A comprehensive, hands-on MLOps bootcamp that takes you from basic concepts to production-ready machine learning deployments. This repository contains 10+ practical lessons covering the entire MLOps lifecycle using real-world tools and datasets.

This bootcamp covers the complete MLOps journey:

• Version Control with Git for ML projects
• Experiment Tracking with MLflow
• Containerization with Docker
• ML Pipelines with preprocessing, training, and deployment
• CI/CD Automation with GitHub Actions

• Kubernetes Deployment for scalable ML services
• Horizontal Pod Autoscaling (HPA) for dynamic scaling
• A/B Testing for model comparison and traffic control
• End-to-End MLOps with monitoring and observability

oreilly-mlops-bootcamp/ ├── Day 1/ # Foundation concepts │ ├── lesson-2-git/ # Version control with Git │ ├── lesson-3-mlflow/ # Experiment tracking │ ├── lesson-4-docker/ # Containerization │ ├── lesson-5-ml-pipeline/ # End-to-end ML pipeline │ └── lesson-6-ci-cd/ # CI/CD automation │ └── Day 2/ # Production deployment ├── lesson-2-kubernetes-basics/ # Kubernetes fundamentals ├── lesson-5-kubernetes-hpa/ # Auto-scaling ├── lesson-7-ab-testing/ # A/B testing └── lesson-10-mlops-practices/ # Complete MLOps workflow 

• Python 3.11 (required for MLflow compatibility)
• Git - Version control system
• Docker - Containerization platform
• Kubectl - Kubernetes command-line tool
• Kubernetes cluster (Docker Desktop with K8s enabled)

• MLflow - Experiment tracking and model management
• Flask/FastAPI - Web frameworks for API development
• scikit-learn - Machine learning library
• pandas - Data manipulation
• pytest - Testing framework
• prometheus-client - Monitoring metrics

• Jupyter Notebook - Interactive development
• Postman - API testing
• GitHub CLI - GitHub integration

# Clone the repository git clone https://github.com/AmmarMohanna/oreilly-mlops-bootcamp.git cd oreilly-mlops-bootcamp # Create conda environment with Python 3.11 conda create -n mlops-py311 python=3.11 conda activate mlops-py311 # Verify Python version python --version # Should show Python 3.11.x

# For each lesson, install requirements cd Day\ 1/lesson-2-git/ pip install -r requirements.txt # Repeat for other lessons as needed

Begin with Day 1 lessons in order:

1. Git fundamentals → Day 1/lesson-2-git/
2. MLflow tracking → Day 1/lesson-3-mlflow/
3. Docker containerization → Day 1/lesson-4-docker/
4. ML pipelines → Day 1/lesson-5-ml-pipeline/
5. CI/CD automation → Day 1/lesson-6-ci-cd/

Then progress to Day 2 for production deployment: 6. Kubernetes basics → Day 2/lesson-2-kubernetes-basics/ 7. Auto-scaling → Day 2/lesson-5-kubernetes-hpa/ 8. A/B testing → Day 2/lesson-7-ab-testing/ 9. Complete MLOps → Day 2/lesson-10-mlops-practices/

Day 1: Foundation ├── Git Version Control ├── MLflow Experiment Tracking ├── Docker Containerization ├── ML Pipeline Development └── CI/CD Automation ↓ Day 2: Production ├── Kubernetes Deployment ├── Auto-scaling (HPA) ├── A/B Testing └── Complete MLOps Workflow 

# Error: AttributeError: 'EntryPoints' object has no attribute 'get' # Solution: Use Python 3.11 conda create -n mlops-py311 python=3.11 conda activate mlops-py311

# macOS AirPlay uses port 5000 # Solution: Use alternative ports mlflow ui --port 5001 python -m mlflow server --port 5001 --host 0.0.0.0

# Ensure Docker is running docker --version docker ps

# Check cluster status kubectl cluster-info kubectl get nodes

• Check individual lesson README files for specific troubleshooting
• Review error messages in terminal output
• Ensure all prerequisites are properly installed
• Verify Python version is 3.11 for MLflow compatibility

┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ Development │ │ Experiment │ │ Deployment │ │ │ │ Tracking │ │ │ │ • Git │───▶│ • MLflow │───▶│ • Docker │ │ • Local Dev │ │ • Model Reg │ │ • Kubernetes │ │ • Testing │ │ • Metrics │ │ • Monitoring │ └─────────────────┘ └─────────────────┘ └─────────────────┘ │ │ │ ▼ ▼ ▼ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ CI/CD Pipeline│ │ Model Serving │ │ Observability │ │ │ │ │ │ │ │ • GitHub │ │ • Flask API │ │ • Prometheus │ │ • Actions │ │ • FastAPI │ │ • Grafana │ │ • Automation │ │ • A/B Testing │ │ • HPA │ └─────────────────┘ └─────────────────┘ └─────────────────┘ 

We welcome contributions! Here's how you can help:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

• Additional datasets and use cases
• More advanced Kubernetes configurations
• Additional monitoring and observability tools
• Performance optimization techniques
• Security best practices

This project is licensed under the MIT License - see the LICENSE file for details.

• O'Reilly Media for the educational framework
• MLflow team for the excellent experiment tracking tool
• Kubernetes community for container orchestration
• Open source contributors who made these tools possible

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Documentation: Check individual lesson README files

Ready to start your MLOps journey? 🚀

Begin with Day 1: MLOps Foundation and work your way through to Day 2: Applied MLOps for a complete production-ready MLOps experience!