Break Docker Tutorials, Build Real Skills

🚀 Built a fully automated CI/CD pipeline — from code to cloud in under 3 minutes. I've been on a self-learning journey into DevOps, and this week I wanted to stop reading about CI/CD and actually build one. Here's what happens the moment I run git push: ✅ GitHub Actions spins up a fresh Ubuntu VM ✅ 4 automated pytest tests run — if any fail, everything stops ✅ A Docker image is built and pushed to Docker Hub (tagged with the commit SHA for traceability) ✅ The container is deployed live to AWS EC2 via SSH ✅ The old version is replaced with zero manual steps Total time: ~2 minutes. Human involvement after the push: zero. What I learned building this: → The needs: keyword in GitHub Actions creates a real safety chain — broken code physically cannot reach production → Secrets management isn't optional — credentials belong in encrypted GitHub Secrets, never in code → Docker layer caching (copying requirements.txt before your app code) can cut build time significantly → Tagging images with github.sha means every deployment is traceable to its exact commit — rollback in under 60 seconds Tech stack: Python · Flask · Docker · GitHub Actions · AWS EC2 · pytest Live API: http://13.61.177.113:5000 The biggest lesson? Automation isn't about replacing effort — it's about making effort reliable. #DevOps #CICD #Docker #AWS #GitHubActions #CloudComputing #LearningInPublic #Python

🚀 Day 4 of My DevOps Journey — Building My First Docker Image Yesterday I ran containers. Today, I built my own. This is where Docker stopped feeling like a tool… and started feeling like power. 🔹 What I Learned: ▪️ What a Dockerfile is ▪️ How to define instructions (FROM, COPY, RUN, CMD) ▪️ How images are built layer by layer ▪️ Difference between base image and custom image 🔹 Mini Project: I created my own Docker image for a simple app: ✔ Used nginx:alpine as base image ✔ Added custom HTML page ✔ Built image using docker build ✔ Ran container using my own image ✔ Verified output in browser 🔹 Real Issue I Faced: ❌ Changes not reflecting after rebuild 🔹 What Was Wrong: Docker was using cached layers 🔹 How I Fixed It: ✔ Used --no-cache during build ✔ Understood how Docker layer caching works 💡 Key Learning: “If you don’t understand Docker layers, you don’t understand Docker.” Now I can: ▫️ Build custom images ▫️ Modify application behavior ▫️ Prepare apps for deployment Next → Docker Compose (multi-container setup 🔥) If you’re learning DevOps, let’s connect and grow together 🤝 #DevOps #Docker #Dockerfile #Containers #Cloud #LearningInPublic #BuildInPublic #CI_CD

Been heads down on Kubernetes basics for my MLOps learning. Most people jump straight to Kubeflow or complex ML pipelines. I took a step back. I created a sample project that walks through deploying a simple Flask app on K8s. Nothing fancy. Just the fundamentals. What I actually did: 1. Built a Docker image from a basic Flask app (can be replaced by real world ml api) 2. Wrote a deployment.yaml file 3. Set up Minikube locally to test 4. Deployed with kubectl apply 5. Watched pods spin up and down What clicked for me: The load balancing part finally made sense when I saw traffic distributing across 2 pods. Killed one pod manually and Kubernetes brought up another one immediately. That self-healing piece is huge for model inference services. Also realized - the same deployment.yaml that works on Minikube will work on EKS, AKS, or GKE with minimal changes. That's the real power. For MLOps specifically: This same pattern is what you'd use to deploy a model API. The Flask app just needs to call model.predict() instead of returning "Hello World". Everything else - scaling, rolling updates, health checks - stays the same. Not saying Kubernetes is always the answer. For a simple inference endpoint, ECS Fargate is probably fine. But if you're running multiple models, need GPU scheduling, or want cloud portability, K8s makes sense. Next for me: Add a real model (maybe a simple transformer), figure out GPU node selectors, and get Prometheus metrics working. The repo is linked in comments if anyone wants to run through it themselves. Took me about 2 hours start to finish. Question for those doing ML inference in production: Are you using K8s or something simpler? What pain points should I watch for? #MLOps #Kubernetes #LearningInPublic https://lnkd.in/gM_-DtWp

