The field of MLOps (Machine Learning Operations), which focuses on effectively managing and operationalizing ML workflows, has grown because of the advancements in machine learning (ML). The goal of this study is to examine and contrast the difficulties encountered in the implementation of MLOps in enterprises with those encountered in DevOps. An SLR (Systematic Literature Review) is the first step in the research process to find the issues raised in the literature. The results of this study are based on qualitative content analysis using grounded theory and semi-structured interviews with 12 ML practitioners from different sectors. Organisational, technical, operational, and business problems are the four distinct aspects of challenges for MLOps that our study highlights. These challenges are further defined by eleven different themes. Our research indicates that while some issues, such as data and model complexity, are unique to MLOps, others are shared by DevOps and MLOps as well. The report offers suggestions for further research and summarises the difficulties.

中文翻译：

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采用 MLOps 的挑战分析


由于机器学习 （ML） 的进步，专注于有效管理和操作 ML 工作流程的 MLOps（机器学习操作）领域已经发展起来。本研究的目的是检查和对比在企业中实施 MLOps 与 DevOps 中遇到的困难。SLR（系统文献综述）是研究过程中发现文献中提出的问题的第一步。本研究的结果基于使用扎根理论的定性内容分析和对来自不同行业的 12 名 ML 从业者的半结构化访谈。组织、技术、运营和业务问题是我们研究强调的 MLOps 面临的四个不同挑战方面。这些挑战由 11 个不同的主题进一步定义。我们的研究表明，虽然某些问题（例如数据和模型复杂性）是 MLOps 独有的，但其他问题也是 DevOps 和 MLOps 所共有的。该报告为进一步研究提供了建议并总结了困难。