The Open-Closed Principle of Modern Machine Learning Frameworks

Recent advances in computing technologies and the availability of huge volumes of data have sparked a new machine learning (ML) revolution, where almost every day a new headline touts the demise of human experts by ML models on some task. Open source software development is rumoured to play a significant role in this revolution, with both academics and large corporations such as Google and Microsoft releasing their ML frameworks under an open source license. This paper takes a step back to examine and understand the role of open source development in modern ML, by examining the growth of the open source ML ecosystem on GitHub, its actors, and the adoption of frameworks over time. By mining LinkedIn and Google Scholar profiles, we also examine driving factors behind this growth (paid vs. voluntary contributors), as well as the major players who promote its democratization (companies vs. communities), and the composition of ML development teams (engineers vs. scientists). According to the technology adoption lifecycle, we find that ML is in between the stages of early adoption and early majority. Furthermore, companies are the main drivers behind open source ML, while the majority of development teams are hybrid teams comprising both engineers and professional scientists. The latter correspond to scientists employed by a company, and by far represent the most active profiles in the development of ML applications, which reflects the importance of a scientific background for the development of ML frameworks to complement coding skills. The large influence of cloud computing companies on the development of open source ML frameworks raises the risk of vendor lock-in. These frameworks, while open source, could be optimized for specific commercial cloud offerings.