AI & Deep Learning
Sub brand
Date: September 10, 2019
Time: 8:00 a.m.–9:00 a.m. PT
Duration: 1 hour

The number of video cameras around the world is expected to exceed 45 billion by 2022. Deep learning can help the public good by tapping into this massive feed of data to gather real-time, actionable insights for areas like disaster relief. Learn how NVIDIA’s DeepStream SDK can accelerate disaster response by streamlining applications such as analytics, intelligent traffic control, automated optical inspection, object tracking, and web content filtering. The application framework features hardware-accelerated building blocks that bring deep neural networks and other complex processing tasks into a stream processing pipeline.

Join this in-depth, technical webinar to explore the complete streaming analytics toolkit for situational awareness.

In this webinar, you’ll learn how to:
  • Deploy NVIDIA® TensorRT™ into a hardware-accelerated IVA application in a few short steps
  • Build a high-throughput, low-latency streaming framework capable of scaling with the size of your data
  • Get the most performance out of your hardware

We also invite you to join the Accelerating Real-Time AI-Based Video and Image Analytics webinar to learn how to create DeepStream applications from edge to cloud, deploy and scale them using GPU-accelerated containers and integrate with IoT services to extract deeper insights.



You will receive an email with instructions on how to join the webinar shortly.

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DGX Station Datasheet

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DGX Station Whitepaper

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Jason Tichy

Senior Solutions Architect , NVIDIA

Jason Tichy is a Senior Solutions Architect with NVIDIA and supports the Federal space. His previous experience includes designing NASA's MMS mission, which earned him multiple awards for innovations in flight dynamics. Jason has applied his expertise to key areas of defense and reconnaissance, using methods of high-performance computing and data science to accelerate mission-critical systems and save lives. His research interests include new methods in parallel computing, deep learning, and physical simulations.

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Webinar: Description here

Date & Time: Wednesday, April 22, 2018