Unlocking Machine Learning Performance and Productivity: XGBoost on GPUs
Traditional approaches to machine learning have stifled developers with a constrained workflow that slows iteration speed and limits model accuracy. For the first time, data scientists now have the tools they need to run the entire data science pipeline, supercharged with GPUs.
In this webinar, we’ll show you how to speed up machine learning with GPU-accelerated XGBoost, for up to 50x faster workflows compared with CPU-only systems.
In this webinar, we’ll show you how to speed up machine learning with GPU-accelerated XGBoost, for up to 50x faster workflows compared with CPU-only systems.
Watch this deep dive into GPU-accelerated machine learning, to explore:
- Why XGBoost is currently the most popular and versatile machine learning algorithm
- The benefits of running XGBoost on GPUs vs CPU, and how to get started
- How to effortlessly scale up workflows with greater speed leveraging RAPIDS GPU-accelerated XGBoost, with Pandas-like ease of use
- How to tame terabyte size datasets using multi-GPU, multi-node configurations.
ONDEMAND WEBINAR REGISTRATION
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Presented By
Paul Mahler
Senior Data Scientist and Technical Product Manager for Machine Learning at NVIDIA
Paul Mahler is a Senior Data Scientist and Technical Product Manager for Machine Learning at NVIDIA in Boulder, CO. At NVIDIA, Paul’s focus has been on building tools that accelerate data science workflows by leveraging the power of GPU technology. Prior to NVIDIA, Paul worked as a data scientist at a Fin Tech start-up in San Francisco, delivering predictive models around risk and credit worthiness.
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