A domain-specific language for monitoring ML model performance - CEA - Commissariat à l’énergie atomique et aux énergies alternatives Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

A domain-specific language for monitoring ML model performance

Résumé

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 re- quires 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.
Fichier principal
Vignette du fichier
SAM_2023_1_.pdf (744.26 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

cea-04485024 , version 1 (01-03-2024)

Identifiants

Citer

Panagiotis Kourouklidis, Dimitris Kolovos, Joost Noppen, Nicholas Matragkas. A domain-specific language for monitoring ML model performance. 2023 ACM/IEEE - MODELS-C - International Conference on Model Driven Engineering Languages and Systems Companion, Oct 2023, Västerås, Sweden. pp.266-275, ⟨10.1109/models-c59198.2023.00056⟩. ⟨cea-04485024⟩
6 Consultations
9 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More