MLOps Recommendation System

This project is a simple product recommendation system built using a Flask web application, a machine learning model based on Singular Value Decomposition (SVD), and Docker for containerization. The system predicts product ratings for users based on historical interaction data.

Machine Learning

Features

• Flask Web Application: A web interface where users can select user IDs and product IDs to get product recommendations.
• Machine Learning Model: The recommendation model is trained using the scikit-surprise library with SVD for collaborative filtering.
• Dockerized Application: The app is fully containerized using Docker and can be deployed on any machine with Docker support.
• Responsive Web Interface: The web app is styled using Bootstrap for a clean and responsive design.
• Modular Project Structure: Cleanly separated logic for model loading, web interface, and prediction.

Project Structure

MLOps_Flask_Recommendation_System/
├── app.py                 # Main Flask application
├── Dockerfile             # Dockerfile to containerize the Flask app
├── docker-compose.yml     # Docker Compose for managing the Docker container
├── requirements.txt       # Python dependencies for the project
├── model/
│   └── svd_model.pkl      # Trained SVD model
└── templates/
    ├── index.html         # HTML template for the form (input)
    └── result.html        # HTML template for displaying recommendation results

Getting Started

Prerequisites

• Docker: You need Docker installed to run the application.
• Docker Compose: Ensure Docker Compose is installed for easy container management.

Running the Application

1. Clone the repository:

   git clone https://github.com/Simacoder/MLOPs_Recommendations.git
   cd MLOps_Flask_Recommendation_System

2. Build and run the Docker container using Docker Compose:

   docker-compose up --build

3. Open your browser and go to http://localhost:5000.

4. Select a user ID and product ID from the dropdowns to get a product recommendation based on the trained model.

Stopping the Application

To stop the running application, you can use:

docker-compose down

Customization

• Model: You can retrain the model using the scikit-surprise library and replace the existing svd_model.pkl in the model/ directory.
• Data: Modify or add new users and products by replacing the model training dataset.

Future Enhancements

• Add a database (e.g., PostgreSQL) for storing user and product data.
• Implement continuous integration (CI) and continuous deployment (CD) pipelines using Jenkins or GitHub Actions.
• Deploy the application to a cloud platform like AWS, GCP, or Azure.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Authors

• Simanga Mchunu