Welcome to this blog series which explores innovative uses of Ubuntu Core. Throughout this series, Canonical’s Engineers will show what you can build with this Core 26 release, highlighting the features and tools available to you.
In this first blog, Michael Croft-White, Engineer Director for Canonical’s Telemetry team, will show you how Ubuntu Core integrates with both AWS IoT Greengrass and Azure IoT Edge to enable cloud-driven device management and intelligent edge workloads. With these platforms, developers gain centralised control of deployments, flexible workload orchestration, and the ability to process data locally while still leveraging the awesome power of the cloud for analytics and monitoring. Coupled with Ubuntu Core architecture, this provides an end-to-end infrastructure for managing the complete lifecycle of devices in the field.
Scaling to meet edge and cloud demands
Cloud platforms have transformed how we build and operate connected systems. With services like AWS IoT Greengrass and Azure IoT Edge, developers can deploy, monitor, and manage workloads across fleets of devices from a central location.
However, not everything can happen in the cloud. Many IoT scenarios require workloads to run directly on the device, whether to reduce latency, operate with intermittent connectivity, or handle sensitive data locally. This is especially true for applications involving AI inference, real-time processing, or autonomous decision-making.
The challenge, then, is not choosing between cloud and edge, but combining them effectively. You need a way to run intelligent workloads locally while still benefiting from cloud-based orchestration, updates, and observability.
Ubuntu Core sits at the centre of this approach. By providing a secure, consistent, and immutable platform on the device, it enables seamless integration with cloud runtimes, allowing you to extend cloud intelligence to the edge without compromising on performance or control.
Cloud runtime integration
Both AWS and Azure bring their own way of extending cloud capabilities to the edge. On Ubuntu Core, these are delivered as snaps and container-based runtimes that sit neatly on top of the system.
When it comes to cloud runtime integration, both AWS IoT Greengrass and Azure IoT Edge offer a clean, secure path. Whether it’s Greengrass or the Azure, the agent snap cleanly authorises, authenticates, and integrates the device into the End-2-End workflow.
Once connected, the process is streamlined: the Greengrass snap registers with AWS and starts receiving cloud-defined components to capture, process, or send data; similarly, the Azure-provisioned device automatically pulls containerised modules via IoT Hub and executes them locally
In both cases, the important part is that the device becomes an extension of the cloud. You don’t manually install applications anymore—you deploy them.
Deploying an edge AI workload
Let’s take a simple example. Imagine you want your device to capture images from a webcam and detect objects in real time.
With AWS IoT Greengrass, you can package this logic into components. One component captures images, another processes them using a model, and a third handles communication with the cloud. Once deployed, these components run continuously on the device, reacting to data as it arrives.
On Azure IoT Edge, you would achieve the same outcome using modules. A container pulls images from the camera, another runs inference—perhaps using Intel OpenVINO—and another sends results upstream. These modules are defined in the cloud and delivered automatically to the device.
In both cases, the device is doing the heavy lifting locally. It captures data, runs inference, and only sends the results back to the cloud. This keeps latency low and reduces the amount of data being transmitted.
Configuration and onboarding
One of the advantages of Ubuntu Core 26 is how easily you can prepare devices for this kind of deployment. You can build an image that already includes the necessary snaps—Greengrass or Azure components—and ship it directly to the field.
When the device boots, it connects to the network, authenticates with the cloud platform, and registers itself. From that point on, it appears in your cloud console, ready to receive workloads.
This is often referred to as zero-touch onboarding. The device effectively introduces itself, and you take over from the cloud side.
You could, for example, ship identical hardware to different customers without deciding upfront what each one will do. Once powered on, each device appears in your cloud environment, and you assign it a role by deploying the appropriate workload.
Updating and evolving workloads
After deployment, requirements rarely stay the same. You might want to update your AI model, tweak how images are processed, or change how results are handled.
With both Greengrass and Azure IoT Edge, you can make these changes centrally and push them to the device. The new version is deployed, the old one is replaced, and the system continues running.
Ubuntu Core supports this process by ensuring updates are transactional and reliable. If something goes wrong, the system can roll back to a known good state.
This means you can confidently evolve your application over time, even across a large fleet of devices.
Monitoring and debugging
Once your devices are running in the field, visibility becomes essential.
Through AWS or Azure, you can see whether your workloads are running, check logs, and monitor communication with the cloud. If something stops working, you don’t need to send someone on-site—you can investigate remotely and have full access to the system.
You can also redeploy components or modules, restart services, or update configurations, all from your cloud interface.
For devices deployed in remote or hard-to-reach locations, this capability is invaluable.
What’s next?
Ubuntu Core 26 provides a secure and reliable foundation for edge devices, while AWS IoT Greengrass and Azure IoT Edge bring the cloud-native capabilities needed to deploy and manage workloads at scale.
Together, they allow you to treat your devices not as isolated systems, but as part of a larger, cloud-connected platform—where applications can be deployed, updated, and monitored just like any other cloud service.
In future blogs, we’ll explore more advanced scenarios, including deeper integration with cloud services and more complex workload orchestration.
Below are some useful links for further reading:
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