  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.
