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.