Before an AI agent can troubleshoot Kubernetes, the platform needs reliable observability, useful runbooks, clear permissions, safe rollback procedures, and engineers who understand the systems underneath. That is why this upcoming hands-on workshop from Packt caught my attention: Link to my post in LinkedIn The workshop explores how infrastructure agents can combine: - Live Kubernetes state - Prometheus metrics - Runbooks and postmortems - MCP-based tooling - Human approval gates - Dry-run and audit workflows The practical scenarios include Kubernetes CrashLoopBackOff investigations, GPU scheduling failures, latency troubleshooting, autoscaling inspection, and controlled deployment rollbacks. Through my work with Kubernetes, GPU infrastructure, observability, and production platforms, I have seen how much time incident response depends on collecting the right context quickly. I am now exploring how AI agents could assist operators with on-call investigations, cluster troubleshooting, and incident diagnosis without removing human judgment or control. I am particularly interested in how these patterns could evolve into reusable platform capabilities for teams and clients running complex Kubernetes and AI workloads. My public focus remains firmly grounded in Kubernetes, observability, platform engineering, and operational fundamentals. I chose to share this workshop because it connects those foundations with practical AI tooling rather than treating AI as a substitute for sound engineering. Packt has provided an exclusive 50% discount for my network. Use code: ERDEN50 📅 August 27, 2026 💻 Online, hands-on workshop 🔗 Registration: https://lnkd.in/dJxCpnRJ

You've got a cluster. Now make it do something. Phase 3 — Orbit Basics is now open. In a handful of kubectl commands you'll take a bare nginx image and turn it into a real web app: running, self-scaling, reachable from your browser, and serving your own page. No YAML wizardry. Just your terminal and the cluster you already built. 📌 What's inside: → Step 1: Deploy nginx (3 replicas, one command) → Step 2: Bolt on a HorizontalPodAutoscaler — watch it scale itself → Step 3: Expose it with a NodePort and curl your way in → Step 4: Hijack the homepage with a ConfigMap volume 💡 The plot twist: you'll manually scale to 15 pods… and watch the autoscaler drag them right back down. That lesson alone is worth the price of admission. 🎯 Your mission: Follow the steps, run curl 127.0.0.1:<NodePort>, and post a screenshot of your custom Hello World! page below. That screenshot = your points toward Level 3. No more "I'll learn scaling someday." Someday is today. 👇

Welcome to the community! @Roger Clayton @IBrahim Nassiri @Ragnar Skogheim @Rollyn Moises @Deandre Williams @Krenar Radoniqi @Ethan Walker @Phello Tsolo @Tanzil Chowdhury @Mayank Johari @Ava Reynolds @Maor Klir @Mr Shinden @Luis G This is not another Discord or Slack where your posts disappear in the noise. I’ll be your community moderator and help you build real Kubernetes skills in the cloud-native landscape. If you are serious about learning and landing a job in this space, my DMs are open. You can start here: Welcome (Start Here) We’re kicking off a free CKA cohort call this Wednesday - it’s on the community calendar. This cohort is part of my own goal to pass CKA this year, so you’ll be learning alongside me, not just watching. You can join whether you are completely new to Kubernetes or already have some experience. Check the Calendar tab for the “CKA Cohort” event and grab the meeting link there.