OpenML: Insights from 10 years and more than a thousand papers.

Area of Science:

• Machine Learning
• Open Science
• Data Science

Background:

• OpenML is an open-source platform facilitating machine learning evaluation.
• It enables sharing of datasets, tasks, workflows, and model evaluations.
• The platform fosters a collaborative ecosystem for AI research and development.

Purpose of the Study:

• To detail the impact of OpenML over the past decade.
• To share lessons learned from building and maintaining the platform.
• To outline future directions for open-science infrastructure in machine learning.

Main Methods:

• Analysis of platform usage and citation data.
• Review of lessons learned in platform development and expansion.
• Description of ongoing efforts and future vision for OpenML.

Main Results:

• OpenML has inspired over 1,500 publications across diverse scientific fields.
• Rich metadata, collaborative benchmarking, and open interfaces have enhanced research and interoperability.
• The platform has significantly contributed to reproducible science and AI advancement.

Conclusions:

• OpenML has a substantial impact on machine learning research and education.
• Lessons learned emphasize the value of metadata, collaboration, and open interfaces.
• Future efforts aim to expand capabilities and integrate with other platforms for broader open-science infrastructure.