Operations | Monitoring | ITSM | DevOps | Cloud

AI at the edge: simplifying infrastructure with Cisco and Canonical

Legacy infrastructure was not designed for the requirements of the AI era. While large-scale model training remains centralized in data centers, test-time inference is rapidly shifting to the edge to reduce latency and bandwidth consumption. This shift creates a new frontier for enterprise AI, but deploying at the edge introduces significant manual complexity, interoperability issues, and security vulnerabilities.

AI Made Infrastructure Weird Again | Ubuntu Summit 26.04

For years, we were told we were escaping hardware. Virtualization, containers, and Kubernetes made the underlying servers practically invisible to the average application developer. Then came the AI boom and infrastructure got incredibly weird again. In this fast-paced lightning talk, Billy Olson from Canonical breaks down why the modern AI server is no longer just a machine, but a volatile distributed system packed inside a single chassis.

The next era of telco clouds: get open infrastructure choice with Sylva and Canonical Kubernetes

The telco industry is undergoing a fundamental change. Over the past few years, the increasing maturity of cloud-native infrastructure has accelerated the movement from manually operated and hardware-centric systems to automated, software-defined platforms. Underpinning this change are open source initiatives such as the Sylva project. Sylva is hosted by Linux Foundation Europe and heavily backed by major telecom operators and vendors.

Kubeflow MLOps tutorial: from notebook development to production inference

In this video, our engineering team takes you through a full end-to-end Kubeflow implementation, step by step – from data exploration to production inference. Follow the journey of a house price prediction use case and see how modern MLOps components work together: Kubeflow architectures and starter repositories Notebook-based development workflows Data exploration and model development MLflow for experiment tracking Katib for hyperparameter optimization Kubeflow Pipelines for automated preprocessing and training KServe for scalable model inference.

What is RDMA over Converged Ethernet (RoCE)?

Previous articles walked through RDMA (Remote Direct Memory Access) as a programming model and InfiniBand as the fabric that was built around it. Both led to the same conclusion, even if it was never stated outright: moving data, not compute, becomes the bottleneck once systems scale. So what happens when you want RDMA, but you’re already running an Ethernet network you’re not keen to replace? That’s usually where RDMA over Converged Ethernet (RoCE) enters the conversation.

NVIDIA Approach for Achieving ASIL B Qualified Linux | Ubuntu Summit 26.04

Can a general purpose, open source operating system like Linux be deployed in safety-critical products? Can it achieve certifications to standards like ISO 26262? This question has become increasingly common in recent years. In this talk, Bryan provides a safety integrity qualification approach for Linux. It is composed of Linux Kernel, user space libraries (like libc) and user-space components (like init processes), up to ASIL B according to ISO 26262:2018.

Beyond tokens per watt - using Ubuntu 26.04 LTS for AI

Tokens per watt (TpW) – the measure of useful AI work produced per watt of energy consumed – is the metric at top of mind for CEOs, heads of AI, and infrastructure teams alike. With the tremendous cost of GPU clusters, extracting as much value as possible from the expense is critical. But in the pursuit of tokens, it’s important to remember that hardware efficiency isn’t the only factor influencing data center operating costs, or the output of useful, revenue-generating AI work.

A look into Ubuntu Core 26: Deploying AI models on Renesas RZ/V series for production

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).

Configure Ubuntu with YAML | Ubuntu Summit 26.04

Learn how to configure Ubuntu at launch using declarative, idempotent instructions stored in a version-controlled YAML file. In this talk, Rajan explains how this approach minimizes arbitrary commands, reduces risks of command injection and privilege escalation, and ensures validation and error handling. This is relevant on major public and private clouds, and virtualization solutions ranging from VMware, WSL, LXD, Multipass, Proxmox, and more.