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The Impact of Resource Allocation on the Machine Learning Lifecycle

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. Vol. 66 (2024) . - pp. 203-219.
ISSN 1867-0202
DOI: https://doi.org/10.1007/s12599-023-00842-7

Official URL: Volltext

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 in another language

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
Refereed: Yes
Keywords: ML management; Machine learning lifecycle; Artificial intelligence; Resource-based view; Design science research
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
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: 15 Dec 2023 10:20
Last Modified: 13 May 2024 09:58
URI: https://eref.uni-bayreuth.de/id/eprint/88052