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GTC On-Demand

Presentation
Media
Abstract:

This customer panel brings together AI implementers who have deployed deep learning at scale. The discussion will focus on specific technical challenges they faced, solution design considerations, and best practices learned from implementing their respective solutions.

This customer panel brings together AI implementers who have deployed deep learning at scale. The discussion will focus on specific technical challenges they faced, solution design considerations, and best practices learned from implementing their respective solutions.

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Topics:
AI and DL Business Track (high level), Data Center and Cloud Infrastructure, Deep Learning and AI Frameworks
Type:
Panel
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9121
Streaming:
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Abstract:
Algorithmic advancements and new research capabilities frequently overshadow the infrastructure that enables that research and serves it to customers in production applications. Having a solid infrastructure for real world machine learning often ends up being the biggest determinant of success and is an exciting area of research and engineering in its own right. These environments are what allow brilliant algorithms to deliver value at scale. We'll detail how Capital One has designed its GPU computing environment to accelerate machine learning efforts and outline the services used, the framework to leverage those services, and the engineering practices used to develop and deploy well-governed, accurate models to high-volume production environments. Beyond production deployments, we'll discuss how this infrastructure performs large-scale testing of models and frameworks to explore the interactions of deep learning tools like MXNet and TensorFlow. We'll also discuss the practices that enabled Capital One to hire a high-performing team in this incredibly desirable field.
Algorithmic advancements and new research capabilities frequently overshadow the infrastructure that enables that research and serves it to customers in production applications. Having a solid infrastructure for real world machine learning often ends up being the biggest determinant of success and is an exciting area of research and engineering in its own right. These environments are what allow brilliant algorithms to deliver value at scale. We'll detail how Capital One has designed its GPU computing environment to accelerate machine learning efforts and outline the services used, the framework to leverage those services, and the engineering practices used to develop and deploy well-governed, accurate models to high-volume production environments. Beyond production deployments, we'll discuss how this infrastructure performs large-scale testing of models and frameworks to explore the interactions of deep learning tools like MXNet and TensorFlow. We'll also discuss the practices that enabled Capital One to hire a high-performing team in this incredibly desirable field.  Back
 
Topics:
Accelerated Analytics, Tools and Libraries, Data Center and Cloud Infrastructure, Finance
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8843
Streaming:
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