Joseph Gonzalez is a founding member of the UC Berkeley Sky Computing Lab and the RISE Lab where he studies the design of future cloud architectures and machine learning systems. He is also a member of the Berkeley AI Research Group where he works on new neural architectures for computer vision, natural language processing, and robotics. Gonzalez's research addresses problems in neural network design, efficient inference, computer vision, prediction serving, autonomous vehicles, graph analytics, and distributed systems. Building on his research, Gonzalez co-founded Aqueduct to commercialize a radically simpler production data science platform. Finally, Gonzalez helped develop the Data Science program at UC Berkeley and co-created Data100 which is now taught to over 1500 students a semester.

Prior to joining Berkeley, Gonzalez co-founded Turi Inc (formerly GraphLab) based on his thesis work and created the GraphX project (now part of Apache Spark). Gonzalez's innovative work has earned him significant recognition, including the Okawa Research Grant, NSF Early Career Award, and the NSF Expedition Award.

Abstract: Over the past decade, I have worked on projects ranging from the early graph processing frameworks (GraphLab) and distributed data processing frameworks (Apache Spark) to more recent large-scale systems for machine learning and data processing (Clipper, Ray, CloudBurst, and Modin). I have seen the rise (and fall) of various ML systems and the importance of data and data systems driving the field forward. In this talk, I will present the evolution of prediction serving systems, what we got wrong, and where things are headed. I will introduce our new work on feature stores and try to explain why they exist in the first place. I will then conclude by presenting a new vision for the future of cloud computing, one in which we attempt to defy data gravity and disrupt the economics of the cloud.

Martin Maas is a Staff Research Scientist at Google Research and part of the Brain team. His research interests are in language runtimes, computer architecture, systems, and machine learning, with a focus on applying machine learning to systems problems. Before joining Google, Martin completed his PhD in Computer Science at the University of California at Berkeley, where he worked on hardware support for managed languages and architectural support for memory-trace obliviousness.

Abstract: Machine learning has the potential to significantly improve computer systems. While recent research in this area has shown great promise, not all problems are equally well-suited for applying ML techniques, and some remaining challenges have prevented wider adoption of ML techniques in systems. In this talk, I will introduce a taxonomy to classify machine learning for systems approaches, discuss how to identify cases that are a good fit for machine learning, and lay out a longer-term vision of how we can improve systems using ML techniques, ranging from computer architecture to language runtimes.