🚀 Just open-sourced the Deployment.io skill for coding agents - a SKILL.md runbook that teaches your agent how to deploy AI-generated code to your cloud account correctly on the first try. Paired with a leaner MCP server, we cut our most-used tool descriptions by ~80% by moving the runbook into the skill. The combined release gives your agent more capability while costing less context per session. What the skill does: framework detection, git pre-flight, port discovery, log-based diagnosis, and approval handling. Loads only when the prompt matches a deploy intent - rich without being always-on. Works with Claude Code, Claude Desktop, Cursor, OpenAI Codex, Gemini CLI, and 25+ other SKILL.md-compliant agents. MIT licensed. Blog: https://lnkd.in/gCf3ktwt #AIAgents #DevOps #CodingAgents #OpenSource

🗓️ Day 38/100 — 100 Days of AWS & DevOps Challenge Today: pull an image, give it a new tag. Two commands. $ sudo docker pull busybox:musl $ sudo docker tag busybox:musl busybox:blog Same Image ID. That's the detail worth understanding. docker tag doesn't copy anything. It creates a new pointer to the same underlying image layers. Both busybox:musl and busybox:blog share the same 1.41MB of storage — tagging is free in terms of disk space. You can have 50 tags on the same image and it still only occupies the storage of one image. Why this matters in CI/CD pipelines: This is exactly how image promotion works in production. A build produces myapp:build-456. Tests pass. The pipeline re-tags it: $ docker tag myapp:build-456 myapp:staging $ docker tag myapp:build-456 myapp:latest No new image is created. No layers are duplicated. The same image — tested and verified — now carries multiple tags that represent its promotion status. When production needs a rollback: $ docker tag myapp:build-455 myapp:latest One command. The previous build is live again. Because tags are just labels. One more concept worth knowing: tags are mutable. busybox:latest today might be a different image tomorrow when the maintainer updates it. If you need to pin to a specific image permanently, use the digest: $ docker pull busybox@sha256:abc123def... A digest is immutable — it always refers to the exact same image layers regardless of when or where it's used. For production deployments, digests over tags. Full tagging guide + Q&A on GitHub 👇 https://lnkd.in/gvUtPawg #DevOps #Docker #Containers #CICD #Linux #100DaysOfDevOps #KodeKloud #LearningInPublic #CloudEngineering #ContainerRegistry #Kubernetes

Day 37 of #90DaysOfDevOps — Docker Revision & Cheat Sheet 🚀 Instead of just learning Docker, I wanted something I can actually use in real projects. So today I paused and revised everything from Day 29–36 and built a practical Docker Cheat Sheet. 💡 What I covered: Containers, Images, and Dockerfile basics Volumes & Bind Mounts Networking & multi-container communication Docker Compose (real-world setups) Multi-stage builds & image optimization Docker Hub (push, pull, tagging) 📌 Key realization: Learning tools is easy… Remembering and applying them is the real challenge. That’s why this cheat sheet will be my go-to reference while building projects. 🔗 Cheat Sheet: https://lnkd.in/gB5MPDAJ Would love feedback from the community 🙌 #Docker #DevOps #90DaysOfDevOps #LearnInPublic #CloudComputing #TrainWithShubham

Hi Everyone, 🚀 Docker fundamentals? Done. Now diving deeper into Kubernetes. As part of my CKA prep, I revisited a fundamental question: 👉 If Docker already works so well… where does Kubernetes actually fit in? Here’s how I think about it now: Docker does an excellent job at: ✔ Packaging applications ✔ Running containers reliably But at scale, new challenges show up 👇 🔹 Handling traffic spikes 🔹 Ensuring high availability 🔹 Rolling out updates without downtime ⚡ That’s where Kubernetes adds real value. It’s not a replacement — it’s the orchestration layer that builds on top of containers. 👉 Docker = Container runtime 👉 Kubernetes = Orchestration across clusters Managing: 🔹 Scaling 🔹 Self-healing 🔹 Load balancing 🔹 Controlled rollouts 💡 What I focused on next was how Kubernetes actually works under the hood We often run kubectl apply — but the flow behind it is what makes everything click: 1️⃣ Request hits the API Server (cluster entry point) 2️⃣ Desired state stored in etcd 3️⃣ Controllers reconcile → Deployment → ReplicaSet → Pods 4️⃣ Scheduler assigns Pods to nodes 5️⃣ Kubelet ensures containers are running ✅ Desired state → Actual state 🤯 One design detail I really appreciate: Everything flows through the API Server — which makes the system: ✔ Consistent ✔ Observable ✔ Easier to debug Even in failure scenarios: 🟢 Running workloads continue 🔴 But new changes pause if the control plane is unavailable This phase is less about “learning Kubernetes” and more about refining mental models for how distributed systems behave. Following Varun Joshi's CKA Certification Course 2025 and documenting everything as I go. 🔗 Course: Cloud With VarJosh – CKA Certification Course 2025 Youtube :- https://lnkd.in/esk3khMB Github:- https://lnkd.in/e8wQ7Fk9 Which Kubernetes concept helped you level up your understanding the most? #Kubernetes #Docker #CKA #DevOps #CloudNative #K8s #Containers #SRE #LearningInPublic

