Publications by the same author
plus in the repository
plus in Google Scholar

Bibliografische Daten exportieren
 

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

DOI zum Zitieren der Version auf EPub Bayreuth: https://doi.org/10.15495/EPub_UBT_00007577
URN to cite this document: urn:nbn:de:bvb:703-epub-7577-0

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 der Verlagsversion: https://doi.org/10.1109/ACCESS.2023.3262138

[thumbnail of Machine_Learning_Operations_MLOps_Overview_Definition_and_Architecture.pdf]
Format: PDF
Name: Machine_Learning_Operations_MLOps_Overview_Definition_and_Architecture.pdf
Version: Published Version
Available under License Creative Commons BY-NC-ND 4.0: Attribution, Noncommercial, No Derivative Works
Download (2MB)

Project information

Project title:
Project's official title
Project's id
Open Access Publizieren
No information

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.

Further data

Item Type: Article in a journal
Keywords: CI/CD; DevOps; machine learning; MLOps; operations; workflow orchestration
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 330 Economics
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
Language: English
Originates at UBT: Yes
URN: urn:nbn:de:bvb:703-epub-7577-0
Date Deposited: 18 Mar 2024 09:42
Last Modified: 18 Mar 2024 09:43
URI: https://epub.uni-bayreuth.de/id/eprint/7577

Downloads

Downloads per month over past year