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https://doi.org/10.21256/zhaw-30443| Publication type: | Conference paper |
| Type of review: | Peer review (publication) |
| Title: | MLOps as enabler of trustworthy AI |
| Authors: | Billeter, Yann Denzel, Philipp Chavarriaga, Ricardo Forster, Oliver Schilling, Frank-Peter Brunner, Stefan Frischknecht-Gruber, Carmen Reif, Monika Ulrike Weng, Joanna |
| et. al: | No |
| DOI: | 10.21256/zhaw-30443 |
| Conference details: | 11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 30-31 May 2024 |
| Issue Date: | 31-May-2024 |
| Publisher / Ed. Institution: | ZHAW Zürcher Hochschule für Angewandte Wissenschaften |
| Language: | English |
| Subjects: | AI; MLOps; Explainability; Trustworthiness |
| Subject (DDC): | 006: Special computer methods |
| 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/30443 |
| Fulltext version: | Accepted version |
| License (according to publishing contract): | Licence according to publishing contract |
| Departement: | School of Engineering |
| Organisational Unit: | Centre for Artificial Intelligence (CAI) Institute of Applied Mathematics and Physics (IAMP) |
| Published as part of the ZHAW project: | certAInty – A Certification Scheme for AI systems |
| Appears in collections: | Publikationen School of Engineering |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 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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