Machine learning systems are being widely deployed in Facebook's datacenter fleets and over billions of edge devices. This talk presents the infrastructure implications of Facebook products and workloads that leverage machine learning and highlights key system design challenges when scaling machine learning solutions to billions of people. What are key similarities and differences between cloud and edge infrastructure? The talk will then conclude with system research directions for deploying machine learning at scale.

Carole-Jean Wu is a Research Scientist at Facebook's AI Infrastructure Research. She is also a tenured Associate Professor in Arizona State University. Carole-Jean's research focuses in Computer and System Architectures. More recently, her research has pivoted into designing systems for machine learning. She is the leading author of "Machine Learning at Facebook: Understanding Inference at the Edge" that presents unique design challenges faced when deploying ML solutions at scale to the edge, from over billions of smartphones to Facebook's virtual reality platforms. Carole-Jean received her M.A. and Ph.D. from Princeton and B.Sc. from Cornell.