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.
Abstract
As machine learning (ML) starts to offer competitive advantages for an increasing number of application domains, many organisations invest in developing ML-enabled products. The development of these products poses unique challenges compared to traditional software engineering projects and requires the collaboration of people from different disciplines. This work focuses on alleviating some of these challenges related to implementing monitoring systems for deployed ML models. To this end, a domain-specific language (DSL) is developed that data scientists can use to declaratively define monitoring workflows. Complementary to the DSL, a runtime component is developed that implements the specified behaviour. This component is designed to be easily integrated with the rest of an organisation's ML platform and extended by software engineers that do not necessarily have experience with model-driven engineering. An evaluation of the proposed system that supports the validity of the approach is also presented.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | 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: |
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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 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:208524 |