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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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Abstract:
Enterprises are eager to take advantage of artificial intelligence technologies such as deep learning to introduce new services and enhance insights from company data. As data science teams move past proof of concept and begin to operationalize deep learning, it becomes necessary to focus on the creation of a complete data architecture that eliminates bottlenecks to facilitate faster model iteration. Designing a data architecture involves thinking holistically about the deep learning pipeline, from data ingest and edge analytics, to data prep and training in the core data center, to archiving in the cloud. It is necessary to understand performance requirements and data services needed, but one should also consider future extensibility and supportability as deep learning hardware and cloud approaches evolve over time. This session will examine all the factors involved in the architecture of a deep learning pipeline, focusing in on data management and the hybrid cloud. Careful infrastructure planning can smooth the flow of data through your deep learning pipeline, lead to faster time to deployment, and thus maximum competitive differentiation.
Enterprises are eager to take advantage of artificial intelligence technologies such as deep learning to introduce new services and enhance insights from company data. As data science teams move past proof of concept and begin to operationalize deep learning, it becomes necessary to focus on the creation of a complete data architecture that eliminates bottlenecks to facilitate faster model iteration. Designing a data architecture involves thinking holistically about the deep learning pipeline, from data ingest and edge analytics, to data prep and training in the core data center, to archiving in the cloud. It is necessary to understand performance requirements and data services needed, but one should also consider future extensibility and supportability as deep learning hardware and cloud approaches evolve over time. This session will examine all the factors involved in the architecture of a deep learning pipeline, focusing in on data management and the hybrid cloud. Careful infrastructure planning can smooth the flow of data through your deep learning pipeline, lead to faster time to deployment, and thus maximum competitive differentiation.  Back
 
Topics:
Data Center & Cloud Infrastructure, Performance Optimization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8974
Streaming:
Share:
 
 
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