The authors reflect on 10 years of OpenML, the open-source platform that turns machine-learning experiments into open, linked, and reusable knowledge. They present the current state of the OpenML ecosystem and its design and assess how community-curated datasets, tasks, and benchmark suites have powered more than 1,500 studies, advanced machine-learning research, and improved reproducibility. Lastly, they share lessons from a decade of building, maintaining, and expanding OpenML to guide the future of open-science infrastructure for machine learning.