Bibliograph.
Daten | Weber, Christian; Hirmer, Pascal; Reimann, Peter: A Model Management Platform for Industry 4.0 - Enabling Management of Machine Learning Models in Manufacturing Environments.
In: Abramowicz, Witold (Hrsg); Alt, Rainer (Hrsg); Klein, Gary (Hrsg); Paschke, Adrian (Hrsg); Sandkuhl, Kurt (Hrsg): Proceedings of the 23rd International Conference on Business Information Systems (BIS).
Universität Stuttgart, Fakultät Informatik, Elektrotechnik und Informationstechnik.
Lecture Notes in Business Information Processing, englisch.
Springer International Publishing, November 2020.
ISSN: 1865-1348.
Artikel in Tagungsband (Konferenz-Beitrag).
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| CR-Klassif. | H.3.4 (Information Storage and Retrieval Systems and Software)
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| Keywords | Model Management; Machine Learning; Metadata Tracking |
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| Kurzfassung | Industry 4.0 use cases such as predictive maintenance and product quality control make it necessary to create, use and maintain a multitude of di erent machine learning models. In this setting, model management systems help to organize models. However, concepts for model management systems currently focus on data scientists, but do not support non-expert users such as domain experts and business analysts. Thus, it is dicult for them to reuse existing models for their use cases. In this paper, we address these challenges and present an architecture, a metadata schema and a corresponding model management platform.
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| Abteilung(en) | Universität Stuttgart, Institut für Parallele und Verteilte Systeme, Anwendersoftware
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| Projekt(e) | GSaME-NFG
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| Eingabedatum | 5. Mai 2020 |
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