Context. Despite the increasing adoption of Machine Learning Operations (MLOps), teams still encounter challenges in effectively applying this paradigm to their specific projects. While there is a large variety of available tools usable for MLOps, there is simultaneously a lack of consolidated architecture knowledge that can inform the architecture design. Objective. Our primary objective is to provide a comprehensive overview of (i) how MLOps architectures are defined across the literature and (ii) which tools are mentioned to support the implementation of each architecture component. Method. We apply the Systematic Mapping Study method and select 43 primary studies via automatic, manual, and snowballing-based search and selection procedures. Subsequently, we use card sorting to synthesize the results. Results. We contribute (i) a categorization of 35 MLOps architecture components, (ii) a description of several MLOps architecture variants, and (iii) a systematic map between the identified components and the existing MLOps tools. Conclusion. This study provides an overview of the state of the art in MLOps from an architectural perspective. Researchers and practitioners can use our findings to inform the architecture design of their MLOps systems.

翻译：背景。尽管机器学习运维（MLOps）的采用日益增加，团队在将这一范式有效应用于具体项目时仍面临挑战。尽管有大量可用于MLOps的工具，但同时缺乏能够指导架构设计的、系统化的架构知识。目标。我们的主要目标是全面概述：（i）文献中如何定义MLOps架构，以及（ii）提及了哪些工具来支持每个架构组件的实现。方法。我们应用系统性图谱研究方法，通过自动化、手动和滚雪球式的检索与筛选流程，选取了43项主要研究。随后，我们使用卡片分类法对结果进行综合归纳。结果。我们贡献了：（i）对35个MLOps架构组件的分类，（ii）对若干MLOps架构变体的描述，以及（iii）在已识别组件与现有MLOps工具之间建立的系统性映射图谱。结论。本研究从架构视角概述了MLOps的最新技术现状。研究人员和实践者可以利用我们的发现来指导其MLOps系统的架构设计。