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Abstract:
As research and clinical healthcare organizations formulate and Implement AI strategies, a crucial component is planning for the proper AI compute infrastructure. This talk will address compute infrastructure planning in healthcare settings, including reference architectures and best practices that NVIDIA has developed based on our internal AI supercomputer, as well as examples of successful AI deployments by leading healthcare organizations.
As research and clinical healthcare organizations formulate and Implement AI strategies, a crucial component is planning for the proper AI compute infrastructure. This talk will address compute infrastructure planning in healthcare settings, including reference architectures and best practices that NVIDIA has developed based on our internal AI supercomputer, as well as examples of successful AI deployments by leading healthcare organizations.  Back
 
Topics:
AI in Healthcare
Type:
Talk
Event:
GTC Washington D.C.
Year:
2018
Session ID:
DC8155
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Abstract:
We'll introduce deep learning infrastructure for building and maintaining autonomous vehicles. This includes techniques for managing the lifecycle of deep learning models from definition, training and deployment to reloading and life-long learning. DNN autocurates and pre-labels data in the loop. Given data, it finds the best run-time optimized deep learning models. With these methodologies, one takes data from the application and feeds DL predictors to it. This infrastructure is divided into multiple tiers and is modular, with each of the modules containerized to lower infrastructures like GPU-based cloud infrastructure.
We'll introduce deep learning infrastructure for building and maintaining autonomous vehicles. This includes techniques for managing the lifecycle of deep learning models from definition, training and deployment to reloading and life-long learning. DNN autocurates and pre-labels data in the loop. Given data, it finds the best run-time optimized deep learning models. With these methodologies, one takes data from the application and feeds DL predictors to it. This infrastructure is divided into multiple tiers and is modular, with each of the modules containerized to lower infrastructures like GPU-based cloud infrastructure.  Back
 
Topics:
Autonomous Vehicles
Type:
Talk
Event:
GTC Israel
Year:
2018
Session ID:
SIL8114
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Abstract:

We''ll introduce deep learning infrastructure for building and maintaining autonomous vehicles, including techniques for managing the lifecycle of deep learning models, from definition, training and deployment to reloading and life-long learning. DNN autocurates and pre-labels data in the loop. Given data, it finds the best run-time optimized deep learning models. Training scales with data size beyond multi-nodes. With these methodologies, one takes only data from the application and feeds DL predictors to it. This infrastructure is divided into multiple tiers and is modular, with each of the modules containerized to lower infrastructures like GPU-based cloud infrastructure.

We''ll introduce deep learning infrastructure for building and maintaining autonomous vehicles, including techniques for managing the lifecycle of deep learning models, from definition, training and deployment to reloading and life-long learning. DNN autocurates and pre-labels data in the loop. Given data, it finds the best run-time optimized deep learning models. Training scales with data size beyond multi-nodes. With these methodologies, one takes only data from the application and feeds DL predictors to it. This infrastructure is divided into multiple tiers and is modular, with each of the modules containerized to lower infrastructures like GPU-based cloud infrastructure.

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Topics:
AI Application Deployment and Inference, Data Center and Cloud Infrastructure, Autonomous Vehicles, Autonomous Machines
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8531
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