Towards a low-code solution for monitoring machine learning model performance

Panagiotis Kourouklidis; Dimitris Kolovos; Nicholas Matragkas; Joost Noppen

doi:10.1145/3417990.3420196

Towards a low-code solution for monitoring machine learning model performance

Panagiotis Kourouklidis, Dimitris Kolovos, Nicholas Matragkas, Joost Noppen

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

As the use of machine learning techniques by organisations has become more common, the need for software tools that provide the robustness required in a production environment has become apparent. In this paper, we review relevant literature and outline a research agenda for the development of a low-code solution for monitoring the performance of a deployed machine learning model on a continuous basis.

Original language	English
Title of host publication	Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings
Publisher	ACM
Pages	423-430
Number of pages	8
ISBN (Electronic)	9781450381352
DOIs	https://doi.org/10.1145/3417990.3420196
Publication status	Published - 26 Oct 2020
Event	23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Virtual, Online, Canada Duration: 16 Oct 2020 → 23 Oct 2020

Publication series

Name	Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings

Conference

Conference	23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020
Country/Territory	Canada
City	Virtual, Online
Period	16/10/20 → 23/10/20

Bibliographical note

Funding Information:
This paper disseminates results from the Lowcomote project, that received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 813884.

Publisher Copyright:
© 2020 ACM.

Keywords

Concept drift
Data drift
Machine learning
Model monitoring
Software engineering

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10.1145/3417990.3420196

Cite this

Kourouklidis, P., Kolovos, D., Matragkas, N., & Noppen, J. (2020). Towards a low-code solution for monitoring machine learning model performance. In Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings (pp. 423-430). (Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings). ACM. https://doi.org/10.1145/3417990.3420196

Kourouklidis, Panagiotis ; Kolovos, Dimitris ; Matragkas, Nicholas et al. / Towards a low-code solution for monitoring machine learning model performance. Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings. ACM, 2020. pp. 423-430 (Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings).

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Kourouklidis, P , Kolovos, D, Matragkas, N & Noppen, J 2020, Towards a low-code solution for monitoring machine learning model performance. in Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings. Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings, ACM, pp. 423-430, 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020, Virtual, Online, Canada, 16/10/20. https://doi.org/10.1145/3417990.3420196

Towards a low-code solution for monitoring machine learning model performance. / Kourouklidis, Panagiotis ; Kolovos, Dimitris; Matragkas, Nicholas et al.
Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings. ACM, 2020. p. 423-430 (Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Kourouklidis P , Kolovos D, Matragkas N, Noppen J. Towards a low-code solution for monitoring machine learning model performance. In Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings. ACM. 2020. p. 423-430. (Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings). doi: 10.1145/3417990.3420196

Towards a low-code solution for monitoring machine learning model performance

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