| title | ModelForge Backend |
|---|---|
| emoji | 🤖 |
| colorFrom | indigo |
| colorTo | purple |
| sdk | docker |
| app_port | 7860 |
| pinned | false |
| short_description | AutoML backend — FastAPI + sklearn + LightGBM + XGBoost |
ModelForge Backend is a server-side application designed to support an automated machine learning workflow. It allows users to upload datasets, process them, and run machine learning models with minimal manual intervention.
The goal of this project is to simplify the end-to-end machine learning pipeline, making it more accessible and efficient.
The backend provides functionality for handling structured datasets and preparing them for machine learning tasks. It includes data preprocessing steps such as handling missing values and encoding categorical variables. The system is designed to support multiple machine learning models and can be extended to include model evaluation and optimization.
This project is built using Python and relies on common machine learning and backend development libraries. The application is designed to run as a web service using a lightweight framework.
The repository includes the main application file along with configuration and deployment files.
- main.py contains the core backend logic and API endpoints
- requirements.txt lists all required dependencies
- Dockerfile is used for containerizing the application
- Procfile defines process execution for certain deployment platforms
- railway.toml and render.yaml are used for deployment configuration
To run the project on your local machine, install the required dependencies and start the server.
pip install -r requirements.txt python main.py
If the application uses an ASGI server, you may run it using:
uvicorn main:app --reload
The project is configured for deployment on platforms such as Railway and Render. It can also be deployed using Docker for a more controlled environment.
For Railway deployment, connect the repository and ensure the correct start command is set.
uvicorn main:app --host 0.0.0.0 --port $PORT
The project can be extended to include advanced model tuning, better evaluation metrics, and a more interactive API layer. Logging, authentication, and scalability improvements can also be added.
ModelForge Backend is a foundation for building automated machine learning systems. It focuses on reducing manual effort while maintaining flexibility for further enhancements.