Operations | Monitoring | ITSM | DevOps | Cloud

Observability is how platform teams stop being the answer to every question and start building platforms that answer those questions themselves. This article explains specifically how observability enables platform engineers to support development teams better which reducing ticket volume, cutting MTTR, enabling SLO ownership, and making microservice debugging something devs can do without escalating to you.

UK organisations using cloud services carry a clear legal obligation: they must demonstrate compliance with UK GDPR and the Data Protection Act 2018, not simply assert it. The shift to cloud and hosted infrastructure does not transfer that responsibility to a provider. It distributes it across a chain of controllers and processors that regulators expect you to understand and manage. Post-Brexit, that obligation is set within a distinct legal framework.

IP over Dense Wavelength Division Multiplexing (IPoDWDM) is a network architecture that integrates optical transmission capabilities directly into IP networking equipment such as routers and switches. This approach represents a significant evolution from traditional network designs, where IP and optical layers were managed as separate domains with distinct hardware and operational teams.

CostFormation is how your organization models cost allocation. As more teams adopt it, protecting that logic matters. RBAC for CostFormation Namespaces lets you scope access at the namespace level, so the right people can view and edit Dimensions, and everyone else can’t.

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 our releases, highlighting the features and tools available to you. In this blog, Asa Mirzaieva, engineer from the Silicon Alliances team, will show you how to deploy optimised AI models on Renesas RZ/V series hardware using the Dynamically Reconfigurable Processor for AI (DRP-AI).

This post covers some techniques to get agents to validate their work. We’ll cover why this is important, a variety of types of checks you can employ, and mechanisms you can use to enforce them.

Enterprise AI has crossed an inflection point. The model problem is largely covered. What remains unsolved is the operational impact: how to run AI inference and agentic processes continuously, reliably, and at a cost that doesn’t cancel out the value. Most enterprises are discovering this the hard way. GPU utilization dashboards show 80%. Actual compute efficiency is half that. Token demand is compounding at 200-500% annually as agents multiply every action into dozens of model calls.

I have an Upsun project that's nothing but proofs of concept. It's a dashboard, basically. Each POC gets its own tile. Click in, and you land on a page with three tabs. The first tab is a written explanation of what the POC argues. The second tab is the POC itself, with a built-in demo that automates a walkthrough of the feature so the recipient can watch it run without me on the call.