Large-scale machine learning at Facebook: Implications of platform design on developer productivity
AI plays a key role in achieving Facebook’s mission of connecting people and building communities. Nearly every visible product is powered by machine learning algorithms at its core, from delivering relevant content to making the platform safe. Scaling these products to billions of global users has uncovered many fascinating challenges at every layer in the systems stack, such as uncovering computational and storage bottlenecks, making the ML platform efficient and productive for the ML engineers and tackling critical challenges such as privacy and environmental sustainability.
Kim Hazelwood and Mohamed Fawzy offer an end-to-end look at how applied ML has continued to change the landscape of the platforms and infrastructure at Facebook.
What you'll learn
- Learn how Facebook uses ML and AI
Kim Hazelwood is a senior engineering manager leading the AI Infrastructure Foundation and AI Infrastructure Research efforts at Facebook, where the focus is designing and optimizing efficiency hardware and software systems for Facebook’s many applied machine learning-based products and services. Previously, Kim was a tenured associate professor at the University of Virginia, a software engineer at Google, and director of systems research at Yahoo Labs. She’s been recognized with an NSF CAREER Award, the Anita Borg Early Career Award, the MIT Technology Review Top 35 Innovators under 35 Award, and the ACM SIGPLAN 10-Year Test of Time Award. She serves on the board of directors of CRA and has authored over 50 conference papers and one book. She holds a PhD in computer science from Harvard University.
Mohamed Fawzy is senior manager and tech lead at Facebook. In his six years at the company, he’s worked on its distributed storage system and was part of the team that developed cold storage, Facebook’s exabyte archiver storage system that keeps your memories safe. More recently, he started the Distributed Training Group to build large-scale distributed training infrastructure for deep learning.
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