Operations | Monitoring | ITSM | DevOps | Cloud

Ubuntu images on Microsoft Azure have recently started shipping with the open source package azure-vm-utils included by default. Azure VM utils is a package that provides essential utilities and udev rules to optimize the Linux experience on Azure. This change results in more reliable disks, smoother networking on accelerated setups, and fewer tweaks to get things running. Here’s what you need to know.

Software delivery is more complex than ever. Teams need speed, reliability, and scalability to stay competitive. Site Reliability Engineering (SRE), DevOps, and Platform Engineering are three key disciplines that address these challenges. Though these terms are often used together, they are not the same and share distinct differences. In this blog, we’ll discuss each term individually, compare SRE vs. DevOps vs. Platform Engineering, and also show how they work together.

Modern systems are complex, distributed, and fast-changing, so keeping them reliable requires more than watching dashboards. Observability vs. Monitoring explains how teams gain the deep insight needed to detect, diagnose, and resolve issues. Monitoring collects predefined metrics and alerts you to known problems, while observability provides rich, contextual telemetry to investigate unknown failures.

In early October, Red Hat disclosed a breach of a GitLab system used by its Consulting division. Threat actors claim to have exfiltrated hundreds of gigabytes of project data — and while investigations are still underway, reports suggest consulting engagement artifacts may have been impacted. For the organizations involved, the concern isn’t limited to reputational damage.

I wanted to share a story of a recent engagement with a law firm to highlight the strategic importance of compliance in today’s legal sector. It started with a single email. A mid-sized law firm received a regulator’s request for evidence following a client complaint. The issue wasn’t malpractice; it was a missed filing deadline caused by a system slowdown. The firm had no audit trail to prove the delay was technical, not procedural.

The Problem: AI workloads are dynamic, unpredictable, and expensive. Data prep can choke your pipeline, training jobs hog GPUs without awareness, and inference, the most latency-sensitive phase, is notoriously hard to scale efficiently. Worse, traditional infrastructure tools treat GPU as a static commodity, ignoring model intent, workload shape, and sharing capabilities.