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.

Vehicles are becoming more and more like mobile data centres. On average, a modern vehicle contains over 60 sensors that monitor various aspects of the vehicle, generating an immense amount of data that is processed on the go. This transformation is creating an unprecedented set of challenges for OEMs. Edge computing is a new paradigm that is changing how data is processed in these types of environments.