The Impact of Resource Allocation on the Machine Learning Lifecycle

Titelangaben

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: https://doi.org/10.1007/s12599-023-00842-7

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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.