Fundamentals of Accelerated Data Science

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Course Overview

In this workshop, you’ll learn how to build and execute end-to-end GPU-accelerated data science workflows that enable you to quickly explore, iterate, and get your work into production. Using the RAPIDS™-accelerated data science libraries, you’ll 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.

Who Should Attend

Developers

Course Objectives

• 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

Course Outline