AI/ML Ecosystem Guide

This guide covers the main aspects of the AI/ML ecosystem, from frameworks to tools and best practices.

Contents

• Deep Learning Frameworks
• MLOps Tools
• Best Practices
• Deployment
• Monitoring

Structure

The folder is organized as follows:

ai-ecosystem/
├── frameworks/         # Deep learning frameworks
├── mlops/             # MLOps tools and practices
├── deployment/        # Deployment guides
└── monitoring/        # Monitoring tools

Resources

• Official framework documentation
• Best practice guides
• Code examples
• Tutorials

Capstone Project: Real Estate Price Prediction

A full-stack ML application that predicts real estate prices using Streamlit, FastAPI, H2O, and MLOps.

🚀 Features

• Interactive user interface with Streamlit
• RESTful API with FastAPI
• Automated ML model with H2O AutoML
• Complete MLOps pipeline with DVC and MLflow
• Monitoring with Prometheus
• Automated deployment with GitHub Actions
• Unit tests with pytest

🛠️ Technologies Used

• Frontend: Streamlit
• Backend: FastAPI
• ML: H2O AutoML
• MLOps: DVC, MLflow
• Containerization: Docker, Docker Compose
• CI/CD: GitHub Actions
• Monitoring: Prometheus
• Tests: pytest

📋 Prerequisites

• Python 3.9+
• Docker and Docker Compose
• Git
• GitHub account (for CI/CD)

🚀 Installation

1. Clone the project:

git clone https://github.com/votre-username/real-estate-ml.git
cd real-estate-ml

2. Create the virtual environment:

python -m venv venv
source venv/bin/activate  # Linux/Mac
venv\Scripts\activate     # Windows

3. Install the dependencies:

pip install -r requirements.txt

4. Prepare the data:

dvc pull
dvc repro prepare

5. Train the model:

dvc repro train

6. Launch the application:

docker-compose up

🌐 Accessing the Services

• Streamlit frontend: http://localhost:8501
• FastAPI API: http://localhost:8000
• API documentation: http://localhost:8000/docs
• MLflow: http://localhost:5000
• Prometheus: http://localhost:9090

📊 Project Structure

real-estate-ml/
├── data/
│   ├── raw/
│   │   └── housing_data.csv
│   └── processed/
├── models/
│   ├── trained/
│   └── deployed/
├── src/
│   ├── data/
│   ├── models/
│   ├── api/
│   └── frontend/
├── tests/
├── notebooks/
├── configs/
└── .github/
    └── workflows/

🔧 Configuration

The config.yaml file contains all of the project’s configuration:

• Data parameters
• Model configuration
• API parameters
• Frontend configuration
• MLflow parameters
• Monitoring thresholds

🧪 Tests

Run the tests:

pytest tests/

📈 Monitoring

The following metrics are monitored:

• Total number of predictions
• Prediction latency
• Error rate
• Model performance

🤝 Contributing

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

📝 License

This project is licensed under the MIT License. See the LICENSE file for more details.

👥 Authors

• Your Name - @votre_twitter

🙏 Acknowledgments

• Streamlit
• FastAPI
• H2O.ai
• DVC
• MLflow