MLOps in Data Science Projects: A Review

Data Science (DS) has gained increased relevance due to the potential to extract useful insights from data. Quite commonly, this involves the utilization of Machine Learning (ML). The challenging pursuit of developing and productionizing ML models can be supported and automated through MLOps, a specialization of the DevOps paradigm from software development. Therefore, MLOps offers significant potential for DS projects, which are suffering from notable failure rates. Accordingly, this literature review focuses on examining the current state-of-the-art of the publications in this area. Most importantly, the analysis showed that the current MLOps approaches in the literature predominantly emphasize model development and deployment, while organizational aspects (business understanding, evaluation) in a DS project are neglected. As DS project success is not exclusively dependent on technical matters, advancing the MLOps field by bridging the gap between business objectives and the modeling perspective through appropriate frameworks should be pursued in future research.