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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-30443
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dc.contributor.authorBilleter, Yann-
dc.contributor.authorDenzel, Philipp-
dc.contributor.authorChavarriaga, Ricardo-
dc.contributor.authorForster, Oliver-
dc.contributor.authorSchilling, Frank-Peter-
dc.contributor.authorBrunner, Stefan-
dc.contributor.authorFrischknecht-Gruber, Carmen-
dc.contributor.authorReif, Monika Ulrike-
dc.contributor.authorWeng, Joanna-
dc.date.accessioned2024-04-12T09:20:50Z-
dc.date.available2024-04-12T09:20:50Z-
dc.date.issued2024-05-31-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/30443-
dc.description.abstractAs 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.isoende_CH
dc.publisherZHAW Zürcher Hochschule für Angewandte Wissenschaftende_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectAIde_CH
dc.subjectMLOpsde_CH
dc.subjectExplainabilityde_CH
dc.subjectTrustworthinessde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleMLOps as enabler of trustworthy AIde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitCentre for Artificial Intelligence (CAI)de_CH
zhaw.organisationalunitInstitut für Angewandte Mathematik und Physik (IAMP)de_CH
dc.identifier.doi10.21256/zhaw-30443-
zhaw.conference.details11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 30-31 May 2024de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedIntelligent Vision Systemsde_CH
zhaw.webfeedResponsible Artificial Intelligence Innovationde_CH
zhaw.funding.zhawcertAInty – A Certification Scheme for AI systemsde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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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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