EMMM: A Unified Meta-Model for Tracking Machine Learning Experiments
Paper in proceeding, 2022

Traditional software engineering tools for managing assets—specifically, version control systems—are inadequate to manage the variety of asset types used in machine-learning model development experiments. Two possible paths to improve the management of machine learning assets include 1) Adopting dedicated machine-learning experiment management tools, which are gaining popularity for supporting concerns such as versioning, traceability, auditability, collaboration, and reproducibility; 2) Developing new and improved version control tools with support for domain-specific operations tailored to machine learning assets. As a contribution to improving asset management on both paths, this work presents Experiment Management Meta-Model (EMMM), a meta-model that unifies the conceptual structures and relationships extracted from systematically selected machine-learning experiment management tools. We explain the metamodel’s concepts and relationships and evaluate it using real experiment data. The proposed meta-model is based on the Eclipse Modeling Framework (EMF) with its meta-modeling language, Ecore, to encode model structures. Our meta-model can be used as a concrete blueprint for practitioners and researchers to improve existing tools and develop new tools with native support for machine-learning-specific assets and operations.



Management tools

Machine learning experiments


Samuel Idowu

University of Gothenburg

Daniel Strüber

Radboud University

University of Gothenburg

Thorsten Berger

University of Gothenburg

Ruhr-Universität Bochum

Proceedings - 48th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2022

9781665461528 (ISBN)

48th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2022
Gran Canaria, Spain,

Subject Categories

Other Computer and Information Science

Software Engineering

Information Science



More information

Latest update

1/3/2024 9