Accelerate your data science with RAPIDS.AI

RAPIDS (https://rapids.ai/) is a collection of open source libraries for running data science pipelines end-to-end on the GPU. Its APIs follow familiar libraries like pandas, Scikit-Learn, NetworkX, SciPy signal, and XGBoost, but utilize optimized NVIDIA® CUDA® primitives and high-bandwidth GPU memory under the hood. Join us as we explore the library and common use cases including feature engineering and preprocessing sped up by 10x over pandas, and XGBoost sped up 4-7x over CPU. We'll also cover how RAPIDS interacts with the major deep learning libraries, allowing for on GPU preprocessing and feature engineering for your deep learning workflows.

About the speaker:
Even Oldridge is a Senior Applied Research Scientist at NVIDIA working at the intersection of deep learning and tabular data on the RAPIDS.AI team. He has a Ph.D. in Computational Photography and an M.A.Sc. in Programmable Hardware from the University of British Columbia. Prior to joining NVIDIA he worked as a Principal Data Scientist at Realtor.com, leading their search and ranking research efforts.

About the venue:
Northeastern’s downtown Vancouver campus currently offers the Master of Science in Computer Science program with focused specializations in Artificial Intelligence, Human-Computer Interaction (HCI) and Software Engineering. Tailored to Vancouver’s needs as a world innovation hub, the degree programs offer working professionals a clear path to advancing their careers. Northeastern University's world-renowned experiential approach empowers students, faculty, alumni, and partners to create impact far beyond the confines of discipline, degree, and campus.