

#304 Accelerating Data Science with Nick Becker, Technical Product Manager at NVIDIA & Dan Hannah, Associate Director at SES AI
30 snips Jun 2, 2025
Nick Becker, a Technical Product Manager at NVIDIA, and Dan Hannah, Associate Director at SES AI, dive into how GPU acceleration is revolutionizing data science in AI and materials discovery. They discuss the importance of transitioning from CPU to GPU for efficiency and speed, especially in battery technology. Topics include the role of machine learning in developing new materials, leveraging NVIDIA's advanced frameworks, and the impact of dimensionality reduction techniques like UMAP in identifying viable molecular candidates for innovation.
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GPUs Empower Parallel Data Science
- GPUs excel at parallel processing, making them valuable for AI, data frames, clustering, and graph analytics.
- This broad applicability explains why GPUs accelerate various data science tasks beyond just neural networks.
Hunting Electrolytes with AI
- Dan Hannah's team explores new battery electrolytes via AI models and computational chemistry.
- They aim to discover novel molecules to improve battery performance by simulating billions of candidates.
Switch to GPUs Seamlessly
- When datasets grow large, assess if GPU acceleration benefits your workflow.
- Installing NVIDIA Rapids allows seamless switching to GPU with no code changes, boosting efficiency.