MLOps tools enable continuous development of machine learning, following the DevOps process. Different MLOps tools have been presented on the market, however, such a number of tools often create confusion on the most appropriate tool to be used in each DevOps phase. To overcome this issue, we conducted a multivocal literature review mapping 84 MLOps tools identified from 254 Primary Studies, on the DevOps phases, highlighting their purpose, and possible incompatibilities. The result of this work will be helpful to both practitioners and researchers, as a starting point for future investigations on MLOps tools, pipelines, and processes.

翻译：MLOps工具通过遵循DevOps流程，支持机器学习模型的持续开发。尽管市场上已涌现出多种MLOps工具，但工具数量的激增往往导致用户难以确定每个DevOps阶段最适用的工具。为解决这一问题，我们开展了一项多语种文献综述，系统梳理了从254项原始研究中识别出的84种MLOps工具，将其映射至DevOps各阶段，并明确了各工具的功能定位及潜在兼容性问题。本研究成果将为从业者和研究者提供参考，作为未来深入探究MLOps工具、管道及流程的起点。