CPUs vs GPUs for Your End-to-End Data Science Workflows

We know that model training and inference is faster on GPU, but the slowest, most draining part of a data scientist’s typical day is processing data into the structure the model requires. Can GPUs help us with this challenge as well? To answer this question, we’ll compare cycles per second and costs of CPU vs GPU, look at speed gains with the Rapids.ai framework, and calculate ROI as our AI/ML models scale. Join us as we lay out the compelling case for why you should be using GPUs for your end-to-end data science workflows, including ETL jobs.

Join us and see:
• A typical data science day with and without GPU acceleration
• How to easily convert your code to take advantage of GPU-accelerated libraries
• How to calculate cost savings of GPU clusters vs large CPU clusters