PERIDIO x ADVANTECH GUIDE
Ship vision AI on NVIDIA® Jetson™ — without the 18-month detour.
Getting an NVIDIA® Jetson™ computer vision demo running takes days. Getting 1,000 devices into production takes 12–18 months — unless you start with the right stack. This is the playbook for shipping vision AI on industrial-grade hardware, with the OS, OTA, and fleet management already done for you.
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INTRODUCTION
NVIDIA® Gets You Building.
Peridio x Advantech Gets You Shipping.
NVIDIA® built the best developer on-ramp in edge AI. NVIDIA® JetPack™, pre-trained models, and world-class tooling mean teams can go from idea to working inference in days. Computer vision on NVIDIA® Jetson™ is real: detecting defects on production lines, monitoring crops from autonomous harvesters, analyzing video for safety compliance. The models work. The hardware exists. NVIDIA® Jetson™ platform delivers the compute.
Building and shipping are two different disciplines.
Getting from a working demo to 1,000 devices in the field requires a different kind of infrastructure — manufacturing reproducibility, secure boot chains, OTA updates, fleet management, Yocto-based Linux, compliance documentation.
The production software layer required to actually ship and maintain devices at scale can consume 12–18 months of engineering effort that nobody planned for.
Who this guide is for
- Engineering and product leaders shipping NVIDIA® Jetson™ vision AI from pilot to fleet
- Teams on Advantech industrial hardware who need production OS, OTA, and fleet management
- Organizations stuck between a working demo and repeatable production deployments
What questions it answers
Ubuntu vs. Yocto — which path actually scales on NVIDIA® Jetson™ fleets?
See where Ubuntu breaks down at scale, why in-house Yocto becomes a staffing trap, and how a third production path avoids both.
How do you avoid the 12–18 month production infrastructure detour?
Learn how pre-integrated OS, OTA, and fleet management replace custom Linux work so your team ships product, not infrastructure.
Which Advantech platforms match your use case and environment?
Map vision AI workloads to iCAM, MIC, ITA, and TREK platforms — from line-side cameras to rugged outdoor and in-vehicle deployments.
How do Peridio and Advantech fit together from prototype to production?
Follow the stack from NVIDIA® Jetson™ through Advantech hardware, Avocado OS, and fleet operations — in days, not months.
THE PROBLEM
You Invested in Production Hardware.
Your Software Should Match.
You chose Advantech for a reason: industrial-grade hardware. Wide temperature ranges. 7–10 year product lifecycles. The certifications and long-term availability that production demands. You're not building a prototype. You're building a product that needs to run for a decade in environments that punish anything less than industrial quality.
But here's what we see across the NVIDIA® Jetson™ ecosystem: teams invest in that premium hardware and then try to build the production software layer themselves. The demo works. The pilot looks great. And then somewhere around the 100th device, everything that was easy becomes hard. OTA updates that brick devices. Security questionnaires the team can't answer. ML engineers spending their sprints on Linux infrastructure instead of improving models.
12–18 months
typical time to build production software infrastructure from scratch
$5K–$10K
cost per truck roll when a field update fails
The hardware is production-grade. The software needs to be too. And that gap is where deployments stall.
That's exactly why Peridio and Advantech work together. Advantech builds the industrial edge AI hardware — cameras, edge systems, and rugged platforms designed for the environments where vision AI actually runs. Peridio provides the production software layer: the OS, security, OTA updates, and fleet management that makes those devices deployable and maintainable at scale.
THE OS DECISION
Your engineers want Yocto. Your IT team wants Ubuntu. Both are right — and that's the problem.
Inside almost every NVIDIA® Jetson™ team scaling past prototype, the same conflict is playing out. IT and OT teams push for Canonical Ubuntu. It's familiar. It maps to what they know from cloud and datacenter environments. It simplifies hiring. It has vendor support. From a traditional IT perspective, it's the safe choice.
Engineers push back. They know that production edge devices need something different: Customizable OS, reproducible builds, minimal attack surface, immutable infrastructure. They're asking for Yocto. And they're right that production needs it.
Both sides have valid reasoning. But both paths, taken alone, lead to real problems.
The path
Ubuntu
Ubuntu gets you moving fast. But edge is not the cloud. What works in a datacenter creates real consequences on hardware that needs to run unattended for years.
Runtime overhead
A general-purpose desktop OS carries services, packages, and processes your application does not need — consuming resources on hardware where every watt and cycle counts.
Upgrade lock-in
Ubuntu's update model can block future JetPack™ upgrades. Teams end up frozen on an older version, unable to access new NVIDIA® capabilities without a costly migration.
Compliance gaps
The EU Cyber Resilience Act is raising the compliance floor. Reproducible builds, SBOM generation, CVE patching cadence: Ubuntu plus Docker can't answer these questions cleanly.
Packaging constraints
The Snap Store and apt ecosystem weren't designed for embedded fleet deployment. Moving applications between devices and maintaining reproducibility become increasingly painful at scale.
The path
Yocto
Engineers requesting Yocto are responding to the right instinct. Reproducible builds, minimal attack surface, and immutable infrastructure are production necessities — not preferences.
But building Yocto in-house creates a different problem. The talent pool is small. You're competing with automotive OEMs and FAANG for the same few hundred people who deeply understand Yocto. Teams budget three months for integration and spend eighteen.
Teams that build Yocto in-house often trade one trap for another — solving the OS problem by creating a staffing and maintenance problem that compounds every year.
That hardware investment only pays off if the software running on it can be maintained, updated, and secured for the same timeframe. An OS strategy that freezes you on old JetPack versions or depends on a single Yocto expert undermines the value of production-grade hardware in the first place.
THE SOLUTION
Production-grade software for production-grade hardware.
There's a third option — one that gives you Yocto's production outcomes (immutable, reproducible, secure, compliant) without requiring your team to become Yocto experts or maintain a custom build system forever. That's the Advantech + Peridio stack on NVIDIA® Jetson™.
The architecture separates your OS from your application. The OS layer — locked down, patched, compliant — is managed for you. Your team manages the application layer: models, pipelines, configs. Ship updates independently, on your schedule, without touching the firmware underneath. And because Avocado OS is pre-integrated with Advantech's NVIDIA® Jetson™ platforms, you're deploying on production hardware in days.
Your Application
vision AI · Custom models · Analytics
Peridio + Avocado OS
Secure Boot · OTA Updates · Fleet Mgmt · Recovery
Advantech Hardware
ICAM-540 · ICAM-520 · MIC-717 · ITA-510
NVIDIA® Jetson™
ORIN NX · AGX ORIN · THOR
Ship models, not operating systems. Your team was hired to build vision AI — not maintain Linux infrastructure.
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Replace 12–18 months of infrastructure work with a production-ready stack you can deploy in days.