The paper introduces OpenML, a platform designed to facilitate networked science in machine learning. OpenML allows researchers to share and organize detailed data, code, and experimental results, enabling more effective collaboration and addressing the challenges of reproducibility and scalability in machine learning research. The authors discuss the benefits of networked science, highlighting how it can lead to serendipitous discoveries, dynamic division of labor, and increased collaboration. They also outline the design principles of OpenML, including its ability to handle large-scale data, support various task types, and integrate with popular machine learning tools. The paper concludes by detailing the benefits of OpenML for individual scientists, students, and the broader machine learning community, emphasizing its role in enhancing productivity, knowledge, and reputation.The paper introduces OpenML, a platform designed to facilitate networked science in machine learning. OpenML allows researchers to share and organize detailed data, code, and experimental results, enabling more effective collaboration and addressing the challenges of reproducibility and scalability in machine learning research. The authors discuss the benefits of networked science, highlighting how it can lead to serendipitous discoveries, dynamic division of labor, and increased collaboration. They also outline the design principles of OpenML, including its ability to handle large-scale data, support various task types, and integrate with popular machine learning tools. The paper concludes by detailing the benefits of OpenML for individual scientists, students, and the broader machine learning community, emphasizing its role in enhancing productivity, knowledge, and reputation.