一句话：通过分析 MLOps 社区 66 小时从业者演讲，识别出 17 个 ML 生产化中的社会技术反模式并提出建议。
尽管 ML 赋能软件在经济上快速增长，企业仍普遍难以将 ML 模型投入生产；已有研究多关注测试、流水线等技术问题，社会技术层面关注不足。本文开展该领域规模最大的定性实证研究，人工分析 MLOps 社区录制的 66 小时从业者演讲（Slack 工作区成员超 11,000 人）。研究识别出 17 个反模式，其根源常在于组织或管理问题，并给出从技术方案、指南到组织重构的应对建议。最后，作者将发现与既有研究对照，既证实已有结论、验证自身结果，也提出新洞见。
Although machine learning (ML)-enabled software systems seem to be a success story considering their rise in economic power, there are consistent reports from companies and practitioners struggling to bring ML models into production. Many papers have focused on specific, and purely technical aspects, such as testing and pipelines, but only few on socio-technical aspects. Driven by numerous anecdotes and reports from practitioners, our goal is to collect and analyze socio-technical challenges of productionizing ML models centered around and within teams. To this end, we conducted the largest qualitative empirical study in this area, involving the manual analysis of 66 hours of talks that have been recorded by the MLOps community. By analyzing talks from practitioners for practitioners of a community with over 11,000 members in their Slack workspace, we found 17 anti-patterns, often rooted in organizational or management problems. We further list recommendations to overcome these problems, ranging from technical solutions over guidelines to organizational restructuring. Finally, we contextu-alize our findings with previous research, confirming existing results, validating our own, and highlighting new insights.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{MailachS23,
title = {Socio-Technical Anti-Patterns in Building ML-Enabled Software: Insights from Leaders on the Forefront},
author = {Alina Mailach and Norbert Siegmund},
booktitle = {ICSE},
year = {2023},
doi = {10.1109/icse48619.2023.00067}
}