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

Bibliografische Daten exportieren
 

The Impact of Resource Allocation on the Machine Learning Lifecycle

DOI zum Zitieren der Version auf EPub Bayreuth: https://doi.org/10.15495/EPub_UBT_00007534
URN to cite this document: urn:nbn:de:bvb:703-epub-7534-3

Title data

Duda, Sebastian ; Hofmann, Peter ; Urbach, Nils ; Völter, Fabiane ; Zwickel, Amelie:
The Impact of Resource Allocation on the Machine Learning Lifecycle.
In: Business & Information Systems Engineering. (2023) .
ISSN 1867-0202
DOI der Verlagsversion: https://doi.org/10.1007/s12599-023-00842-7

[thumbnail of s12599-023-00842-7.pdf]
Format: PDF
Name: s12599-023-00842-7.pdf
Version: Published Version
Available under License Creative Commons BY 4.0: Attribution
Download (678kB)

Project information

Project title:
Project's official title
Project's id
Projektgruppe WI Künstliche Intelligenz
No information
Projektgruppe WI Strategisches IT-Management
No information

Abstract

An organization’s ability to develop Machine Learning (ML) applications depends on its available resource base. Without awareness and understanding of all relevant resources as well as their impact on the ML lifecycle, we risk inefficient allocations as well as missing monopolization tendencies. To counteract these risks, the study develops a framework that interweaves the relevant resources with the procedural and technical dependencies within the ML lifecycle. To rigorously develop and evaluate this framework the paper follows the Design Science Research paradigm and builds on a literature review and an interview study. In doing so, it bridges the gap between the software engineering and management perspective to advance the ML management discourse. The results extend the literature by introducing not yet discussed but relevant resources, describing six direct and indirect effects of resources on the ML lifecycle, and revealing the resources’ contextual properties. Furthermore, the framework is useful in practice to support organizational decision-making and contextualize monopolization tendencies.

Further data

Item Type: Article in a journal
Keywords: ML management; Machine learning lifecycle; Artificial intelligence; Resource-based view; Design science research
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
Research Institutions
Research Institutions > Affiliated Institutes
Research Institutions > Affiliated Institutes > Branch Business and Information Systems Engineering of Fraunhofer FIT
Research Institutions > Affiliated Institutes > FIM Research Center for Information Management
Faculties
Faculties > Faculty of Law, Business and Economics
Language: English
Originates at UBT: Yes
URN: urn:nbn:de:bvb:703-epub-7534-3
Date Deposited: 13 Mar 2024 08:32
Last Modified: 13 Mar 2024 08:33
URI: https://epub.uni-bayreuth.de/id/eprint/7534

Downloads

Downloads per month over past year