Machine Learning Operations (MLOps) streamline the lifecycle of machine-learning models in production. In recent years, the topic has picked the interest of practitioners, and consequently, a considerable number of tools and gray literature on architecting MLOps environments has emerged. However, this has created a new problem for organizations: selecting the most appropriate tools and design options for implementing their MLOps environments. To alleviate this problem, this paper proposes a reference architecture and requirements for MLOps by systematically reviewing 58 industrial gray literature articles. Such reference architecture drawn from the state of practice shall aid organizations in making better design and technology choices when embarking on their MLOps journey while providing a technology-independent baseline for further MLOps research.
Kumara et al. (Tue,) studied this question.