🚀 Day 4: Docker Images & Layers (Explained Simply) By now, you’ve run containers 🐳 But have you ever wondered… 👉 What exactly is inside an image? Let’s break it down in the simplest way 👇 🥪 Think of Docker Images Like a Sandwich A sandwich has layers: 🍞 Bread 🧀 Cheese 🥬 Lettuce 🍅 Tomato 👉 Together → it becomes a complete sandwich 💡 Same with Docker: 👉 Image = Collection of layers 📦 What is a Docker Image? 👉 A Docker Image is a blueprint of your application It contains: ✔️ Code ✔️ Libraries ✔️ Dependencies ✔️ Runtime 👉 Everything needed to run your app 🧱 What are Layers? Each step in building an image creates a layer Example: Base OS (Ubuntu) Install Python Copy app code Run app 👉 Each step = one layer 🔄 Why Layers Are Powerful? ✅ Faster builds (reuse layers) ✅ Less storage ✅ Easy updates 💡 If one layer changes → others stay same 🔥 Real-Time Example When you run: docker pull nginx 👉 What happens: 1️⃣ Docker checks existing layers 2️⃣ Downloads only missing layers 3️⃣ Combines them into one image 🎯 One-Line Understanding 👉 Image = Stack of layers 👉 Container = Running image 💡 Key Takeaway Docker Images are not a single file ❌ 👉 They are made of multiple reusable layers ✅ That’s why Docker is fast and efficient 🚀 📌 Next Post (Day 5): 👉 Dockerfile (Build your own image step-by-step) #Docker #DevOps #Cloud #AWS #Kubernetes #Learning

I’ve just published a hands-on Docker session focused on simplifying containerization and making it practical for real-world use. In this video, I walk through: ✔ Core Docker concepts (images, containers, commands) ✔ Running and managing containers effectively ✔ Troubleshooting common issues like container exits and port mapping ✔ Deploying an application using AWS EC2 and Terraform My focus with this series is to break down DevOps concepts into clear, practical workflows that engineers can actually apply, not just theory. Project repository (fork and follow along): This is Part 1 of the series. In the next part, I’ll be covering more advanced topics including Docker networking, volumes, Dockerfiles, and CI/CD automation. I enjoy building and sharing practical cloud and DevOps solutions. Always open to connecting with professionals in the space and discussing ideas around scalable systems and modern infrastructure. #Docker #DevOps #AWS #Terraform #CloudEngineering #PlatformEngineering

Dockerfile Essentials: Build Better Containers In my recent learning session, I explored how to write efficient and production-ready Dockerfiles. Sharing some key takeaways that every developer should know 👇 🔹 Core Dockerfile Instructions FROM → Defines the base image RUN → Executes commands during build CMD → Default container command ENTRYPOINT → Main executable COPY / ADD → Transfer files into container EXPOSE → Document application port ENV → Manage environment variables ⚡ Best Practices I Found Useful ✔ Use lightweight base images (like alpine) ✔ Leverage multi-stage builds for smaller images ✔ Use .dockerignore to avoid unnecessary files ✔ Run containers with non-root users for better security 🚀 Optimization Tips ✅ Combine RUN commands to reduce layers ✅ Cache dependencies smartly ✅ Use exec form (CMD ["node", "app.js"]) ✅ Clean up unused files to reduce image size #Docker #DevOps #Backend #NodeJS #Cloud #SoftwareEngineering #Learning #Tech