Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-30443
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Billeter, Yann | - |
dc.contributor.author | Denzel, Philipp | - |
dc.contributor.author | Chavarriaga, Ricardo | - |
dc.contributor.author | Forster, Oliver | - |
dc.contributor.author | Schilling, Frank-Peter | - |
dc.contributor.author | Brunner, Stefan | - |
dc.contributor.author | Frischknecht-Gruber, Carmen | - |
dc.contributor.author | Reif, Monika Ulrike | - |
dc.contributor.author | Weng, Joanna | - |
dc.date.accessioned | 2024-04-12T09:20:50Z | - |
dc.date.available | 2024-04-12T09:20:50Z | - |
dc.date.issued | 2024-05-31 | - |
dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/30443 | - |
dc.description.abstract | As Artificial Intelligence (AI) systems are becoming ever more capable of performing complex tasks, their prevalence in industry, as well as society, is increasing rapidly. Adoption of AI systems requires humans to trust them, leading to the concept of trustworthy AI which covers principles such as fairness, reliability, explainability, or safety. Implementing AI in a trustworthy way is encouraged by newly developed industry norms and standards, and will soon be enforced by legislation such as the EU AI Act (EU AIA). We argue that Machine Learning Operations (MLOps), a paradigm which covers best practices and tools to develop and maintain AI and Machine Learning (ML) systems in production reliably and efficiently, provides a guide to implementing trustworthiness into the AI development and operation lifecycle. In addition, we present an implementation of a framework based on various MLOps tools which enables verification of trustworthiness principles using the example of a computer vision ML model. | de_CH |
dc.language.iso | en | de_CH |
dc.publisher | ZHAW Zürcher Hochschule für Angewandte Wissenschaften | de_CH |
dc.rights | Licence according to publishing contract | de_CH |
dc.subject | AI | de_CH |
dc.subject | MLOps | de_CH |
dc.subject | Explainability | de_CH |
dc.subject | Trustworthiness | de_CH |
dc.subject.ddc | 006: Spezielle Computerverfahren | de_CH |
dc.title | MLOps as enabler of trustworthy AI | de_CH |
dc.type | Konferenz: Paper | de_CH |
dcterms.type | Text | de_CH |
zhaw.departement | School of Engineering | de_CH |
zhaw.organisationalunit | Centre for Artificial Intelligence (CAI) | de_CH |
zhaw.organisationalunit | Institut für Angewandte Mathematik und Physik (IAMP) | de_CH |
dc.identifier.doi | 10.21256/zhaw-30443 | - |
zhaw.conference.details | 11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 30-31 May 2024 | de_CH |
zhaw.funding.eu | No | de_CH |
zhaw.originated.zhaw | Yes | de_CH |
zhaw.publication.status | acceptedVersion | de_CH |
zhaw.publication.review | Peer review (Publikation) | de_CH |
zhaw.webfeed | Datalab | de_CH |
zhaw.webfeed | Intelligent Vision Systems | de_CH |
zhaw.webfeed | Responsible Artificial Intelligence Innovation | de_CH |
zhaw.funding.zhaw | certAInty – A Certification Scheme for AI systems | de_CH |
zhaw.author.additional | No | de_CH |
zhaw.display.portrait | Yes | de_CH |
Appears in collections: | Publikationen School of Engineering |
Files in This Item:
File | Description | Size | Format | |
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2024_Billeter-etal_MLOps-for-Trustworthy-AI_SDS24.pdf | Accepted Version | 108.33 kB | Adobe PDF | View/Open |
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Billeter, Y., Denzel, P., Chavarriaga, R., Forster, O., Schilling, F.-P., Brunner, S., Frischknecht-Gruber, C., Reif, M. U., & Weng, J. (2024, May 31). MLOps as enabler of trustworthy AI. 11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 30-31 May 2024. https://doi.org/10.21256/zhaw-30443
Billeter, Y. et al. (2024) ‘MLOps as enabler of trustworthy AI’, in 11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 30-31 May 2024. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. Available at: https://doi.org/10.21256/zhaw-30443.
Y. Billeter et al., “MLOps as enabler of trustworthy AI,” in 11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 30-31 May 2024, May 2024. doi: 10.21256/zhaw-30443.
BILLETER, Yann, Philipp DENZEL, Ricardo CHAVARRIAGA, Oliver FORSTER, Frank-Peter SCHILLING, Stefan BRUNNER, Carmen FRISCHKNECHT-GRUBER, Monika Ulrike REIF und Joanna WENG, 2024. MLOps as enabler of trustworthy AI. In: 11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 30-31 May 2024. Conference paper. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. 31 Mai 2024
Billeter, Yann, Philipp Denzel, Ricardo Chavarriaga, Oliver Forster, Frank-Peter Schilling, Stefan Brunner, Carmen Frischknecht-Gruber, Monika Ulrike Reif, and Joanna Weng. 2024. “MLOps as Enabler of Trustworthy AI.” Conference paper. In 11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 30-31 May 2024. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. https://doi.org/10.21256/zhaw-30443.
Billeter, Yann, et al. “MLOps as Enabler of Trustworthy AI.” 11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 30-31 May 2024, ZHAW Zürcher Hochschule für Angewandte Wissenschaften, 2024, https://doi.org/10.21256/zhaw-30443.
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