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GTC On-Demand

AI Application Deployment and Inference
Presentation
Media
Deep Learning Implementers Panel: Field Insights for Accelerating Deep Learning Performance, Productivity and Scale

This customer panel brings together A.I. implementers who have deployed deep learning at scale using NVIDIA DGX Systems. We'll focus on specific technical challenges we faced, solution design considerations, and best practices learned from implementing our respective solutions. Attendees will gain insights such as: 1) how to set up your deep learning project for success by matching the right hardware and software platform options to your use case and operational needs; 2) how to design your architecture to overcome unnecessary bottlenecks that inhibit scalable training performance; and 3) how to build an end-to-end deep learning workflow that enables productive experimentation, training at scale, and model refinement.

This customer panel brings together A.I. implementers who have deployed deep learning at scale using NVIDIA DGX Systems. We'll focus on specific technical challenges we faced, solution design considerations, and best practices learned from implementing our respective solutions. Attendees will gain insights such as: 1) how to set up your deep learning project for success by matching the right hardware and software platform options to your use case and operational needs; 2) how to design your architecture to overcome unnecessary bottlenecks that inhibit scalable training performance; and 3) how to build an end-to-end deep learning workflow that enables productive experimentation, training at scale, and model refinement.

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Keywords:
AI Application Deployment and Inference, AI and DL Business Track (high level), Data Center and Cloud Infrastructure, AI for Business, HPC and Supercomputing, GTC Silicon Valley 2018 - ID S8194
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AI in Healthcare
Presentation
Media
Scalable Development and Deployment of Machine Learning Algorithms in a Clinical Setting
The development and deployment of machine learning models is fraught with complexity. These challenges are exacerbated in a clinical setting where data volume is low, instances can be large, annotation requires domain expertise, and deployment often involves integration into black-box systems. In this talk, we will discuss how these challenges affect the process of applying machine learning to medical imaging and highlight solutions which enable the integration of ML into a radiology reading room.
The development and deployment of machine learning models is fraught with complexity. These challenges are exacerbated in a clinical setting where data volume is low, instances can be large, annotation requires domain expertise, and deployment often involves integration into black-box systems. In this talk, we will discuss how these challenges affect the process of applying machine learning to medical imaging and highlight solutions which enable the integration of ML into a radiology reading room.  Back
 
Keywords:
AI in Healthcare, Deep Learning and AI, GTC Washington D.C. 2018 - ID DC8157
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