A Domain-Specific Language for Monitoring ML Model Performance

Kourouklidis, Panagiotis, Kolovos, Dimitris orcid.org/0000-0002-1724-6563, Noppen, Joost et al. (1 more author) (2023) A Domain-Specific Language for Monitoring ML Model Performance. In: Proceedings - 2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion, MODELS-C 2023. 2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2023, 01-06 Oct 2023 Proceedings - 2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion, MODELS-C 2023 . Institute of Electrical and Electronics Engineers Inc. , SWE , pp. 266-275.

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Item Type: Proceedings Paper
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Funding Information: The work presented in this paper has been partially supported by the Lowcomote project, which received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curiegrant agreement No. 813884. Publisher Copyright: © 2023 IEEE. This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy.

Keywords: Dataset Shift,Machine Learning,MLOps,Model-Driven Engineering
Dates:
  • Published: 22 December 2023
  • Accepted: 7 August 2023
Institution: The University of York
Academic Units: The University of York > Faculty of Sciences (York) > Computer Science (York)
Depositing User: Pure (York)
Date Deposited: 31 Jan 2024 13:00
Last Modified: 24 Oct 2024 00:30
Published Version: https://doi.org/10.1109/MODELS-C59198.2023.00056
Status: Published
Publisher: Institute of Electrical and Electronics Engineers Inc.
Series Name: Proceedings - 2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion, MODELS-C 2023
Identification Number: 10.1109/MODELS-C59198.2023.00056
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Filename: SAM_2023_1_.pdf

Description: Accepted Manuscript

Licence: CC-BY 2.5

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