Traditional CPU-driven data science workflows can be cumbersome, but with the power of GPUs, researchers can make sense of data quickly to drive business decisions. In this Deep Learning Institute (DLI) workshop, developers will learn how to build and execute end-to-end GPU accelerated data science workflows that enable them to quickly explore, iterate, and get their work into production. Using the RAPIDS accelerated data science libraries, developers will apply a wide variety of GPU-accelerated machine learning algorithms, including XGBoost, cuGRAPH’s single-source shortest path, and cuML’s KNN, DBSCAN, and logistic regression to perform data analysis at scale.

All workshop attendees get access to fully configured, GPU-accelerated servers in the cloud, guidance from a DLI-certified instructor, and the opportunity to network with other developers, data scientists, and researchers. Attendees can earn a certificate to prove subject matter competency and support professional growth. 

• Implement GPU-accelerated data preparation and feature extraction using cuDF and Apache Arrow data frames
• Apply a broad spectrum of GPU-accelerated machine learning tasks using XGBoost and a variety of cuML algorithms 
• Execute GPU-accelerated graph analysis with cuGraph, achieving massive-scale analytics in small amounts of time 
• Rapidly achieve massive-scale graph analytics using cuGraph routines