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The Impact of Resource Allocation on the Machine Learning Lifecycle

Author

Listed:
  • Sebastian Duda

    (Fraunhofer Institute for Applied Information Technology FIT Branch Business & Information Systems Engineering
    University of Bayreuth)

  • Peter Hofmann

    (Fraunhofer Institute for Applied Information Technology FIT Branch Business & Information Systems Engineering
    appliedAI Initiative GmbH
    University of Bayreuth)

  • Nils Urbach

    (Fraunhofer Institute for Applied Information Technology FIT Branch Business & Information Systems Engineering
    Frankfurt University of Applied Sciences)

  • Fabiane Völter

    (Fraunhofer Institute for Applied Information Technology FIT Branch Business & Information Systems Engineering
    University of Bayreuth)

  • Amelie Zwickel

    (University of Bayreuth)

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.

Suggested Citation

  • Sebastian Duda & Peter Hofmann & Nils Urbach & Fabiane Völter & Amelie Zwickel, 2024. "The Impact of Resource Allocation on the Machine Learning Lifecycle," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 66(2), pages 203-219, April.
    • Handle: RePEc:spr:binfse:v:66:y:2024:i:2:d:10.1007_s12599-023-00842-7
    DOI: 10.1007/s12599-023-00842-7
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    References listed on IDEAS

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