How To Deploy Machine Learning Model On React JS

Machine learning models have become an essential part of many web applications. With the rise of React JS as a popular front-end framework, developers are looking for ways to integrate machine learning models into their React applications. In this article, we will discuss how to deploy a machine learning model on a React JS application.

Step 1: Build your Machine Learning Model

The first step is to build and train your machine learning model using a library like TensorFlow or Scikit-learn. Once your model is trained and ready for deployment, you can proceed to the next step.

Step 2: Convert your Model to a Web-friendly Format

Before deploying your machine learning model, you need to convert it to a web-friendly format such as TensorFlow.js. TensorFlow.js allows you to run machine learning models in the browser, making it easier to integrate with your React JS application.

Step 3: Create a React Component for your Model

Once your model is in a web-friendly format, you can create a React component to integrate it into your application. This component will be responsible for loading the model and making predictions based on user input.

“`javascript
import React, { useState } from ‘react’;
import * as tf from ‘@tensorflow/tfjs’;

const MyModelComponent = () => {
const [model, setModel] = useState(null);

useEffect(() => {
const loadModel = async () => {
const loadedModel = await tf.loadLayersModel(‘path_to_your_model/model.json’);
setModel(loadedModel);
}

loadModel();
}, []);

const handlePrediction = async (inputData) => {
const prediction = await model.predict(inputData);
// Display prediction
}

return (

)
}
“`

Step 4: Deploy your React JS Application

Finally, you can deploy your React JS application with the integrated machine learning model. You can use platforms like Netlify, Vercel, or Heroku to host your application and make it accessible to users.

Conclusion

Integrating machine learning models into React JS applications is a powerful way to enhance user experience and make predictive analysis accessible to a wider audience. By following the steps outlined in this article, you can deploy your machine learning model on a React JS application and create a seamless user experience.