Remote Management for Edge AI Devices and Models across Diverse Applications
When talking about AI deployment on the edge, it’s quite flexible and smooth for an individual project or demo testing to adjust model parameters and hardware connections on 1 device. However, things are going to be more complicated across industrial applications, especially when you own 100 or even 10k+ devices at the same time, deploying in different cities or areas. Effective management could be a difficult problem.
Besides, there’s another challenge of staying in sync with the latest software iterations and adapting to any interface changes. It demands an investment of time and resources, given the need to address bug fixes because of the integration of new updates and extensions, all while manually dealing with the update process for each device.
That’s why remote management stands out as an effective method in large-scale edge deployment. Here, I would like to introduce three useful AI software that are working well on our NVIDIA Jetson Edge devices:
Remote Management for Packages Update
Allxon
Allxon delivers a comprehensive BSP OTA (Over-The-Air) solution for NVIDIA Jetson edge AI device fleets, revolutionizing the way updates are managed. The process is streamlined into just three straightforward steps:
✅ Confirm supported file types of BSP image and security patch
✅ Prepare deployment script and generate BSP OTA artifact
✅ Deploy on the edge, seamlessly refreshing firmware, driver, and BIOS components.
The advantages extend beyond only getting the latest version of BSP in time! It’ll also save device storage with a slimmed and customized image by removing unwanted file types from a standard BSP, also eliminating endless bug fixing and security issues by updating and deploying BSPs with the latest security patches.
If you are planning to get started with Allxon, be sure to check out our wiki guidance about executing the OTA Payload Package update for Jetson devices.
Remote Management for Jetson Device Check and AI Pipelines
Lumeo
Lumeo provides a no-code video analytics platform that enables you to rapidly design, deploy, and monitor custom video analytics and other vision-AI enabled applications.
During the image/video data transferring from Jetson to Lumeo cloud, Lumeo will send requests to Jetson edge devices and expect to get responses continuously, so that it can make sure the specific device is awake and can join the following workflow of detecting part. Therefore, you can check if the edge device’s status is on or off before AI models actually work in each application task. In the meanwhile, you can also upload your own AI models to the Lumeo cloud in order to deploy them on your application anytime.
Feel free to check out our wiki guidance about how to set up Lumeo on your Jetson device to process video from Streams, IP Cameras on the same network, or attached USB cameras.
Scailable
Scailable offers an advanced platform tailored for the seamless development and management of Edge AI solutions on a larger scale. It empowers you to effortlessly deploy AI/ML models to a multitude of edge devices without the necessity for extensive on-device engineering. You can access crucial installation details and device activities, while flexibly grouping devices and deploying models to specific device clusters. It’s getting much easier to track the model assignment during the deploying process, including the model connected with each device on the detail page.
Seeed NVIDIA Jetson Ecosystem
Seeed is an Elite partner for edge AI in the NVIDIA Partner Network. Explore more carrier boards, full system devices, customization services, use cases, and developer tools on Seeed’s NVIDIA Jetson ecosystem page.
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