  The final goal of all industrial machine learning (ML) projects is to develop
ML products and rapidly bring them into production. However, it is highly
challenging to automate and operationalize ML products and thus many ML
endeavors fail to deliver on their expectations. The paradigm of Machine
Learning Operations (MLOps) addresses this issue. MLOps includes several
aspects, such as best practices, sets of concepts, and development culture.
However, MLOps is still a vague term and its consequences for researchers and
professionals are ambiguous. To address this gap, we conduct mixed-method
research, including a literature review, a tool review, and expert interviews.
As a result of these investigations, we provide an aggregated overview of the
necessary principles, components, and roles, as well as the associated
architecture and workflows. Furthermore, we furnish a definition of MLOps and
highlight open challenges in the field. Finally, this work provides guidance
for ML researchers and practitioners who want to automate and operate their ML
products with a designated set of technologies.
