Fundamentals of Accelerated Data Science
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.
- 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
Developers
Experience with Python, ideally including pandas and NumPy
Overview of objectives, content, and learning outcomes
Using GPUs to accelerate data preprocessing and transformation
Applying GPU acceleration to train and optimize ML models
Hands-on project applying GPU-accelerated techniques to real-world data
Knowledge check and interactive discussion
Recap of key concepts and closing remarks