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GTC ON-DEMAND

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Abstract:
Our talk covers architecture considerations for federating ML and DL data pipelines to exploit GPU acceleration for a seamless tier of data science deployments across edge, core, and cloud. We'll take a look at different requirements, architecture options to meet them, and the resulting benefits to deliver distributed deployments of the data pipeline stages across data ingestion, data prep, training, inference validation, data science, and model serving. We'll also explore a few ways in which customers are deploying these. We will be joined by implementers of stages of the AI and data pipeline today to hear about their deployments and experiences.
Our talk covers architecture considerations for federating ML and DL data pipelines to exploit GPU acceleration for a seamless tier of data science deployments across edge, core, and cloud. We'll take a look at different requirements, architecture options to meet them, and the resulting benefits to deliver distributed deployments of the data pipeline stages across data ingestion, data prep, training, inference validation, data science, and model serving. We'll also explore a few ways in which customers are deploying these. We will be joined by implementers of stages of the AI and data pipeline today to hear about their deployments and experiences.  Back
 
Topics:
Accelerated Data Science, AI Application, Deployment & Inference, Data Center & Cloud Infrastructure
Type:
Sponsored Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9997
Streaming:
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