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GTC ON-DEMAND

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Abstract:
How do you accelerate innovation and deliver faster time-to-value for your AI initiative, while ensuring enterprise-grade security and high performance? How do you provide easy access to the tools and data your data science teams need for large-scale distributed ML/DL with greater agility for rapid prototyping and iteration? We'll discuss practical examples and lessons learned from GPU-Accelerated ML/DL use cases in financial services, healthcare, and other industries. Learn how to quickly deploy containerized multi-node environments for TensorFlow and other ML DL tools in a multi-tenant architecture with a shared pool of resources using GPUs.
How do you accelerate innovation and deliver faster time-to-value for your AI initiative, while ensuring enterprise-grade security and high performance? How do you provide easy access to the tools and data your data science teams need for large-scale distributed ML/DL with greater agility for rapid prototyping and iteration? We'll discuss practical examples and lessons learned from GPU-Accelerated ML/DL use cases in financial services, healthcare, and other industries. Learn how to quickly deploy containerized multi-node environments for TensorFlow and other ML DL tools in a multi-tenant architecture with a shared pool of resources using GPUs.  Back
 
Topics:
AI Application, Deployment & Inference, AI & Deep Learning Business Track (High Level)
Type:
Sponsored Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9982
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