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GTC On-Demand

AI Application Deployment and Inference
Presentation
Media
Deep Learning Implementers Panel: Field Insights for Accelerating Deep Learning Performance, Productivity and Scale

This customer panel brings together A.I. implementers who have deployed deep learning at scale using NVIDIA DGX Systems. We'll focus on specific technical challenges we faced, solution design considerations, and best practices learned from implementing our respective solutions. Attendees will gain insights such as: 1) how to set up your deep learning project for success by matching the right hardware and software platform options to your use case and operational needs; 2) how to design your architecture to overcome unnecessary bottlenecks that inhibit scalable training performance; and 3) how to build an end-to-end deep learning workflow that enables productive experimentation, training at scale, and model refinement.

This customer panel brings together A.I. implementers who have deployed deep learning at scale using NVIDIA DGX Systems. We'll focus on specific technical challenges we faced, solution design considerations, and best practices learned from implementing our respective solutions. Attendees will gain insights such as: 1) how to set up your deep learning project for success by matching the right hardware and software platform options to your use case and operational needs; 2) how to design your architecture to overcome unnecessary bottlenecks that inhibit scalable training performance; and 3) how to build an end-to-end deep learning workflow that enables productive experimentation, training at scale, and model refinement.

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Keywords:
AI Application Deployment and Inference, AI and DL Business Track (high level), Data Center and Cloud Infrastructure, AI for Business, HPC and Supercomputing, GTC Silicon Valley 2018 - ID S8194
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Industrial Inspection
Presentation
Media
Deep Learning with Sparse Uncertain Data: An Oil & Gas Perspective
Deep learning techniques have the potential to enable a step change in modeling efficiency for industrial systems. By increasing efficiency and accuracy of diagnostics, and extracting meaning from large amounts of industrial data, deep learning provides a pathway to truly differentiated outcomes. In this talk, we will discuss our experience building deep learning models for Oil & Gas applications and the CI/CD process for managing the lifecycle of the models in production. We will present novel applications of deep learning for anomaly detection, rock formation identification and optimization. The hybrid modeling framework combining physics-based models with deep learning techniques will be highlighted with specific application of production optimization.
Deep learning techniques have the potential to enable a step change in modeling efficiency for industrial systems. By increasing efficiency and accuracy of diagnostics, and extracting meaning from large amounts of industrial data, deep learning provides a pathway to truly differentiated outcomes. In this talk, we will discuss our experience building deep learning models for Oil & Gas applications and the CI/CD process for managing the lifecycle of the models in production. We will present novel applications of deep learning for anomaly detection, rock formation identification and optimization. The hybrid modeling framework combining physics-based models with deep learning techniques will be highlighted with specific application of production optimization.  Back
 
Keywords:
Industrial Inspection, GTC Silicon Valley 2018 - ID S8789
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