Operations | Monitoring | ITSM | DevOps | Cloud

Data is everywhere, not just in large centralised data centres, but in smaller outposts, like retail outlets, remote or branch offices, filming locations and even cars. Use cases for edge storage include local processing, content collaboration that cannot take place with high network latency, and for ingest – where locally created content is captured, protected and then made available to other systems.

Canonical is proud to announce that we have joined the Sylva project of Linux Foundation Europe as a General Member. We aim to bring our open source infrastructure solutions to Sylva and contribute to the project’s goal of providing a platform to validate cloud-native telco functions.

We have just returned from the stunning city of Riga, where Canonical hosted the Ubuntu Summit 2023, and we are still buzzing with the energy from meeting so many of you. Preparing this event was a team effort. A small team of us have been meeting for months, brainstorming ideas, preparing huge to-do lists, and dividing responsibilities. It was difficult, but also hugely rewarding, and an opportunity many of us felt was a privilege.

When OpenAI released ChatGPT on November 30, 2022, no one could have anticipated that the following 6 months would usher in a dizzying transformation for human society with the arrival of a new generation of artificial intelligence. Since the emergence of deep learning in the early 2010s, artificial intelligence has entered its third wave of development. The introduction of the Transformer algorithm in 2017 propelled deep learning into the era of large models.

In the mission-critical workloads of modern enterprises, where time boundaries and determinism can make a major difference, the demand for real-time systems has never been more urgent. In our latest whitepaper, we delve into the realm of real-time Linux. The whitepaper is a comprehensive guide to understanding and unlocking the potential of real-time Linux to meet the stringent demands of industrial workloads.

Most people know that it is important to apply security updates. It can be challenging, however, to accomplish this while maximising the uptime of the services you are running on top. Every change, even applying security patches, carries some risk of disrupting your workloads. You therefore need to be deliberate about your update strategy.

In the last couple of years, the AI landscape has evolved from a researched-focused practice to a discipline delivering production-grade projects that are transforming operations across industries. Enterprises are growing their AI budgets, and are open to investing both in infrastructure and talent to accelerate their initiatives – so it’s the ideal time to make sure that you have a comprehensive toolkit for machine learning (ML).

Canonical, the publisher of Ubuntu, announced today the general availability of Charmed Kubeflow 1.8. Charmed Kubeflow is an open source, end-to-end MLOps platform that enables professionals to easily develop and deploy AI/ML models. It runs on any cloud, including hybrid cloud or multi-cloud scenarios. This latest release also offers the ability to run AI/ML workloads in air-gapped environments.

You probably know that it is important to apply security updates. You may not be as clear on the details of how to do that. We are going to explain best practices for applying Ubuntu updates to single instances and what the built-in unattended-upgrades tool does and does not do.

Today the lowest cost media per terabyte for backups is still tape, even after factoring in the handling costs of manually loading and unloading tape libraries, and logistics surrounding off-site storage. However, while inexpensive, tapes are inflexible. And when used as an offline solution it can take many hours to retrieve them from offsite storage – not to mention the additional time required to load them into a tape library before a recovery can even start.