Machine Learning Operations (MLOps): Overview, Definition, and Architecture

TL;DR

This paper provides a comprehensive overview of MLOps, defining its principles, components, roles, architecture, workflows, and open challenges to guide researchers and practitioners in automating and operationalizing ML products.

Contribution

It offers the first aggregated overview of MLOps principles, architecture, and workflows based on literature, tools, and expert insights, clarifying its scope and challenges.

Findings

Defined MLOps and its core principles.

Outlined architecture and workflows for MLOps.

Identified open challenges in operationalizing ML.

Abstract

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.