从业人员发现，在建立和部署机器学习系统时，团队在需求、数据和整合等方面都面临一些具体的协作挑战，主要来源于沟通、文档记录、工程和流程等方面，因此提出了一些建议以应对这些挑战。

构建基于机器学习的系统中的协作挑战：沟通，文档，工程和流程

Collaboration Challenges in Building ML-Enabled Systems: Communication,  Documentation, Engineering, and Process

Historically time-reversibility of the transitions or processes underpinning
Markov chain Monte Carlo methods (MCMC) has played a key r\^ole in their
development, while the self-adjointness of associated operators together with
the use of classical functional analysis techniques on Hilbert spaces have led
to powerful and practically successful tools to characterize and compare their
performance. Similar results for algorithms relying on nonreversible Markov
processes are scarce. We show that for a type of nonreversible Monte Carlo
Markov chains and processes, of current or renewed interest in the Physics and
Statistical literatures, it is possible to develop comparison results which
closely mirror those available in the reversible scenario. We show that these
results shed light on earlier literature, proving some conjectures and
strengthening some earlier results.

本文研究了一种当前或重新引起物理学和统计学关注的非可逆蒙特卡洛马尔可夫链和过程，开发了一种与可逆情景类似的比较结果，揭示了早期文献的一些猜想，并加强了一些早期结果。