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Build and Deploy AI Models Using NVIDIA DeepStream on Jetson and AWS IoT Core

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Edge computing is essential for the processing and generating real-time insights from billions of sensors around the world. Pixels from cameras are converted to actionable insights using AI on the edge.

Intelligent video analytics technology is used to aid traffic management for cities, automated checkout in retail stores, non-contact automated visual inspection in manufacturing facilities, or crowd monitoring for safe social distancing measures, and more. After the insights are generated on the edge, the next step is to send this vital information to the cloud for further analytics. 

NVIDIA and Amazon Web Services (AWS) have partnered to simplify workflows to quickly build, deploy, and manage AI-based video analytics applications. With NVIDIA NGC, data scientists and developers can deploy AI frameworks with containers, get a head start with pretrained models, deploy AI services in cloud-native containers, and orchestrate using Kubernetes, which can be effectively deployed at the edge and to the cloud and give you faster time-to-market.

NVIDIA DeepStream is the accelerated framework to build AI-powered intelligent video analytics apps and services. With DeepStream end-to-end inference and IoT capabilities, you can generate real-time insights on the edge and seamlessly connect to the cloud. The SDK offers a great degree of flexibility, enabling you to customize your video analytic solution. 

To connect to the cloud, you can use the message-broker plugin to either use one of the built-in message protocols or create a custom adapter.  

The message broker plugin provides the API to create and integrate custom protocol adapters to communicate to any cloud services. AWS created a custom adapter to publish MQTT messages from DeepStream applications running on the edge to AWS IoT Core. This enables you to deploy and manage AI applications on the edge using AWS cloud services.    

For more information, see the following resources: