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GTC ON-DEMAND

Presentation
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Abstract:
We'll discuss the challenges uncovered in AI and deep learning workloads, discuss the most efficient approaches to handling data, and examine use cases in autonomous vehicles, retail, health care, finance, and other markets. Our talk will cover the complete requirements of the data life cycle including initial acquisition, processing, inference, long-term storage, and driving data back into the field to sustain ever-growing processes of improvement. As the data landscape evolves with emerging requirements, the relationship between compute and data is undergoing a fundamental transition. We will provide examples of data life cycles in production triggering diverse architectures from turnkey reference systems with DGX and DDN A3I to tailor-made solutions.
We'll discuss the challenges uncovered in AI and deep learning workloads, discuss the most efficient approaches to handling data, and examine use cases in autonomous vehicles, retail, health care, finance, and other markets. Our talk will cover the complete requirements of the data life cycle including initial acquisition, processing, inference, long-term storage, and driving data back into the field to sustain ever-growing processes of improvement. As the data landscape evolves with emerging requirements, the relationship between compute and data is undergoing a fundamental transition. We will provide examples of data life cycles in production triggering diverse architectures from turnkey reference systems with DGX and DDN A3I to tailor-made solutions.  Back
 
Topics:
HPC and AI, HPC and Supercomputing
Type:
Sponsored Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9983
Streaming:
Share:
 
Abstract:

Analytics and AI present a serious challenge to businesses in developing new expertise and transforming data architectures from enterprise-class to AI-ready. AI workloads demand a different approach to managing the data lifecycle. The new AI datacenter must be optimized for ingesting, storing, transforming and optimizing data and feeding that data through hyper-intensive analytics workflows and ultimately, extracting value. Ensuring the maximum value of your investment into GPU platforms like NVIDIA's DGX-1 requires careful planning. Learn how to architect and deploy data platforms with robust and balanced performance for all I/O patterns.

Analytics and AI present a serious challenge to businesses in developing new expertise and transforming data architectures from enterprise-class to AI-ready. AI workloads demand a different approach to managing the data lifecycle. The new AI datacenter must be optimized for ingesting, storing, transforming and optimizing data and feeding that data through hyper-intensive analytics workflows and ultimately, extracting value. Ensuring the maximum value of your investment into GPU platforms like NVIDIA's DGX-1 requires careful planning. Learn how to architect and deploy data platforms with robust and balanced performance for all I/O patterns.

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Topics:
Artificial Intelligence and Deep Learning
Type:
Talk
Event:
GTC Europe
Year:
2018
Session ID:
E8489
Streaming:
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Abstract:
Analytics and AI present a serious challenge to businesses in developing new expertise and transforming data architectures from enterprise-class to AI-ready. AI workloads demand a different approach to managing the data lifecycle. The new AI datacenter must be optimized for ingesting, storing, transforming and optimizing data and feeding that data through hyper-intensive analytics workflows and ultimately, extracting value. Failing fast during experimentation, and scaling successful models quickly to production is vital. Learn how to architect and deploy data platforms with robust and balanced performance for all I/O patterns.
Analytics and AI present a serious challenge to businesses in developing new expertise and transforming data architectures from enterprise-class to AI-ready. AI workloads demand a different approach to managing the data lifecycle. The new AI datacenter must be optimized for ingesting, storing, transforming and optimizing data and feeding that data through hyper-intensive analytics workflows and ultimately, extracting value. Failing fast during experimentation, and scaling successful models quickly to production is vital. Learn how to architect and deploy data platforms with robust and balanced performance for all I/O patterns.  Back
 
Topics:
Data Center & Cloud Infrastructure, Performance Optimization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8975
Streaming:
Share:
 
 
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