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https://doi.org/10.21256/zhaw-27724| Publikationstyp: | Konferenz: Paper |
| Art der Begutachtung: | Peer review (Publikation) |
| Neue Version verfügbar unter: | https://digitalcollection.zhaw.ch/handle/11475/28148 |
| Titel: | From concept to implementation : the data-centric development process for AI in industry |
| Autor/-in: | Luley, Paul-Philipp Deriu, Jan Milan Yan, Peng Schatte, Gerrit A. Stadelmann, Thilo |
| et. al: | No |
| DOI: | 10.21256/zhaw-27724 |
| Tagungsband: | Proceedings of the 10th IEEE Swiss Conference on Data Science |
| Angaben zur Konferenz: | 10th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 22-23 June 2023 |
| Erscheinungsdatum: | Jun-2023 |
| Verlag / Hrsg. Institution: | IEEE |
| Sprache: | Englisch |
| Schlagwörter: | MLOps; ML pipeline; Data preparation |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren |
| Zusammenfassung: | We examine the paradigm of data-centric artificial intelligence (DCAI) as a solution to the obstacles that small and medium-sized enterprises (SMEs) face in adopting AI. While the prevalent model-centric approach emphasizes collecting large amounts of data, SMEs often suffer from small datasets, data drift, and sparse ML knowledge, which hinders them from implementing AI. DCAI, on the other hand, emphasizes to systematically engineer the data used to build an AI system. Our contribution is to provide a concrete, transferable implementation of a DCAI development process geared towards industrial application, specifically in machining and manufacturing, and demonstrate how it enhances data quality by fostering collaboration between domain experts and ML engineers. This added value can place AI at the disposal of more SMEs. We provide the necessary background for practitioners to follow the rationale behind DCAI and successfully deploy the provided process template. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/27724 |
| Volltext Version: | Akzeptierte Version |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| Departement: | School of Engineering |
| Organisationseinheit: | Centre for Artificial Intelligence (CAI) |
| Publiziert im Rahmen des ZHAW-Projekts: | DISTRAL: Industrial Process Monitoring for Injection Molding with Distributed Transfer Learning |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2023_Luley-etal_Data-centric-development-process-for-AI-in-industry.pdf | Accepted Version | 257.32 kB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
Luley, P.-P., Deriu, J. M., Yan, P., Schatte, G. A., & Stadelmann, T. (2023, June). From concept to implementation : the data-centric development process for AI in industry. Proceedings of the 10th IEEE Swiss Conference on Data Science. https://doi.org/10.21256/zhaw-27724
Luley, P.-P. et al. (2023) ‘From concept to implementation : the data-centric development process for AI in industry’, in Proceedings of the 10th IEEE Swiss Conference on Data Science. IEEE. Available at: https://doi.org/10.21256/zhaw-27724.
P.-P. Luley, J. M. Deriu, P. Yan, G. A. Schatte, and T. Stadelmann, “From concept to implementation : the data-centric development process for AI in industry,” in Proceedings of the 10th IEEE Swiss Conference on Data Science, Jun. 2023. doi: 10.21256/zhaw-27724.
LULEY, Paul-Philipp, Jan Milan DERIU, Peng YAN, Gerrit A. SCHATTE und Thilo STADELMANN, 2023. From concept to implementation : the data-centric development process for AI in industry. In: Proceedings of the 10th IEEE Swiss Conference on Data Science. Conference paper. IEEE. Juni 2023
Luley, Paul-Philipp, Jan Milan Deriu, Peng Yan, Gerrit A. Schatte, and Thilo Stadelmann. 2023. “From Concept to Implementation : The Data-Centric Development Process for AI in Industry.” Conference paper. In Proceedings of the 10th IEEE Swiss Conference on Data Science. IEEE. https://doi.org/10.21256/zhaw-27724.
Luley, Paul-Philipp, et al. “From Concept to Implementation : The Data-Centric Development Process for AI in Industry.” Proceedings of the 10th IEEE Swiss Conference on Data Science, IEEE, 2023, https://doi.org/10.21256/zhaw-27724.
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