Operations | Monitoring | ITSM | DevOps | Cloud

Turbocharging host workloads with Calico eBPF and XDP

In Linux, network-based applications rely on the kernel’s networking stack to establish communication with other systems. While this process is generally efficient and has been optimized over the years, in some cases it can create unnecessary overhead that can impact the overall performance of the system for network-intensive workloads such as web servers and databases.

Charmed Spark beta release is out - try it today

The Canonical Data Fabric team is pleased to announce the first beta release of Charmed Spark, our solution for Apache Spark. Apache Spark is a free, open source software framework for developing distributed, parallel processing jobs. It’s popular with data engineers and data scientists alike when building data pipelines for both batch and continuous data processing at scale.

EV charging infrastructure: overcome the challenges with open source

When people ask about the negative points preventing higher electric Vehicles (EV) sales, two points are raised systematically: range anxiety and the availability of charging stations. Range anxiety refers to the fear of running out of battery while driving. Of course, with more range or more charging stations, range anxiety decreases. Unfortunately, most countries lack EV charging infrastructure that meets consumer expectations.

Managing security vulnerabilities and compliance for U.S. Government with Ubuntu Pro

Complying with US government security standards such as FIPS, FedRAMP, and DISA-STIG is essential for federal agencies and any business that deploys systems and services for U.S. government use. However, maintaining a compliant IT ecosystem is a major undertaking, as each regulation brings a host of specialised requirements. And dealing with the never-ending stream of security vulnerabilities that require patching only adds to this task.

The founding moments: Tracing the origins of confidential computing

In Ernest Hemingway’s novel “The Sun Also Rises,” there is a memorable exchange between the author and the main character, Mike. When asked how he went bankrupt, Mike responds with a concise yet profound answer: “Two ways. Gradually, then suddenly.” Innovation happens much in the same way. Gradually, then suddenly. Ideas simmer and evolve, gaining traction until they reach a tipping point.

Strengthen your cloud cyber security with Ubuntu Pro and confidential VMs

In today’s digital landscape, organisations of all sizes have expanded their presence in the cloud. But with this expansion comes a significant increase in the attack surface, making security a top concern. In this blog, we will dive into the exciting world of cloud cyber security, and explore a stronger approach to securing your workloads with the help of Ubuntu.

Canonical Joins Eclipse Adoptium Working Group to Strengthen Commitment to Open Source Java Runtimes

Canonical, the company behind Ubuntu, is thrilled to announce its membership of the Eclipse Adoptium Working Group. As an esteemed project under the Eclipse Foundation, the Adoptium Working Group brings together renowned OpenJDK builders and distributors such as Alibaba, Azul, Huawei, IBM, Microsoft, Red Hat, Rivos, and, most recently, Google.

Kubeflow vs MLFlow: which one to choose?

Data scientists and machine learning engineers are often looking for tools that could ease their work. Kubeflow and MLFlow are two of the most popular open-source tools in the machine learning operations (MLOps) space. They are often considered when kickstarting a new AI/ML initiative, so comparisons between them are not surprising. This blog covers a very controversial topic, answering a question that many people from the industry have: Kubeflow vs MLFlow: Which one is better?

Open Source MLOps on AWS

With the rise of generative AI, enterprises are growing their AI budgets, looking for options to quickly set up the infrastructure and run the entire machine learning cycle. Cloud providers like AWS are often preferred to kick-start AI/ML projects as they offer the computing power to experiment without long-term commitments. Starting on the cloud takes away the burden of computing power, reducing start-up time and cost and allowing teams to iterate more quickly.