Operations | Monitoring | ITSM | DevOps | Cloud

Modular architecture: Decouple compute from storage so each can scale independently. This makes it easier to adapt to growing or shifting workloads over time. Future-ready hardware: Select GPUs and CPUs not just for current workloads but with an eye on scalability, including support for newer accelerator types. Scalable design: Ensure the system allows seamless addition of compute nodes or storage without a full redesign.

‎‎Subscribe. Fuel your curiosity. In this episode, we break down what we’ve learned from teams running AI at scale, and why on-premises infrastructure is making a strong comeback. We’re seeing a shift: performance, cost control, data sovereignty, and platform flexibility are driving conversations about on-prem strategies for AI. No one-size-fits-all answers, but if you’re building or scaling AI, this might help you think a few steps ahead.

Data locality: AI models often rely on large datasets. Locating compute close to the data reduces transfer times and improves training performance. Latency sensitivity: Real-time AI applications, like recommendation systems or edge analytics, depend on low-latency environments. This can be more easily tuned in private or hybrid setups. Hardware specialization: Some AI workloads benefit from custom hardware like GPUs or TPUs. Private cloud allows more control over this, while public cloud offers broader access but less customization.

‎‎Subscribe. Fuel your curiosity. “Open source software is… the glue for everything that everyone does, from sending an email through to managing critical operations, not just space operations.” The European Space Agency (ESA) runs missions ranging from investigating Earth’s forests, to exploring Jupiter’s moons, to deflecting incoming asteroids.

Rebooting servers, resetting passwords (again), and somehow knowing exactly what cable’s missing. This is just another day in the life of a SysAdmin. Today, we say thanks to the quiet force that keeps every system running. Happy. This one’s for you. Watch the chaos (and coffee) unfold.

As mission requirements evolve, federal agencies and defense teams need infrastructure supporting AI/ML workloads anywhere, from secure cloud environments to disconnected edge locations. In this fireside chat, Mark Lewis (VP, Application Services at Canonical) and William Crum (Senior Defense Success Engineer at SpectroCloud) discuss how their organizations are helping federal customers deploy secure, scalable, and consistent Kubernetes and AI infrastructure across hybrid and edge environments.

At Canonical, we’re not just about Ubuntu. We're transforming Big Data with secure, automated, and fully compliant solutions that empower you to deploy with confidence.