Docker and Python Development Essentials

Why am I still writing Bash when Python can do this better? So I started documenting the switch. Just published 3 new posts on my blog: 🐍 Phase 0: Python Basics for People Who Already Know Bash 🐍 Phase 1: Replace Your Shell Scripts with Python 🛠️ Project: Automated CI/CD Pipeline for a Flask App (Docker + Nginx + GitHub Actions + AWS EC2) Everything I'm learning, in public. If you're a DevOps engineer curious about Python or MLOps, I'm writing this roadmap for you. 🔗 [https://lnkd.in/gXJmVMTd] #DevOps #Python #MLOps #CI_CD #LearningInPublic #AWS #Docker

🐳 Optimized Docker images = Faster deployments! Reduced my Python app image from 1.6GB → 96.9MB — that's a 94% size reduction! Using gcr.io/distroless + multi-stage builds to ship lean, production-ready containers. 🚀 #Docker #DevOps #AWS #Python #Optimization #CloudEngineering

Kubernetes Explained For Developers - Save This Cheat Sheet If you're a Python developer getting into Kubernetes, here's everything you need in one carousel: ☸ What Kubernetes actually does (and how it's different from Docker) 📦 Core objects: Pods, Deployments, Services, Ingress 🏨 The Hotel Analogy that makes it all click 💻 Essential kubectl commands (copy-paste ready) 🔒 Config, Secrets, Health Probes ☁️ Cloud K8s: EKS vs GKE vs AKS ✅ Production checklist for your first deploy The fastest way to learn: build one FastAPI app → Dockerfile → Deployment → Service → scale to 3 replicas. That one exercise teaches more than hours of theory. Save 🔖 and share ↗️ with a developer who needs this. #Kubernetes #DevOps #Python #CloudNative #Docker #SoftwareEngineering #K8s #FastAPI #Backend #TechCareer

"What's your tech stack?" Honest answer: Whatever works best for the problem. We use C#, Python, React, Kubernetes, AWS. The usual suspects. But strong engineering goes beyond the tools: > Writing clear, maintainable code > Comprehensive testing (because it matters) > Proper documentation (future-you will thank you) > Meaningful peer reviews that make the work better We choose technologies thoughtfully and build systems designed to scale and last. If you get excited about solving hard problems the right way, you'll fit right in - https://bit.ly/4cjpv9F

We launched our MCP server as an npm package yesterday, and the adoption has been insane!! You can now take any React or Python Claude Code project to production on AWS in literally one command. Try it out and ship faster - https://lnkd.in/d3mpXw5B

A minimal Python MCP server, validated locally with Gemini CLI, then deployed to AWS ECS Express in a single step. This dev walks through the incremental approach — from stdio transport to HTTP, from local to remote — without the extra noise. { author: William McLean + Google Developer Experts } https://lnkd.in/eQ6R5cbc

[Azure Updates] [Launched] Generally Available: Microsoft Agent Framework 1.0. Microsoft Agent Framework is now version 1.0 for both .NET and Python, with stable APIs and a long-term support commitment. Agent Framework 1.0 supports multi-agent orchestration, multi-provider model support, and cross-runtime interoperability via A2A an #azure #azureupdates https://lnkd.in/esqS8KXe

This case study shows building a centralized multi-account AWS monitoring platform managing 25+ accounts using Python Boto3 to fetch resource configurations into MongoDB, with Flask API and Next.js frontend achieving $30k annual savings. More: https://ku.bz/LV7qH0CK1

🚀 Built my first multi-container application using Docker! Over the past few days, I worked through building a simple Flask web app connected to a Redis database — all containerised and orchestrated with Docker Compose. 🔧 What I built: A Flask app with multiple routes: • / → welcome page • /count → increments visit count • /reset → resets the counter Redis used as a key-value store for tracking visits Multi-container setup using Docker Compose 🧠 What I learned: How to containerise a Python application using a Dockerfile Running multiple services with Docker Compose Container-to-container communication using service names (no localhost!) Using environment variables instead of hardcoding configuration Persisting data with Docker volumes 📌 Key takeaway: Understanding how services communicate inside containers (and debugging when they don’t!) was the biggest learning moment. Next step: exploring scaling and load balancing 👀 #Docker #DevOps #Python #Flask #Redis #LearningInPublic CoderCo

A few months ago, I thought Python virtual environments, Docker, and Kubernetes were just different ways to “run code.” Then a small issue changed everything. I had a Kafka consumer working perfectly on my laptop. Clean logic, no errors. But when I moved it to another server… it failed. Missing libraries. Version conflicts. Classic “works on my machine” problem.😀 That’s when I truly understood the role of a Python virtual environment (venv). It helped me isolate dependencies — different projects, different package versions, no conflicts. But the problem wasn’t just Python packages… it was the environment itself. So I moved to Docker. Now, I wasn’t just shipping code — I was shipping the entire environment. Python version, libraries, configurations — everything packed into one image. And suddenly, the same Kafka consumer ran exactly the same everywhere. Problem solved? Not quite. What if the process crashes? What if I need 5 consumers running in parallel? What if one server goes down? That’s where Kubernetes came in. With Kubernetes Pods, my container wasn’t just running — it was being managed. Auto-restarts, scaling, load distribution… things I used to handle manually were now automated. That’s when it clicked: venv helps you develop Docker helps you deploy Kubernetes helps you scale and survive failures Today, I don’t see them as competing tools — they are layers of maturity in building reliable systems. Start simple. But build in a way that you’re ready to scale. #Python #Docker #Kubernetes #Kafka #DevOps #DataEngineering #SystemDesign