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:机器学习运维全流程工具(MLOps)能够实现机器学习的持续开发,符合DevOps流程。市场上出现了不同的MLOps工具,但这么多工具常常会让人们对在每个DevOps阶段使用什么工具感到困惑。为了解决这个问题,我们进行了一次多元共鸣文献综述,对254个初级研究中发现的84个MLOps工具进行了映射,并强调了它们的用途和可能的不兼容性。这项工作的结果对从业者和研究人员都有帮助,可作为未来MLOps工具、流程和管道研究的起点。