Generative AI & Automation – Course Repository

Welcome to the official repository for the Generative AI & Automation course. This project demonstrates modern software development practices including CI/CD, automated testing, security scanning, and collaborative development workflows.

🚀 Quick Links

• Student Guide - How to submit assignments
• Intern Guide - Development workflow for team members
• Contributing Guidelines - Complete contribution guide
• Security Policy - Security guidelines and reporting
• Code of Conduct - Community guidelines

📚 Course Overview

This is an 8-week intensive course for intermediate/advanced students to gain hands-on experience with:

• Building and deploying generative agents
• Fine-tuning foundation models (LLMs, diffusion)
• Working with multimodal AI (text, image, audio, video)
• Hosting models as APIs and serving them for web and mobile front ends
• Automating model workflows using GitHub Actions, Docker, and APIs
• Modern Development Practices: CI/CD, testing, security, collaboration

Each week includes 3 hours of lectures and 3 hours of practical workshops.

🏗️ Repository Structure

generative-ai-course/
├── .github/                    # GitHub configuration
│   ├── workflows/             # CI/CD pipelines
│   ├── ISSUE_TEMPLATE/        # Issue templates
│   └── pull_request_template.md
├── lectures/week-01/          # Weekly lecture materials
├── workshops/week-01/         # Workshop starter kits
├── student-submissions/       # Student assignment submissions
├── scripts/                   # Automation and utility scripts
├── tests/                     # Test suite
├── docs/                      # Documentation
├── capstone-projects/         # Final projects
├── .gitignore                 # Git ignore rules
├── requirements.txt           # Python dependencies
├── pyproject.toml            # Modern Python configuration
├── Dockerfile                # Container configuration
├── docker-compose.yml        # Local development setup
├── Makefile                  # Development commands
└── README.md                 # This file

🔄 CI/CD Pipeline

This project implements a comprehensive CI/CD pipeline that demonstrates real-world development practices:

Pipeline Stages

1. Quality Check: Linting, formatting, type checking
2. Testing: Unit and integration tests across Python versions
3. Security: Vulnerability scanning with Bandit and Safety
4. Student Validation: Automated feedback for submissions
5. Build: Docker image creation
6. Deploy: Staging and production deployment

Branch Strategy

• main: Production-ready code (admin only)
• develop: Integration branch for contributors
• intern/*: Personal branches for interns
• feature/*: Feature development branches
• hotfix/*: Emergency fixes

Automated Checks

• ✅ Code formatting (Black)
• ✅ Style checking (Flake8)
• ✅ Type checking (MyPy)
• ✅ Security scanning (Bandit, Safety)
• ✅ Test coverage (Pytest)
• ✅ Student submission validation

🚀 Getting Started

For Students

1. Fork this repository to your GitHub account
2. Clone your fork locally
3. Work in student-submissions/your-username/week-X/
4. Submit via Pull Request from your fork's main branch

For Contributors/Interns

1. Clone the repository
2. Create your personal branch: intern/your-name
3. Install dependencies: make install
4. Start development: make setup

For Administrators

1. Review PRs to develop branch
2. Test develop branch thoroughly
3. Deploy to production via main branch
4. Monitor CI/CD pipeline health

🛠️ Development Setup

Prerequisites

• Python 3.8+
• Git
• Docker (optional)
• GitHub account

Quick Setup

# Clone repository
git clone https://github.com/NERD-Community-Ethiopia/generative-ai-course.git
cd generative-ai-course

# Install dependencies
make install

# Run quality checks
make quality

# Start development
make setup

Docker Setup (Alternative)

# Build and run with Docker Compose
docker-compose up -d

# Run specific services
docker-compose up jupyter  # Jupyter Lab
docker-compose up tests    # Run tests
docker-compose up lint     # Run linting

📋 Available Commands

# Development
make install              # Install dependencies
make test                 # Run tests
make lint                 # Run linting
make format               # Format code
make security             # Security checks
make quality              # All quality checks

# Docker
make docker-build         # Build Docker image
make docker-run           # Run with Docker Compose
make docker-stop          # Stop Docker services

# Utilities
make clean                # Clean temporary files
make docs                 # Build documentation
make validate-submission  # Validate student submission
make dependency-report    # Generate dependency report
make status               # Show project status

🧪 Testing

Test Structure

tests/
├── unit/              # Unit tests
├── integration/       # Integration tests
├── fixtures/          # Test fixtures
└── conftest.py        # Pytest configuration

Running Tests

# All tests
make test

# Specific test categories
make test-unit
make test-integration

# With coverage
pytest --cov=./ --cov-report=html

🔒 Security

Security Tools

• Bandit: Python security linter
• Safety: Dependency vulnerability scanner
• GitHub Security Advisories: Automated detection

Security Practices

• Never commit sensitive information
• Use environment variables for configuration
• Follow OWASP Top 10 guidelines
• Keep dependencies updated
• Report vulnerabilities privately

📊 Quality Metrics

Code Quality Standards

• Test Coverage: Minimum 80%
• Code Complexity: Maximum 10 (McCabe)
• Security Issues: Zero critical/high
• Documentation: 100% API coverage

Performance Targets

• Build Time: < 10 minutes
• Test Time: < 5 minutes
• Deployment Time: < 5 minutes

🎯 Learning Objectives

By the end of this course, students will be able to:

• Design and implement generative AI agents
• Fine-tune and deploy custom AI models
• Build full-stack applications with AI integration
• Automate AI workflows using modern DevOps practices
• Deploy AI applications to production environments
• Use professional development tools and practices
• Collaborate effectively using Git and GitHub
• Write secure, tested, and maintainable code

📅 Course Schedule

• Week 1: Introduction to Generative AI & Course Setup
• Week 2: Building Your First AI Agent
• Week 3: Fine-tuning Foundation Models
• Week 4: Multimodal AI Applications
• Week 5: API Development & Deployment
• Week 6: Frontend Integration & User Experience
• Week 7: Automation & CI/CD Pipelines
• Week 8: Capstone Project Development

🛠️ Tech Stack

Backend & AI/ML

• Python: Core programming language
• FastAPI/Flask: Web frameworks
• PyTorch: Deep learning framework
• Transformers: Hugging Face library
• LangChain: AI application framework
• OpenAI: API integration

Development & DevOps

• Git & GitHub: Version control and collaboration
• Docker: Containerization
• GitHub Actions: CI/CD automation
• Pytest: Testing framework
• Black/Flake8: Code formatting and linting
• MyPy: Type checking
• Bandit/Safety: Security tools

Frontend & Deployment

• React/Vue.js: Frontend frameworks
• RESTful APIs: API design
• WebSocket: Real-time communication
• PostgreSQL/MongoDB: Databases
• Vector Databases: AI data storage

🤝 Contributing

For Students

• Submit assignments via Pull Requests
• Follow the Student Guide
• Use the student PR template
• Get automated feedback

For Contributors

• Follow the Intern Guide
• Use feature branches
• Submit PRs to develop branch
• Participate in code reviews

For Administrators

• Review and merge PRs
• Monitor CI/CD pipeline
• Handle security incidents
• Manage course content

🏆 Recognition

Student Benefits

• Portfolio Projects: Each assignment becomes a portfolio piece
• GitHub Profile: Professional development history
• Real-world Skills: Industry-standard practices
• Networking: Connect with other developers

Contributor Benefits

• GitHub Contributors: Recognition in project history
• Reference Letters: Professional recommendations
• Skill Development: Advanced development practices
• Career Opportunities: Industry connections

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This course teaches both AI/ML skills and professional software development practices. Students learn to work like real developers while building cutting-edge AI applications.