M Waleed Khan’s Post

CI/CD pipelines should not stop at application builds. When infrastructure changes sit outside the pipeline, teams create bottlenecks, slow reviews, and risky production changes. Watch this on-demand session to see Azure DevOps pipelines managing cloud infrastructure end to end using Infrastructure as Code with Pulumi. The walkthrough shows Pulumi Preview running on pull requests so teams can review infrastructure diffs before merge, followed by automated deployment through the pipeline. You will also see how credentials, secrets, and configuration are handled in CI/CD using Pulumi ESC, and how teams can move toward short-lived authentication with OIDC instead of long-lived tokens. Watch the recording: https://hubs.ly/Q0407rXx0 Demo code: https://hubs.ly/Q0407CMs0 #Azure #AzureDevOps #CICD #DevOps #InfrastructureAsCode

🚀 From Code to Cloud: The Rise of Kubernetes The journey of modern application deployment is fascinating. We started with physical servers, moved to the cloud, embraced containers, and eventually realized one big challenge: 👉 Managing containers at scale is impossible manually. That’s where container orchestration changed everything. 🔹 Containers (Docker) solved the “it works on my machine” problem 🔹 Orchestration automated deployment, scaling, and recovery 🔹 Kubernetes emerged as the industry standard cloud agnostic, scalable, and resilient. This evolution isn’t just about tools it’s about how engineering thinking matured to handle complexity at scale. Still learning, still building, and appreciating how far cloud-native systems have come. 💡 Ameen Alam Hamzah Syed Fahad Khan Ali Jawwad Aneeq Khatri Kubernetes #DevOps #Containers #CloudNative

🚀 𝗗𝗮𝘆 𝟭𝟯/𝟯𝟬 — 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 𝗖𝗹𝘂𝘀𝘁𝗲𝗿 𝗔𝘂𝘁𝗼𝘀𝗰𝗮𝗹𝗲𝗿 When Pods can’t be scheduled due to lack of node resources, scaling Pods alone isn’t enough — the cluster itself must grow. That’s where Cluster Autoscaler comes in. 🧠 What Cluster Autoscaler does? • Automatically adds nodes when Pods are pending • Removes unused nodes when capacity is no longer needed • Works with cloud provider auto-scaling groups 👉 It ensures your cluster always has just enough infrastructure. ⚙️ How it works (simple)? • Watches for unschedulable Pods • Checks which node groups can host them • Increases node count when needed • Removes nodes that are underutilized 👉 It runs in a continuous decision loop. 🆚 Cluster Autoscaler vs HPA • HPA → scales Pods • Cluster Autoscaler → scales Nodes 📌 Both are required for true elasticity. 📊 Why it matters in production • Prevents failed deployments • Reduces cloud cost • Handles traffic spikes • Enables large-scale workloads 📌 Day 13 complete 🔜 Day 14/30 — Kubernetes Scheduling (how Pods get placed on nodes) 🚀 #Kubernetes #K8s #DevOps #CloudNative #Autoscaling #PlatformEngineering #LearningInPublic

Beyond Hosting: The Evolution of the Cloud Native Ecosystem. Cloud computing has shifted from being a simple storage solution to becoming a complex, automated engine. Today, the challenge isn't just "being on the cloud," but managing the immense complexity of modern infrastructure without overwhelming your operations team. The Core Pillars of Modern Infrastructure: -Declarative Provisioning: Using Infrastructure as Code (IaC) with tools like Terraform or OpenTofu to ensure environments are repeatable and immune to manual error. -Container Orchestration: Leveraging Kubernetes to manage the lifecycle of applications, ensuring they are self-healing and highly available. -Continuous Delivery: Transitioning to GitOps and automated pipelines (CI/CD) where every change is tested, scanned, and deployed without human intervention. -Observability and Insights: Moving beyond basic monitoring to deep observability using the LGTM stack (Loki, Grafana, Tempo, Mimir) to understand system behavior in real-time. The objective is to create an environment where infrastructure is invisible - a silent, powerful foundation that allows engineering teams to focus entirely on innovation rather than troubleshooting. #CloudNative #DevOps #SRE #Infrastructure #Kubernetes #Automation #TechTrends

How do you deploy to production without fear? Here’s a visual cheatsheet of the most effective Deployment & Production Testing patterns used in modern cloud systems: 🔹 Blue–Green 🔹 Canary 🔹 Rolling 🔹 Feature Flags 🔹 Smoke & Synthetic testing 🔹 A/B & Chaos testing The goal is simple: 👉 Zero downtime 👉 Fast rollback 👉 High confidence releases Sharing this for anyone working with CI/CD, AWS, Kubernetes, or microservices. #Tech #DevOpsLife #CloudComputing #ReleaseEngineering #SoftwareDevelopment

Kubernetes brings scalability and flexibility, but without the right governance, costs can quickly spiral out of control. For IT leaders, cost optimization isn’t about cutting performance — it’s about engineering efficiency. 3 proven focus areas: • Right-sizing pods and nodes • Using autoscaling policies effectively • Matching workloads to the right compute models The result? Lower cloud spend, better resource utilization, and more predictable operations. Follow Techbridge Latam for practical insights on scaling modern infrastructure. https://lnkd.in/diF6Fs7W ... #TechbridgeLatam #Kubernetes #CloudCosts #ITLeadership #DevOps #PlatformEngineering #CloudInfrastructure #TechStrategy #EngineeringLeadership

Cost optimization isn’t about cutting corners—it’s about smart engineering. By combining IaC with automated alerting, we’ve reduced cloud spend while improving uptime. Efficiency and reliability can coexist. #CloudCostOptimization #SRE #DevOps #FinOps

Why we chose Cloud Run over Kubernetes (and don’t regret it) ☁️🚀 Kubernetes is powerful. But power isn’t always the right answer. For many of our production workloads, we consciously chose Cloud Run over Kubernetes, here’s why 👇 ✅ Zero infrastructure management No clusters to manage, no node upgrades, no YAML fatigue. ✅ True serverless scaling Scales from zero to thousands automatically and scales back to zero when idle. ✅ Faster time-to-market We deploy containers, not platforms. More time shipping features, less time babysitting infra. ✅ Lower operational cost Pay only when requests are served. No idle cluster bills at 2 AM. ✅ Security & isolation by default Built-in IAM, HTTPS, revisions, and traffic splitting, without extra setup. ⚠️ When would we choose Kubernetes instead? Complex service mesh requirements Stateful workloads Heavy custom networking or low-level control 👉 Our takeaway: If your goal is speed, simplicity, and scalability, Cloud Run is a no-brainer. Kubernetes is amazing - but not mandatory for every problem. Choose tools based on outcomes, not hype. #CloudRun #Serverless #GCP #DevOps #Kubernetes #StartupEngineering #CloudArchitecture

The most underrated DevOps skill: knowing what not to build In Cloud/DevOps, we celebrate: • New pipelines • More tooling • Bigger architectures But some of the best decisions I’ve seen were: ❌ Not adding another service ❌ Not over-engineering a solution ❌ Not optimizing too early Sometimes the smartest move is: a simpler architecture, fewer dependencies, and clearer ownership. Complex systems don’t fail because they’re small. They fail because no one fully understands them. If you had to remove one thing from your current setup to make it more reliable, what would it be? #DevOps #CloudEngineering #SystemDesign #AWS #SRE #EngineeringMindset #TechLeadership