RAPIDS for GPU-Accelerated Data Science in Healthcare
Learn how RAPIDS, a new open source project, can speed up your data science workflows by bringing the power of GPU acceleration to your end-to-end machine learning (ML) pipeline.
RAPIDS includes a DataFrame manipulation library (cuDF) for data wrangling and feature engineering that’s very similar to Pandas. It also includes an ML library (cuML) that will provide GPU-accelerated versions of the algorithms available in scikit-learn.
By watching this webinar replay, you'll learn to use GPU-accelerated ML to:
- Improve clinical care and operational efficiency;
- Speed up drug discovery and advance precision medicine; and
- Derive insight and value from all your data –structured, free text, imaging, and genomics.
ONDEMAND WEBINAR REGISTRATION
Presented By
Raghav Mani
Developer Relations Manager for Machine Learning in Healthcare, NVIDIA
Raghav Mani manages Developer Relations at NVIDIA, focusing on Machine Learning use cases in Healthcare. Prior to NVIDIA, Raghav worked for 15 years at Epic leading different product & engineering teams. Most recently, he led a team of data scientists and engineers in building Epic’s ML platform on Azure.
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