OpenML

Insights from 10 years and more than a thousand papers

authored by

Bernd Bischl, Giuseppe Casalicchio, Taniya Das, Matthias Feurer, Sebastian Fischer, Pieter Gijsbers, Subhaditya Mukherjee, Andreas C. Müller, László Németh, Luis Oala, Lennart Purucker, Sahithya Ravi, Jan N. van Rijn, Prabhant Singh, Joaquin Vanschoren, Jos van der Velde, Marcel Wever

Abstract

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.

Organisation(s)

L3S Research Centre

External Organisation(s)

Ludwig-Maximilians-Universität München (LMU)
Munich Center for Machine Learning (MCML)
Eindhoven University of Technology (TU/e)
Microsoft Research
Weierstrass Institute for Applied Analysis and Stochastics (WIAS)
Max Planck Institute for Demographic Research (MPIDR)
Dotphoton AG
University of Freiburg
University of British Columbia
Leiden University

Type

Article

Journal

Patterns

Volume

6

No. of pages

18

Publication date

11.07.2025

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

General Decision Sciences

Electronic version(s)

https://doi.org/10.1016/j.patter.2025.101317 (Access: Open)