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

Title data

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

Official URL: Volltext

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Abstract in another language

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.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: CI/CD; DevOps; machine learning; MLOps; operations; workflow orchestration
Institutions of the University: Faculties > Faculty of Law, Business and Economics > Department of Business Administration
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Information Systems Management > Chair Information Systems Management - Univ.-Prof. Dr.-Ing. Niklas Kühl
Faculties
Faculties > Faculty of Law, Business and Economics
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Information Systems Management
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 330 Economics
Date Deposited: 03 May 2023 10:30
Last Modified: 18 Mar 2024 09:43
URI: https://eref.uni-bayreuth.de/id/eprint/76163