Traditional AI deployment can be time-consuming and resource-intensive, requiring deep knowledge of optimization techniques and hardware acceleration. That’s where NVIDIA Jetson and Edge Impulse come in.
The NVIDIA Jetson and NVIDIA Jetson Orin, powered by GPU-accelerated NVIDIA Tegra processors, are ideal for edge AI applications. You can easily add a USB external microphone or camera — and it's fully supported by Edge Impulse. You'll be able to sample raw data, build models, and deploy trained machine learning models to your device directly from the Edge Impulse Studio.
In this blog, we’ll walk through how Edge Impulse streamlines model deployment, reducing the time to production with powerful devices like the NVIDIA Jetson, and how you can get started in just a few steps.
Ready to bring industrial vision-based AI to life? Watch our free on-demand webinar to learn how to deploy visual anomaly detection at the edge using the Raspberry Pi 5, Jetson Orin Nano Super, and Blues Notecard.
The NVIDIA Jetson family delivers GPU acceleration, energy efficiency, and broad compatibility across industrial and consumer applications. With Edge Impulse, PoC development time is reduced from months to days, enabling faster model tuning for industrial environments and seamless deployment to production-ready hardware.
Developing for the Jetson developer kits is simple, however, before getting started in Edge Impulse, please be sure to follow NVIDIA’s Getting Started guide to familiarize yourself with the Jetson platform and get your specific kit’s dependencies up-to-date.
How can I easily deploy my edge AI model to production-ready hardware?
Edge Impulse simplifies AI development with tools for data collection, model training, and hardware optimization, enabling fast deployment to the NVIDIA Jetson developer kits, and more. With built-in support for TensorRT and quantization, it automates optimization for efficient edge AI performance. Check out the Edge Impulse documentation for more information about the TensorRT deployment library and the step-by-step tutorial for NVIDIA Jetson Edge Impulse firmware deployment.
Here’s a short and sweet, step-by-step breakdown of how to deploy a model to NVIDIA production-ready hardware using Edge Impulse:
- Build your dataset – Build high quality datasets for any use case at scale.
- Train your model – Collect data, train, and optimize in Edge Impulse.
- Optimize your model – Automatically optimize models for hardware acceleration for your target deployment device.
- Deploy with ease – Run the model on Jetson using Edge Impulse linux deployment, or TensorRT library.
- Monitor and improve – Iterate based on real-world performance.
Real-World Use Cases
Developers can quickly prototype and deploy models to Jetson hardware, leveraging optimizations like TensorRT with the Edge Impulse Studio deployment options. Explore real-world applications and hands-on guidance in the Edge Impulse Expert Network tutorials, with use cases covering industrial asset tracking, robotics, manufacturing, and more. Here’s just a few great examples:
- Visitor Heatmap with FOMO Object Detection — Jetson Orin Nano
- Workplace Organizer — Nvidia Jetson Nano
- Recyclable Materials Sorter — Nvidia Jetson Nano
- ESD Protection using Computer Vision — Seeed ReComputer
For more information on NVIDIA Jetson Orin hardware, check out “Unlock the power of the NVIDIA Jetson Orin Hardware Ecosystem with new AI capabilities” and the Jetson Partner Hardware Products list.
Getting Started
Ready to deploy your AI models on NVIDIA Jetson production-ready hardware? Here’s how to begin:
- Sign up for a free Edge Impulse account
- Follow the Edge Impulse Jetson getting started tutorial
- Join the community! Tag us @EdgeImpulse or reach out on the Edge Impulse forum
Also, check out the recorded talk at NVIDIA GTC 2024, “Getting AI to the Edge with NVIDIA Jetson Orin & Edge Impulse,” and the webinar “High-Speed Object Detection with Jetson Nano and Edge Impulse.”