“Democratizing ML” is a hot topic these days - particularly in industry. Efficiency, composability and accessibility of machine learning technology are active areas of investment for many research and product groups. Unfortunately, while machine learning has the potential to fundamentally improve how software is constructed, opportunities to leverage machine learning to improve more conventional developer tools (languages, compilers, and IDEs for example) have largely gone untapped. 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. I’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.

John Myles White is the engineering manager for Facebook’s Better Python team, which is focused on improving developer experiences for Facebook and Instagram’s engineers. Before focusing on Python, John helped design Facebook’s experimentation and survey tools. Prior to coming to Facebook, John was one of the core developers of the Julia programming language for which he built many of the core data science libraries. John also has a PhD in psychology and neuroscience from Princeton, where he developed mathematical models of human decision-making.