Abstract
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.