https://github.com/eziodevio/mlops
MLOps deploying house estimate model
https://github.com/eziodevio/mlops
flask-application mlops mlops-project mlops-workflow
Last synced: 6 months ago
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MLOps deploying house estimate model
- Host: GitHub
- URL: https://github.com/eziodevio/mlops
- Owner: EzioDEVio
- Created: 2024-04-25T12:27:45.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-11-06T14:07:21.000Z (over 1 year ago)
- Last Synced: 2025-01-19T08:16:27.274Z (over 1 year ago)
- Topics: flask-application, mlops, mlops-project, mlops-workflow
- Language: HTML
- Homepage:
- Size: 35.2 KB
- Stars: 6
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
[](https://github.com/EzioDEVio/MLOps/actions/workflows/main.yml) [](https://github.com/EzioDEVio/MLOps/actions/workflows/GHCR.yml)
# 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:**
```bash
git clone https://github.com/EzioDEVio/MLOps.git
cd MLOps
```
2. **Create and Activate a Virtual Environment (optional):**
Windows:
```bash
python -m venv venv
venv\Scripts\activate
```
macOS/Linux:
```bash
python3 -m venv venv
source venv/bin/activate
```
3. **Install Dependencies:**
```bash
pip install -r requirements.txt
```
4. **Run the Application:**
```bash
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:**
```bash
docker build -t mlops-app .
```
2. **Run the Docker Container:**
```bash
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`):**
```yaml
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.
.