Designing for Scale: Autonomous Vehicle Training Infrastructure
Building a deep learning data center for autonomous vehicle system development means starting with the right approach to AI infrastructure. The system must handle massive volumes of data sets and models, as well as the rapid pace of iteration required to deliver safe self-driving cars.
In this webinar, we’ll explore the key considerations, design principles and solutions to scale an AI infrastructure that meets the demands of developing self-driving systems and other compute-intensive workloads.
- Understand the challenges of designing and deploying AI infrastructure at large scale
- Gain insights from the largest deep learning deployments as well as the NVIDIA SATURNV supercomputer that supports its autonomous systems development team
- Explore reference architecture solutions based on NVIDIA DGX-1 that simplify and accelerate the deployment of deep learning data centers for the most compute-intensive use cases
ONDEMAND WEBINAR REGISTRATION
Presented By
TONY PAIKEDAY
Director of Product Marketing, Deep Learning Systems, NVIDIA
Tony Paikeday is the Director of Product Marketing for deep learning systems at NVIDIA, responsible for the world's first portfolio of AI supercomputers for enterprise - NVIDIA DGX Systems. Tony was previously with VMware, responsible for bringing desktop and application virtualization solutions to market, as well as key enabling technologies including GPU virtualization and software-defined data center. Prior to joining VMware, Tony was at Cisco, building its data center solutions. Prior to Cisco, he held business development roles at Nortel working with enterprise and service provider accounts, after having started his career as a Manufacturing Engineer for Ford Motor Company. Tony holds an engineering degree from the University of Toronto.
Louis Capps
Principal Supercomputing Solutions Architect
Louis Capps is a Principal Supercomputing Solutions Architect at NVIDIA with 27 years of high performance system design and simulation focused on CPU, I/O, interconnect and storage performance for large computational cluster solutions. Louis focuses on novel designs that embrace emerging Supercomputing technologies needed for HPC, Deep Learning, Analytics and Visualization applications with recent advancements in Deep Learning and Data Analytics research. In addition, Louis is inventor on over 30 patents in processor, I/O and energy efficient design and is keenly interested in performance, benchmarking and tuning of advanced architectures. Louis works with government, enterprise and cloud architects on revolutionary designs and initiatives paving the way for exa-scale computational and AI GPU based solutions.
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