Accelerate Data Science End to End With GPUs and RAPIDS
Accelerate Data Science End to End With GPUs and RAPIDS

Abstract: 

See how RAPIDS and the open-source ecosystem are advancing data science. In this session, we will explore RAPIDS, the open-source data science platform incubated by NVIDIA. Come learn how to get started leveraging these open-source libraries for faster performance and easier development on GPUs. This includes the core libraries of RAPIDS (cuDF for data frames, cuML for machine learning, and cuGraph for graph analytics), BlazingSQL (a SQL engine built on top of cuDF), Nuclio (a Kubernetes serverless library with GPU support), Numba (a high-performance python just in time compiler), and Dask (a python distributed scheduler). See the latest engineering work new release features (including, benchmarks, roadmaps, and demos), and how all these libraries come together to make data science faster and easier than ever. Finally, hear how customers are leveraging RAPIDS in production, benefiting from early adoption, and outperforming CPU equivalents.

Bio: 

Joshua Patterson is Director of Engineering of RAPIDS, the NVIDIA open-source Data Science Platform. Previously, he spent four years as a Data Science Principal at Accenture Technology Labs, contributing to efforts to build a next-generation cyber defense platform. Patterson spent a year as a White House President Innovation Fellow, working on applying technology to the highest levels of government. He recently completed two and a half years serving on the Census Scientific Advisory Committee. His passions are graph analytics, machine learning, large-scale system design, and storytelling with interactive data visualizations. Patterson holds a Bachelor of Arts in economics from the University of North Carolina at Chapel Hill, and a Master of Arts degree in economics from the University of South Carolina's Moore School of Business.