OpenML: Insights from 10 years and more than a thousand papers
OpenML is an open-source platform that democratizes machine-learning evaluation by enabling anyone to share datasets in uniform standards, define precise machine-learning tasks, and automatically share detailed workflows and model evaluations. More than just a platform, OpenML fosters a collaborative ecosystem where scientists create new tools, launch initiatives, and establish standards to advance machine learning. Over the past decade, OpenML has inspired over 1,500 publications across diverse fields, from scientists releasing new datasets and benchmarking new models to educators teaching reproducible science. Looking back, we detail and describe the platform's impact by looking at usage and citations. We share lessons from a decade of building, maintaining, and expanding OpenML, highlighting how rich metadata, collaborative benchmarking, and open interfaces have enhanced research and interoperability. Looking ahead, we cover ongoing efforts to expand OpenML's capabilities and integrate with other platforms, informing a broader vision for open-science infrastructure for machine learning.
Authors
- Jan N. van Rijn (ORCID: https://orcid.org/0000-0003-2898-2168)
- Lennart Purucker (ORCID: https://orcid.org/0009-0001-1181-0549)
- Joaquin Vanschoren (ORCID: https://orcid.org/0000-0001-7044-9805)
- Jos van der Velde (ORCID: https://orcid.org/0000-0003-0430-1532)
- Tapas Ranjan Das (ORCID: https://orcid.org/0009-0001-7326-0853)
- Marcel Wever (ORCID: https://orcid.org/0000-0001-9782-6818)
- Prabhant Singh
- Matthias Feurer (ORCID: https://orcid.org/0000-0001-9611-8588)
- Sahithya Ravi
- Giuseppe Casalicchio (ORCID: https://orcid.org/0000-0001-5324-5966)
- Bernd Bischl (ORCID: https://orcid.org/0000-0001-6002-6980)
- Pieter Gijsbers (ORCID: https://orcid.org/0000-0001-7346-8075)
- Luis Oala (ORCID: https://orcid.org/0000-0002-1379-8627)
- Andreas Müller (ORCID: https://orcid.org/0000-0002-6128-767X)
- László Németh (ORCID: https://orcid.org/0000-0003-1011-9032)
- S. P. Mukherjee (ORCID: https://orcid.org/0000-0002-2958-0112)
- Sebastian Fischer (ORCID: https://orcid.org/0000-0001-8687-9156)
Institutions
- Leibniz University Hannover (DE)
- Leiden University (NL)
- Microsoft (United States) (US)
- University of British Columbia (CA)
- University of Freiburg (DE)
- LMU Klinikum (DE)
- SdPhotonics (United States) (US)
- Weierstrass Institute for Applied Analysis and Stochastics (DE)
- Max Planck Institute for Demographic Research (DE)
- L3S Research Center (DE)
- Munich Center for Machine Learning
- Ludwig-Maximilians-Universität München (DE)
- Eindhoven University of Technology (NL)
Publication Details
- Journal
- Patterns
- Published
- 2025-07-01
- DOI
- https://doi.org/10.1016/j.patter.2025.101317
- Citations
- 5
- Primary Topic
- Machine Learning and Data Classification
- Type
- article
- Field-Weighted Citation Impact
- 12.40
Funders
- Institut national de recherche en informatique et en automatique (INRIA)
- European Commission
- Deutsche Forschungsgemeinschaft
- Universiteit Leiden
- Technische Universiteit Eindhoven
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek
- KU Leuven
- Ludwig-Maximilians-Universität München
- Tartu Ülikool
- HORIZON EUROPE Framework Programme
- Leibniz-Gemeinschaft