OpenML: Insights from 10 years and more than a thousand papers
OpenML is an open-source platform that standardizes machine-learning datasets, tasks, and evaluations to foster collaborative research and reproducible science. Over a decade, it has inspired over 1,500 publications and advanced open-science infrastructure through rich metadata, benchmarking, and op
Published Jul 1, 202513 citationsPaper ↗
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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.