MLOps Application for Real Estate Price Prediction

This project demonstrates the application of Machine Learning Operations (MLOps) principles to a real estate price prediction model. It includes setting up a Flask application to serve predictions from a trained model and deploying this application using Docker and integrating it into a CI/CD workflow.

Project Structure

• app/: Contains the Flask application files.
  • server.py: The Flask server file with API endpoints.
• models/: Contains the trained model file.
  • california_housing_model.joblib: Pre-trained scikit-learn model.
• templates/: HTML files for the application frontend.
• static/: CSS and JS files for the frontend.
• Dockerfile: Contains all the commands to assemble the app Docker image.
• requirements.txt: List of packages required for the application.

Setup and Installation

Prerequisites

• Python 3.8+
• pip
• virtualenv (optional)

Local Setup

1. Clone the Repository:

   git clone https://github.com/EzioDEVio/MLOps.git
   cd MLOps

2. Create and Activate a Virtual Environment (optional):

   Windows:

   python -m venv venv
   venv\Scripts\activate

   macOS/Linux:

   python3 -m venv venv
   source venv/bin/activate

3. Install Dependencies:

   pip install -r requirements.txt

4. Run the Application:

   python app/server.py

   Visit http://127.0.0.1:5000 in your web browser to view the app.

Docker Deployment

1. Build the Docker Image:

   docker build -t mlops-app .

2. Run the Docker Container:

   docker run -p 5000:5000 mlops-app

   The application should now be accessible at http://localhost:5000.

CI/CD Integration

This project uses GitHub Actions for Continuous Integration and Continuous Deployment (CI/CD).

Workflow

1. Continuous Integration:

   • Build the Docker image.
   • Run tests (add your tests in the workflow).

2. Continuous Deployment:

   • Push the Docker image to a registry (e.g., Docker Hub).
   • Deploy the image to a cloud service (e.g., AWS, Azure).

Setup GitHub Actions

1. Create a .github/workflows directory in your repository.

2. Add a workflow file (e.g., ci-cd.yml):

   name: CI/CD Pipeline
   
   on:
     push:
       branches: [ main ]
     pull_request:
       branches: [ main ]
   
   jobs:
     build:
       runs-on: ubuntu-latest
       steps:
       - uses: actions/checkout@v2
       - name: Set up Python
         uses: actions/setup-python@v2
         with:
           python-version: '3.8'
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
           pip install -r requirements.txt
       - name: Build Docker image
         run: docker build -t mlops-app .
       # Add additional steps for testing and deployment

Contributing

Contributions to this project are welcome! Please fork the repository and submit a pull request with your proposed changes.

License

MIT.

.