Kubernetes

Second post in the Azure Platform Engineering series. In Part 1, we built the provisioning layer of the Internal Developer Platform: Dev Center, Azure Deployment Environments, Bicep templates, and shared AKS runtime patterns. That is necessary, but it is not sufficient. An Internal Developer Platform becomes trustworthy when it enforces standards without turning into a bureaucratic cage. That is where governance, observability, and security enter the picture. The platform must make the right path easy, the risky path difficult, and the unsupported path visible. ...

First post in a two-part series on Platform Engineering on Azure. If your developers still need tickets, handoffs, or tribal knowledge to get a usable environment, your delivery system is slower than your codebase. Platform Engineering is how you fix that. The goal is not to hide infrastructure from developers. The goal is to package infrastructure, security, and observability into a self-service product developers can trust. On Azure, that means combining Microsoft Dev Center, Azure Deployment Environments, Bicep, and a shared runtime such as AKS. ...

Twelfth post in the series. In the previous one, we ran Azure OpenAI with HA and sane retry patterns. This one is for when the nice diagram meets real life. This post is organized as real-world failure scenarios. Each follows: Symptoms → Diagnosis → Root Cause → Resolution → Prevention. Read it once for pattern recognition. Then bookmark it. You will need it again. tl;dr Most late-night AI infra incidents come down to driver drift, memory pressure, scheduler mismatch, throttling, or cold starts. Start with the first check that rules out the biggest class of failure. Scenario 1: NVIDIA driver crash after kernel update Symptoms Monday morning. The ML team reports that all GPU workloads failed over the weekend. Nobody deployed anything. You SSH in: ...

Tenth post in the series. In the previous one, we controlled costs with Spot VMs, right-sizing, and FinOps. Now for the next problem: how to stop being a human help desk for GPU access. tl;dr Self-service AI platforms need isolation, quotas, and scheduling together. The goal is fewer tickets, not faster manual provisioning. The Slack channel that ate your calendar Six months ago, you provisioned a single GPU VM for the ML team. Configured drivers, mounted storage, closed the ticket. Felt like any other infrastructure request. ...

Introduction OpenShift, developed by Red Hat, extends Kubernetes to provide a more robust platform for deploying and managing containerized applications in a complete application platform. It integrates the core features of Kubernetes with additional tools and services to enhance developer productivity and operational efficiency. This guide aims to introduce beginners to deploying applications on OpenShift Local, a streamlined method to run OpenShift clusters locally for development and testing. Using a local OpenShift environment, offers several benefits, especially for developers who are new to OpenShift or Kubernetes: ...

Just sharing an awesome learning resource I found recently. It will introduce you to the application development cycle leveraging OpenShift’s tooling & features with a special focus on securing your environment using Advanced Cluster Security for Kubernetes (ACS). You will get a brief introduction in several OpenShift features like OpenShift Pipelines, OpenShift GitOps, and OpenShift DevSpaces. Check out at https://devsecops-workshop.github.io/