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

Clone the Repository:

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

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

Install Dependencies:
```
pip install -r requirements.txt
```
Run the Application:
```
python app/server.py
```
Visit http://127.0.0.1:5000 in your web browser to view the app.

Docker Deployment

Build the Docker Image:
```
docker build -t mlops-app .
```
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

Continuous Integration:
- Build the Docker image.
- Run tests (add your tests in the workflow).
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

Create a .github/workflows directory in your repository.

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

Specify your project's license here, typically MIT or similar.

EzioDEVio / MLOps