Data Science in 30 Minutes: Accelerating Data Science Workflows on GPUs with Bartley Richardson in New York

Data Science in 30 Minutes: Accelerating Data Science Workflows on GPUs with Bartley Richardson
Join The Data Incubator and Bartley Richardson, Senior Data Scientist and AI Infrastructure Manager at NVIDIA or the next installment of our free online webinar series, Data Science in 30 Minutes: Accelerating Data Science Workflows on GPUs. Abstract:  Data science, and data mining specifically, is the exploration of vast amounts of data to discover actionable knowledge. Today’s data scientists find answers to hard questions using trial and error over multiple iterations, and they employ tools within the Python ecosystem (e.g., Pandas, Numpy, and Scikit-learn) to enhance their solutions and boost productivity. As larger amounts of data are collected and need to be explored, the amount of time spent waiting for results increases. The RAPIDS suite of open source software libraries gives the data scientist the freedom to execute end-to-end analytic pipelines on GPUs, enabling vastly accelerated processing and training on large datasets. By using a familiar DataFrame API that integrates with a variety of ML, DL, and graph algorithms, transitioning from a traditional Python/Pandas environment to RAPIDS is quick, and the speed gained by removing typical serialization costs is large. In this talk, we’ll discuss RAPIDS and its goals, as well as touch on some specific applications of RAPIDS that focus on cybersecurity use cases. About the speakers:  Bartley Richardson, PhD is a Senior Data Scientist and Manager of AI Infrastructure at NVIDIA. His primary focus is the research and application of GPU-accelerated methods (including RAPIDS) that help solve today’s cybersecurity challenges. Prior to NVIDIA, Bartley was a tech lead and performer on multiple DARPA research projects, where he applied data science and machine learning algorithms at-scale to large cyber and information security problems. He was also the principal investigator of an IoT research project which focused on using ML and DL techniques on large amounts of IoT data to provide intelligence value. Michael Li is President of Data Science at Pragmatic Institute, responsible for defining and leading Pragmatic's data courses. Michael founded The Data Incubator in 2014 as a platform for training and placing data scientists. Previously, he worked as a data scientist (Foursquare), Wall Street quant (D.E. Shaw, J.P. Morgan), and a rocket scientist (NASA). He completed his PhD at Princeton as a Hertz fellow and read Part III Maths at Cambridge as a Marshall Scholar. At Foursquare, Michael discovered that his favorite part of the job was teaching and mentoring smart people about data science and so he built up a successful startup focused on what he really loves.