GPUs and GPU platforms have been responsible for the dramatic advancement of deep learning and other neural net methods in the past several years. At the same time, traditional machine learning workloads, which comprise the majority of business use cases, continue to be written in Python with heavy reliance on a combination of single-threaded tools (e.g., pandas and scikit-learn) or large, multi-CPU distributed solutions (e.g., Spark and PySpark).
RAPIDS is the next big step in data science, combining the ease of use of common APIs and the power and scalability of GPUs. Bartley Richardson and Joshua Patterson offer an overview of RAPIDS and and explore cuDF, cuGraph, and cuML—a trio of RAPIDS tools that enable data scientists to work with data in a familiar interface and apply graph analytics and traditional machine learning techniques.
Bartley Richardson is a senior data scientist on the AI infrastructure team at NVIDIA. Bartley’s focus at NVIDIA is the research and application of GPU-accelerated methods that can help solve today’s information security and cybersecurity challenges. Previously, Bartley was a technical lead and performer on multiple DARPA research projects, where he applied data science and machine learning algorithms at scale to solve large cybersecurity problems. He was also the principal investigator of an internet of things research project focused on applying machine and deep learning techniques to large amounts of IoT data to provide intelligence value relating to form, function, and pattern of life. His primary research areas involve NLP and sequence-based methods applied to cyber network datasets as well as cross-domain applications of machine and deep learning solutions to tackle the growing number of cybersecurity threats. He loves using data and visualizations to tell stories and help make complex concepts more relatable. Bartley holds a PhD in computer science and engineering from the University of Cincinnati with a focus on loosely and unstructured query optimization and a BS in computer engineering with a focus on software design and AI.
Joshua Patterson is a director of AI infrastructure at NVIDIA leading engineering for RAPIDS.AI. Previously, Josh was a White House Presidential Innovation Fellow and worked with leading experts across public sector, private sector, and academia to build a next-generation cyberdefense platform. His current passions are graph analytics, machine learning, and large-scale system design. Josh loves storytelling with data and creating interactive data visualizations. He holds a BA in economics from the University of North Carolina at Chapel Hill and an MA in economics from the University of South Carolina Moore School of Business.
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