Add comprehensive documentation for deployment architecture, environm…

README.md

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+# DevOps Technical Assignment
+
+This project demonstrates practical DevOps implementation including structured Git workflow, CI/CD automation, Docker-based deployment, environment separation, monitoring strategy and production debugging approach.
+
+---
+
+## 🔧 Tech Stack
+
+- FastAPI (Python)
+- Docker (Containerization)
+- GitHub Actions (CI/CD)
+- Azure Virtual Machine (Cloud Deployment)
+- Nginx (Reverse Proxy)
+- Git Branching Strategy (Feature → Develop → Main)
+
+---
+
+## 🌿 Branching Strategy
+
+This repository follows a structured Git branching model:
+
+- `main` → Production environment
+- `develop` → Staging / integration environment
+- `feature/*` → Feature development branches
+
+### Workflow
+
+1. Feature branch is created from `develop`
+2. Pull Request is raised to `develop`
+3. CI pipeline validates build & container health
+4. Feature is merged into `develop` (staging deploy triggered)
+5. Release PR is created from `develop` to `main`
+6. Merge to `main` triggers production deployment
+
+Branch protection practices:
+
+- No direct push to `main`
+- CI must pass before merge
+- PR review required
+
+---
+
+## ⚙️ CI/CD Pipeline
+
+CI/CD is implemented using GitHub Actions.
+
+### CI Stage
+
+- Dependency installation
+- Syntax validation
+- Docker image build verification
+- Container health endpoint validation
+
+### CD Stage
+
+- `develop` branch → Deploys staging container
+- `main` branch → Deploys production container
+- Deployment happens via secure SSH automation into Azure VM
+- Containers are rebuilt and restarted on each deployment
+
+---
+
+## 🚀 Deployment Architecture
+
+Application is deployed on a single Azure VM using Docker containers.
+
+### Runtime Architecture
+
+- Production container runs on port **8000**
+- Staging container runs on port **8001**
+- Nginx reverse proxy routes external traffic
+- CI pipeline performs automated remote deployment
+
+### Live URLs
+
+- Staging → `http://20.244.44.228/staging/health`
+- Production → `http://20.244.44.228/prod/health`
+
+---
+
+## 🔐 Environment & Secrets Management
+
+- SSH credentials stored in GitHub Secrets
+- Environment variables passed during container runtime
+- Azure Key Vault recommended for production scale deployments
+
+---
+
+## 🔁 Rollback Strategy
+
+- Revert to previous Git commit
+- Redeploy previous Docker image tag
+- Restart container with last stable version
+- Re-run CI/CD workflow
+
+---
+
+## 📊 Monitoring & Infrastructure Strategy
+
+- Container logs monitored using `docker logs`
+- Nginx access logs help identify traffic patterns
+- Azure Monitor can track CPU / memory utilization
+- Health endpoint alerts can detect service failure
+- Horizontal container scaling can be implemented for load handling
+
+---
+
+## 🛠 Production Debugging Approach
+
+If production deployment fails:
+
+1. Identify blast radius (staging vs production impact)
+2. Check GitHub Actions logs
+3. SSH into Azure VM
+4. Validate container status and logs
+5. Verify environment configuration
+6. Rollback to last stable release
+7. Redeploy and monitor
+
+---
+
+## 🌟 Bonus Implementation
+
+### Dockerization
+
+The application is fully containerized using Docker with a production-ready image design:
+
+- Dockerized application deployment
+- Environment separation using container ports
+- Reverse proxy routing via Nginx
+- Cloud VM deployment on Azure
+- CI validated container health checks
+- Uses slim Python base image for reduced size
+- Runs as non-root user for better security
+- Includes container healthcheck endpoint
+- Supports environment-based deployments
+- Enables reproducible builds across environments
+
+### Kubernetes Deployment Approach (High Level)
+
+For large-scale production deployment, this architecture can be migrated to Kubernetes:
+
+- Docker image can be deployed using Kubernetes Deployment resource
+- Service object can expose application internally
+- Ingress controller can replace Nginx reverse proxy
+- Horizontal Pod Autoscaler can scale pods based on CPU / memory usage
+- ConfigMaps and Secrets can manage runtime configuration
+- Rolling updates can ensure zero downtime deployments
+
+### AWS Exposure
+
+Although this implementation uses Azure VM for deployment, similar architecture can be implemented on AWS using:
+
+- EC2 instances for container hosting
+- Application Load Balancer for routing
+- CloudWatch for logs and metrics
+- Auto Scaling Groups for infrastructure scaling
+- ECS or EKS for container orchestration
+
+---