GPUs and Optimized Containers for AI, Machine Learning, and Deep Learning

Presented by

Chintan Patel, Senior Manager, Product Marketing, NVIDIA

About this talk

NVIDIA NGC software hub offers GPU-Optimized containers for deep learning, machine learning, and HPC applications that simplify the deployment of this software on any GPU-powered systems. This enables data scientists, developers, and researchers to focus on building solutions, gathering insights, and delivering business value. NVIDIA is partnering with Red Hat to make deploying NGC containers and passing through GPU compute resources through Red Hat® OpenShift® even easier. Learn more about: - The NVIDIA accelerated computing platforms that give modern data centers the power to accelerate deep learning, machine learning and HPC workloads. - How to quickly deploy AI frameworks with containers, getting a head start with pre-trained models or model training scripts. - The use of domain specific workflows & Helm charts for the fastest AI implementations, giving you faster time-to-solution. - The GPU Operator that simplifies management of NVIDIA GPU resources in an OpenShift cluster and automates tasks related to bootstrapping GPU nodes. - Running software from NGC on-prem, in the cloud, and edge or using hybrid & multi-cloud deployments. - Enterprise grade support for the entire stack with NGC-Ready systems. About the speaker: Chintan Patel leads Product Marketing for NVIDIA NGC - registry for GPU-optimized AI software. He is responsible for growing awareness and driving adoption of GPU-accelerated software for AI, ML, and HPC. Prior to NVIDIA, he held product marketing and engineering positions at Micrel, Inc. He holds an MBA from Santa Clara University and a bachelor's degree in electrical engineering and computer science from UC Berkeley. This webinar is co-hosted with Nvidia and Red Hat. As a result, both Red Hat & Nvidia are collecting your personal data when you submit such information as part of the registration process above. For information: Red Hat’s: Nvidia:

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