Run FastAPI on AWS Lambda with Mangum

Built a multi-container Python Todo API project from scratch to strengthen my backend and DevOps skills. What I implemented: • Python Flask backend API • PostgreSQL database • Dockerized services using Dockerfile • Multi-container architecture with Docker Compose • Persistent storage using volumes • CI/CD pipeline with GitHub Actions Currently running locally with separate app and database containers. Next step: Public cloud deployment. GitHub Repo: https://lnkd.in/dN3nvEzD #Docker #Python #PostgreSQL #DevOps #GitHubActions #LearningByBuilding

Just finished building an AWS Resource Health Monitor in Python! This automation bot: - Monitors EC2 instances for idle CPU and missing tags - Stores timestamped health reports in S3 - Auto-remediates issues with least privilege IAM permissions - Schedules itself to run daily with EventBridge Built with Python, boto3, and real AWS infrastructure from scratch. Check it out on GitHub 👇:  https://lnkd.in/eKmp7gWd #AWS #Python #CloudComputing #DevOps #boto3 #CloudPractitioner

Gemini CLI is open source and platform agnostic so why not run it on Azure? This dev walks through building a Python MCP server with HTTP transport locally, then deploying it to Azure Kubernetes Service and validating the connection from Gemini CLI over the public endpoint. { author: William McLean + Google Developer Experts } https://lnkd.in/e_Fvp77m

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

Just published a deep-dive blog on Docker + Python 🐳 Covered everything from scratch: → What Docker actually is (with a Maggie noodles analogy, yes) → Writing your first Dockerfile → Docker Compose for multi-service apps → Production setup with Gunicorn + Nginx → Deploying on AWS EC2 If you've been confused about containers, images, port mapping, or why your app "works on your machine" but breaks on the server - this one's for you. Link https://lnkd.in/gcn7atem #Docker #Python #DevOps #WebDevelopment #Flask #CloudComputing #AWS #Programming #100DaysOfCode #TechBlog

A stylized SVG illustration that visualizes the full project in a dark, technical aesthetic. It shows the three-stage pipeline on the left (CSV → Python → LDIF), the AWS VPC with the EC2 server and directory tree on the right, the CloudShell terminal with real command syntax, and key metrics summarized at the bottom. ❤️

Just uploaded the full project to GitHub 🔗 If you're learning Azure Functions or serverless architecture, the repo has everything: • Clean commit history that shows the actual development process (not just the final result) • Detailed README breaking down what I built and what I learned • Proper project structure with .gitignore protecting secrets requirements.txt documenting all dependencies function_app.py with the blob trigger implementation • The goal wasn't just to build it, it was to build it the right way. Professional workflow, clean code, honest documentation. Check it out and let me know if you have questions about the project or serverless architecture. GitHub: https://lnkd.in/eziE_HVb #GitHub #AzureFunctions #Python #OpenSource #CloudComputing

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

🚀 Week 7 – From Scripts to a CLI Tool This week I focused on improving my Python scripting by building something more practical instead of just solving small problems. Here’s what I worked on: • Built a CLI-based tool to simulate EC2 instance monitoring • Implemented filtering for running and stopped instances • Parsed AWS-style JSON data instead of using hardcoded values • Added summary reporting (total, running, stopped) • Enabled command-line arguments using sys.argv • Added option to export output to a file • Structured the tool similar to real CLI tools (like AWS CLI) Also cleaned up my GitHub repository to keep the project focused and professional. This week helped me understand how scripting is actually used in cloud and automation scenarios. Next: Planning to make this more aligned with real AWS CLI behavior and improve data handling further. #Python #Automation #CloudComputing #CLI #LearningJourney

Just got one of my MCP articles approved on an AWS focused channel on Medium - Towards AWS. This article covers deploying MCP servers to Lambda. It was interesting as some extra state managment code was needed to allow Lambda to service the MCP requests. Article is here:  https://lnkd.in/dC5Qfazy The full channel is here: https://towardsaws.com/ #MCP #Python #GoogleCloud #AWScommunity #AWS #AWSCommunityBuilders