Machine learning operations (mlops) : Overview, definition, and architecture

Titelangaben

Kreuzberger, Dominik ; Kühl, Niklas ; Hirschl, Sebastian:
Machine learning operations (mlops) : Overview, definition, and architecture.
In: IEEE Access. Bd. 11 (2023) . - S. 31866-31879.
ISSN 2169-3536
DOI der Verlagsversion: https://doi.org/10.1109/ACCESS.2023.3262138

Volltext

	Format: PDF Name: Machine_Learning_Operations_MLOps_Overview_Definition_and_Architecture.pdf Version: Veröffentlichte Version Verfügbar mit der Lizenz Creative Commons BY-NC-ND 4.0: Namensnennung, nicht kommerziell, keine Bearbeitung
	Download (2MB)

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 contribute to the body of knowledge by providing an aggregated overview of the necessary principles, components, and roles, as well as the associated architecture and workflows. Furthermore, we provide a comprehensive 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.