<strong>Practices for Managing Machine Learning Products: a Multivocal Literature</strong>
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The data available is the replication package as supplementary material of the paper "Practices for Managing Machine Learning Products: a Multivocal Literature Review , providing a detailed chain of evidence so that any researcher can verify all codes and the most relevant excerpts. <br> Abstract: Machine Learning (ML) has grown in popularity in the software industry due to its ability to solve complex problems. Developing ML Systems involves more uncertainty and risk because it requires identifying a business opportunity and managing source code, data, and trained models. Our research aims to identify the existing practices used in the industry for building ML applications, comprehending the organizational complexity of adopting ML Systems. We conducted a Multivocal Literature Review and, then, created a taxonomy of the practices applied to the ML System lifecycle discussed among practitioners and researchers. The core of the study emerged from 41 selected posts from the grey literature and 37 selected scientific papers. Applying Initial Coding and Focused Coding techniques into these data, we mapped 91 practices into six core categories related to designing, developing, testing, and deploying ML Systems. The results, including a taxonomy of practices, provide organizations with valuable insights to identify gaps in their current ML processes and practices and a roadmap for improving, optimizing, and managing ML systems.
可获取的数据为本论文《机器学习产品管理实践：多声文献综述》（Practices for Managing Machine Learning Products: a Multivocal Literature Review）的补充材料复现包，其提供了完整的证据链条，以便所有研究者均可复现所有代码并核验核心相关节选片段。 摘要：机器学习（Machine Learning，ML）凭借其解决复杂问题的能力，在软件行业中愈发普及。开发机器学习系统面临更多不确定性与风险，因为其需要识别商业机遇，并对源代码、数据集与训练好的模型进行管理。本研究旨在识别行业内用于构建机器学习应用的现有实践，并理解落地机器学习系统时的组织复杂性。本研究开展了多声文献综述（Multivocal Literature Review），并基于从业者与研究者讨论的机器学习系统生命周期相关实践，构建了一套实践分类体系。本研究的核心数据源自41篇精选的灰色文献帖子与37篇精选的学术论文。通过对这些数据应用初始编码（Initial Coding）与聚焦编码（Focused Coding）技术，本研究将91项实践归纳为六大核心类别，涵盖机器学习系统的设计、开发、测试与部署环节。本研究的结果（包含实践分类体系）可为各类组织提供极具价值的洞见：帮助其识别当前机器学习流程与实践中的不足，并为机器学习系统的改进、优化与管理提供路线图。
