Software is eating the world, but ML is going to eat software

“Democratizing ML” is a hot topic these days; efficiency, composability and accessibility of machine learning technology are active areas of investment for many research and product groups. While machine learning has the potential to fundamentally change how software is constructed (Software 1.0), there are also great opportunities to leverage machine learning to improve more conventional (Software 1.0) developer tools, compilers, IDEs, and continuous integration systems. Our Developer Infrastructure team is on a mission to fundamentally rethink and retool Facebook’s developer toolchain by applying machine learning at every layer in our stack to support both Software 2.0 as well as Software 1.0. Our goal is to make our developers more productive, and our processes and infrastructure more efficient, by deeply integrating ML into our programming languages and developer tools in novel ways. This talk will detail the work our team has done to improve developer efficiency and resource utilization at Facebook - from updating the Hack programming language to support probabilistic programming techniques, to developing a new suite of AI-driven developer tools. We’ll describe the lessons we’ve learned along the way, as well as future opportunities we see to optimize or auto-tune other common pieces of developer infrastructure.