A practical learning project for developing production deployment skills as a full-stack and AI engineer.
This project documents the process of taking applications from a local development environment to a real production environment.
The focus is not on becoming a DevOps or SRE specialist. The goal is to understand the infrastructure, deployment process, security, reliability, and troubleshooting skills needed to deploy and operate real applications.
The project covers the journey from a Linux server and SSH to a complete production system with Django, PostgreSQL, Nginx, Gunicorn, Docker, Redis, Celery, React, CI/CD, monitoring, backups, and AI services.
The project follows a practical phase based approach.
Each phase introduces a specific deployment concept, explains why it exists, and then applies it through hands-on work.
The general process is:
- Understand the concept
- Learn why it is needed
- Configure it
- Deploy it
- Test it
- Break it deliberately when appropriate
- Troubleshoot the actual error
- Rebuild the important parts from memory
- Document what was learned
A phase is not considered complete just because the concept is understood. The practical deployment must work and the key steps must be reproducible.
The project progresses from the foundation layer to a complete production deployment:
- Linux Fundamentals and SSH
- Development and Production Configuration
- PostgreSQL Production Deployment
- Gunicorn and Nginx
- Domains, DNS and HTTPS
- Static and Media Files
- Docker and Docker Compose
- Redis and Celery in Production
- Process Management
- Full Stack Cloud Deployment
- CI/CD and Deployment Strategies
- Logging and Monitoring
- Backups, Recovery and Production Security
- AI Application Deployment
- Production Troubleshooting
- Deployment Architecture and Documentation
development-engineering/
├── README.md
├── .gitignore
├── docs/
│ ├── architecture/
│ ├── deployment/
│ ├── troubleshooting/
│ └── notes/
├── phases/
├── projects/
└── scripts/