Operations | Monitoring | ITSM | DevOps | Cloud

We’ve all been there. It’s 2 AM, your phone is buzzing with alerts, and you’re suddenly thrust into an incident war room with a dozen other bleary-eyed engineers. The production environment is on fire, customers are affected, and everyone’s trying to piece together what went wrong. But here’s what makes these moments fascinating from a systems perspective – it’s rarely just one person silently fixing the issue in isolation.

Setting up a local Deep Learning environment can be a headache. Between managing CUDA drivers, resolving Python library conflicts, and ensuring you have enough GPU power, you often spend more time configuring than coding. Google Cloud and Canonical work together to solve this with Deep Learning VM Images, which use Ubuntu Accelerator Optimized OS as the base OS. These are pre-configured virtual machines optimized for data science and machine learning tasks.

I just finished building Speedscale’s eBPF-based component to capture and analyze network traffic in a Kubernetes cluster, and it forced me to confront some uncomfortable truths about observability. While there were certainly some challenges along the way, particularly in dealing with Go applications, the approach was relatively straightforward.

Development teams depend on microservices to build, deploy, and scale features independently. Microservices have become the backbone of modern, scalable applications. Scaleway’s managed Kubernetes service (Kubernetes Kapsule) offers a powerful, cost-effective platform for running containerized workloads in the cloud. It’s a great fit for startups and solo engineers who want to focus on shipping features, not managing infrastructure.

When a SaaS company scales, something curious happens. The cloud bill grows. One team swears it’s Kubernetes. Another blames the Black Friday promo. But when you’re unsure whether that increase is tied to healthy SaaS growth or simply overspending, your margins are already at risk. That gap between what’s spent and what’s understood is where indirect costs live. Yet these costs rarely show up in dashboards. Well, until it’s too late.

In software engineering, we often talk about the “iron triangle” of constraints: time, resources, and features. You can rarely fix all three. At many companies, when scope creeps or resources get tight, the timeline is often the first element of the triangle to slip. At Canonical, we take a different approach. For us, time is the fixed constraint. This isn’t just about strict project management. It is a mechanism of trust.

Telecommunications networks are undergoing a cloud-native revolution. 5G promises ultra-fast connectivity and real-time services, but achieving those benefits requires an infrastructure that is agile, low-latency, and highly reliable. Kubernetes has emerged as a cornerstone for telecom operators to meet 5G demands.

Welcome to Spike! Whether you’ve just set up your account or joined an existing team, this guide will help you understand how to receive and respond to incidents.