From inception to insight: Accelerating AI productivity with GPUs (sponsored by Dell Technologies)
Data scientists and machine learning engineers need the flexibility to work in multiple environments without wasting precious time configuring hardware and software and modifying code.
Ramesh Radhakrishnan and John Zedlewski walk you through deploying a simple set of technologies including the Rapid suite of open source software libraries and APIs for executing end-to-end data science and analytics pipelines entirely on GPUs with NVIDIA GPU cloud containers across a combination of laptops, workstations, and enterprise-class Kubernetes clusters without having to modify your code or compromise on performance.
What you'll learn
- Discover a simple set of technologies that can provide the flexibility to easily work in multiple environments
Ramesh Radhakrishnan is a distinguished engineer on the CTO team in the Server and Infrastructure Solutions Group at Dell, where he drives technology strategy for advanced analytics and machine learning and deep learning. At Dell, he’s led technology strategy and architecture in the areas of Energy Efficient uServer Architecture (ARM and Xeon-D), Microsoft hybrid cloud, and workload-optimized systems. He’s a member of the Dell Patent Committee and has 15 published patents.
John Zedlewski is a director of GPU-accelerated machine learning on the NVIDIA Rapids team. Previously, he worked on deep learning for self-driving cars at NVIDIA, deep learning for radiology at Enlitic, and machine learning for structured healthcare data at Castlight. He has an MA/ABD in economics from Harvard with a focus in computational econometrics and an AB in computer science from Princeton.
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