mpolinowski / sklearn-model-deployment

Use Flask, Docker and React.js to Deploy your SKLearn Model to the Web

Serving your SciKit Learn Model as a Prediction API

1. Preparing the ML Model
   • Pick a SKLearn model that fits your dataset
   • Preprocess your dataset and do test trainings / hyperparameter tuning
   • Fit the model to your dataset
   • Pickle the trained model for deployment
2. Preparing the Model API
   • Use Flask to prepare a REST API endpoint for your model
   • Test predictions using HTTP POST requests
3. Containerizing the Application
   • Wrap the Flask application in a Docker image
4. React.js Frontend
   • Write a React.js frontend that can interface with your REST API
5. Serve Frontend using the Flask App
   • Use Vite.js to render the React frontend to static HTML
   • Add a static route to your Flask app to serve the frontend from inside the Docker image