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GTC ON-DEMAND

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Abstract:

Adoption of machine learning (ML) and deep learning has grown at an unprecedented rate in the last few years. With many applications requiring edge compute as well as a strong demand for hybrid and multi cloud no lock-in solutions, customers demand more flexibility in how models are trained and served. This situation warrants a hybrid cloud approach, enabling ML wherever the data lives with the flexibility to access the cloud when local compute resources are lacking. Google Cloud has collaborated with partners, including NVIDIA and Cisco, to enable a standard open source AI platform, Kubeflow, that's built on Kubernetes to provide a consistent machine learning experience for both on-premise and in the cloud. This platform supports deep integration into NVIDIA stack, including TensorRT and RAPIDS. 

Adoption of machine learning (ML) and deep learning has grown at an unprecedented rate in the last few years. With many applications requiring edge compute as well as a strong demand for hybrid and multi cloud no lock-in solutions, customers demand more flexibility in how models are trained and served. This situation warrants a hybrid cloud approach, enabling ML wherever the data lives with the flexibility to access the cloud when local compute resources are lacking. Google Cloud has collaborated with partners, including NVIDIA and Cisco, to enable a standard open source AI platform, Kubeflow, that's built on Kubernetes to provide a consistent machine learning experience for both on-premise and in the cloud. This platform supports deep integration into NVIDIA stack, including TensorRT and RAPIDS. 

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Topics:
Deep Learning & AI Frameworks, AI Application, Deployment & Inference
Type:
Sponsored Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S91030
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