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OpenML: Insights From 10 Years and More Than a Thousand Papers

MCML Authors

Link to Profile Bernd Bischl PI Matchmaking

Bernd Bischl

Prof. Dr.

Director

Statistical Learning and Data Science

Matthias Feurer

Prof. Dr.

Thomas Bayes Fellow

* Former Thomas Bayes Fellow

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.

article

Patterns

6.7. Jul. 2025.

Authors

B. Bischl • G. Casalicchio • T. Das • M. Feurer • S. Fischer • P. Gijsbers • S. Mukherjee • A. C. Müller • L. Németh • L. Oala • L. Purucker • S. Ravi • J. N. van Rijn • P. Singh • J. Vanschoren • J. van der Velde • M. Wever

Links

DOI

Research Area

 A1 | Statistical Foundations & Explainability

BibTeXKey: BCD+25

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