GPU-Accelerated Data Science

• Implement GPU-accelerated data preparation and feature extraction using cuDF and Apache Arrow data frames.
• Apply GPU-accelerated machine learning using XGBoost and cuML.
• Execute GPU-accelerated graph analysis using cuGraph.
• Work with single and multiple GPUs for large datasets.
• Build end-to-end GPU-accelerated data science workflows.
• Analyze massive datasets using RAPIDS.

Module 1: Introduction

• Meet the instructor.
• Create an account.

Module 2: GPU-Accelerated Data Manipulation

• Ingest and prepare several datasets, including datasets larger than available memory.
• Read data directly to single and multiple GPUs with cuDF and Dask cuDF.
• Prepare population, road network, and clinic information for machine learning tasks on the GPU with cuDF.

Module 3: GPU-Accelerated Machine Learning

• Apply essential machine learning techniques to prepared datasets.
• Use supervised and unsupervised GPU-accelerated algorithms with cuML.
• Train XGBoost models with Dask on multiple GPUs.
• Create and analyze graph data on the GPU with cuGraph.

Module 4: Project: Data Analysis to Save the UK

• Use RAPIDS to integrate multiple massive datasets.
• Perform real-world analysis using population-scale data.
• Pivot and iterate analysis as new data becomes available during the simulated epidemic scenario.

1 Day

Certificate: NVIDIA Deep Learning Institute (DLI) Certificate

Assessment: Workshop assessment

Format: Instructor-led workshop with hands-on assessment

Duration: 60 minutes

Questions: 50–60 questions

• GPU-accelerated data ingestion and preparation using cuDF and Dask cuDF
• Data preparation using population, road network, and clinic datasets
• GPU-accelerated supervised and unsupervised machine learning using cuML
• Multi-GPU XGBoost model training using Dask
• GPU-accelerated graph analytics using cuGraph
• Population-scale data analysis using RAPIDS
• Simulated UK epidemic data-analysis project

• Data Scientists
• Data Analysts
• Data Engineers
• Machine Learning Engineers
• AI DevOps Engineers
• Software Engineers
• Solution Architects
• Deep Learning Performance Engineers
• Researchers

• Experience with Python
• Experience with pandas and NumPy is recommended.