Workshop / Overview

The workshop will teach developers how to build and execute end-to-end GPU accelerated data science workflows that enable to quickly explore, iterate, and productize. Using the RAPIDS data science libraries, developers will be able to use pandas, scikit-learn-like APIs to accelerate a wide variety of GPU-accelerated machine learning algorithms, including XGBoost, K-means, DBSCAN, and logistic regression, etc. to perform data analysis at scale. 

Workshop / Outcome


  • Learn how to implement GPU-accelerated data preparation and feature extraction
  • Apply a broad spectrum of GPU-accelerated machine learning tasks and execute GPU-accelerated graph analysis with RAPIDS 
  • Understand how to leverage GPUs for everyday and advanced data science tasks, and how to use RAPIDS to harness the GPU parallel computing power. 

Workshop / Difficulty

Intermediate level

Workshop / Prerequisites

  • Python programming 
  • Basic understanding of data science libraries like numpy, pandas, scikit-learn 

Please perform the following steps:

  1. Review the agenda, prerequisites, and suggested material for the workshop (as detailed in the course datasheet). This is an important step to properly prepare for the workshop.
  2. Create or log into your NVIDIA Developer Program account. This account will provide you with access to all of the DLI training materials during and after the workshop.
  3. Visit websocketstest.courses.nvidia.com and make sure all three test steps are checked “Yes.” This will test the ability for your system to access and deliver the training contents. If you encounter issues, try updating your browser. Note: Only Chrome and Firefox are supported.
  4. Check your bandwidth. 1 Mbps downstream is required and 5 Mbps is recommended. This will ensure consistent streaming of audio/video during the workshop to avoid glitches and delays.



Track / Co-organizers

Manal Jalloul

Certified Instructor and University Ambassador, NVIDIA DLI. Co-founder and CEO, AI Lab

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