Artikel in Tagungsband INPROC-2019-10

Weber, Christian; Hirmer, Pascal; Reimann, Peter; Schwarz, Holger: A New Process Model for the Comprehensive Management of Machine Learning Models.
In: Filipe, Joaquim (Hrsg); Smialek, Michal (Hrsg); Brodsky, Alexander (Hrsg); Hammoudi, Slimane (Hrsg): Proceedings of the 21st International Conference on Enterprise Information Systems (ICEIS); Heraklion, Crete, Greece, May 3-5, 2019.
Universität Stuttgart, Fakultät Informatik, Elektrotechnik und Informationstechnik.
S. 415-422, englisch.
SciTePress, Mai 2019.
ISBN: 978-989-758-372-8; DOI: 10.5220/0007725304150422.
Artikel in Tagungsband (Konferenz-Beitrag).
CR-Klassif.I.2 (Artificial Intelligence)
KeywordsModel Management; Machine Learning; Analytics Process

The management of machine learning models is an extremely challenging task. Hundreds of prototypical models are being built and just a few are mature enough to be deployed into operational enterprise information systems. The lifecycle of a model includes an experimental phase in which a model is planned, built and tested. After that, the model enters the operational phase that includes deploying, using, and retiring it. The experimental phase is well known through established process models like CRISP-DM or KDD. However, these models do not detail on the interaction between the experimental and the operational phase of machine learning models. In this paper, we provide a new process model to show the interaction points of the experimental and operational phase of a machine learning model. For each step of our process, we discuss according functions which are relevant to managing machine learning models.

Abteilung(en)Universität Stuttgart, Institut für Parallele und Verteilte Systeme, Anwendersoftware
Software Campus
Eingabedatum29. Mai 2019
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