There is much to discover in the big, actually astronomically big, datasets that are (and will be) available. The challenge is how to effectively mine these massive datasets.
In this machine learning webinar attendees will learn how CANFAR (the Canadian Advanced Network for Astronomical Research) is using Skytree’s high performance and scalable machine learning system in the cloud. The combination enables astronomers to focus on their analyses rather than having to waste time implementing scalable complex algorithms and architecting the infrastructure to handle the massive datasets involved.
CANFAR is designed with usability in mind. Implemented as a virtual machine (VM), users can deploy their existing desktop code to the CANFAR cloud – delivering instant scalability (replication of the VM as required), without additional development.
Dr. Gray is CTO at Skytree and Associate Professor in the College of Computing at Georgia Tech. His work has focused on algorithmic techniques for making machine learning tractable on massive datasets. He began working with large-scale scientific data in 1993 at NASA’s Jet Propulsion Laboratory in its Machine Learning Systems Group. He recently served on the National Academy of Sciences Committee on the Analysis of Massive Data, as a Kavli Scholar, and a Berkeley Simons Fellow, and is a frequent advisor and speaker on the topic of machine learning on big data in academia, science, and industry.