Machine Learning Operations (MLOps): Overview, Definition, and Architecture
This work addresses the problem of high failure rates in industrial ML projects by clarifying MLOps for researchers and practitioners, though it is incremental as it synthesizes existing knowledge.
The paper tackles the challenge of automating and operationalizing machine learning products in industry, providing an aggregated overview of principles, components, roles, architecture, and workflows for MLOps, along with a definition and open challenges.
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.