Machine Learning Model Deployment with Flask, React & NodeJS

Understand how the web connects the browser to servers using http/https, tls, and html/css/javascript; see how a machine learning model is deployed with Flask and accessed via APIs.

Explore front end development by building a simple index.html with HTML, CSS, and JavaScript, then use the browser inspector to debug across devices.

Clarify the differences between back end and front end in web development using a house analogy, and show Python and JavaScript deploying models online with React.

Explore Bootstrap, a popular CSS framework, and learn to integrate it via CDN for mobile-first, responsive styling of your web app.

Explore the basics of front end development by learning why NodeJS is used, why React is a great library, and how to start and review your first React project files.

Learn how Node.js runs JavaScript outside the browser with the V8 engine to build fast, scalable web apps, and how React and Create React App use npm.

Install Node.js from nodejs.org by choosing the lts or current version on Windows and running the installer. Verify the setup by opening a terminal and typing node -v.

React is a JavaScript library for building user interfaces on the client side with reusable components and fast performance via the virtual DOM. It powers mobile apps via React Native.

Create a new React project with Create React App to bootstrap a front end app, then run cd web app and npm start to launch localhost 3000.

Review the React project structure created with Create React App, including package.json, dependencies, scripts, and the public and src folders with index.js and App.js, rendering on localhost 3000.

Clean up the React project by removing testing libraries and scripts, deleting setupTest.js and app.test.js, removing the eslint config and web vitals, then run npm start to verify localhost 3000.

Explore the core functionalities of React, build components, manage state, fetch data from an API, and create a styled form to connect with a Flask API.

Create a new React component in user.js, export it, and import it into app.js to render it. Understand default and named exports and the basic import-export workflow.

Explore how to pass data between react components with props. See how to send name and age as props and how to trigger actions with function props and onClick.

Learn how to manage data with React states using useState, and build a live counter that updates on button clicks without reloading the page.

Build our first React form using a Form component with name and email state, updated by onChange and submitted with preventDefault to prepare data for a Flask API.

Fetch data from the Random User API in a React app by wiring a fetch data button, storing the first user in state, and rendering the user’s name and email.

Explore backend aspects of deploying a machine learning model using flask, including building a flask project, model architecture, and saving weights for deployment.

Explore Flask as a lightweight Python web framework for deploying models, wrapping Werkzeug and Jinja templating, handling http requests, and enabling communication with other services.

Install flask with pip or conda, create app.py with a Flask app and a root route returning a greeting, then run flask run to view it locally on port 5000.

Explore a linear overview from data preparation to model deployment, showing how to deploy and share a data science model with Flask, APIs, and user feedback.

Build a k-nearest neighbors model from the Pima Indians diabetes data to predict diabetes risk from user input. Save weights with pickle for deployment in a Flask, React, NodeJS app.

Save and load your machine learning model weights using pickle, including pickle.dump to create a binary file, with notes on joblib as an alternative for larger data, aiding deployment.

learn to save and load neural network weights with PyTorch, using state_dict and torch.save, test net in Visual Studio Code, and prepare weights for deployment to front-end and back-end apps.

Explore the fundamentals of APIs, including what an application programming interface is, how web APIs communicate via http, urls, get and post requests, and restful principles.

Credit Abhay Parashar and demonstrate deploying a Cayenne-based model, linking RDS to web development, building weights, and serving the model as an api with Node and React.

Build a flask api around your data science model by loading a weights pickle file and rendering index.html, then handle post and get requests to return predictions.

Build and deploy a diabetes prediction app by transforming form input into a pandas dataframe, using predict_proba, and formatting results in Flask, with debugging and template setup.

Learn to debug and test a Flask app in development, ensure 200 status and index.html rendering, and understand production security with the Werkzeug debugger for QA.

Learn to test and debug APIs with Postman, sending get requests to a local Flask app and validating a 200 status, while exploring headers, params, and future deployments.

Learn how to move your React and Flask projects from development to real servers, deploying both the front end and back end to Heroku.

Explore popular build tools for front-end development, including Webpack, Rollup, esbuild, Snowpack, Vite, and Parcel; compare pros, cons, learning curves, and use cases for production and libraries.

Learn to set up and deploy a machine learning API on Heroku, using Git and GitHub to push a live app, with a free hobby tier and cost considerations.

Install Git to enable version control and deploy your model to Heroku as an API, using Git workflows with Heroku or GitHub for version tracking and debugging.

Deploy your model to Heroku as an API by logging in, creating an app, and pushing with git; enable Flask CORS to connect with the front end.

Learn how to test your RDS model with Postman by sending get requests to the predict endpoint, verify 200 responses, and validate data flow.

Explore how HTML and a proc file support deploying a Flask-based API to Heroku, detailing the predict endpoint, render templates, and Gunicorn integration.

Build the React frontend with Create React App, run npm run build to trigger Webpack, and generate a static build folder containing CSS, JavaScript, and media files for deployment.

Deploy your React frontend to a Heroku server using automatic or manual GitHub deployments, after creating a Heroku account and app and selecting a region to view the live site.

Develop the React front end for the diabetes prediction app by building a form that sends inputs to the Flask API and displays results, delivering a live Heroku deployment.

Build a React form for the diabetes prediction model, defining fields such as pregnancies, glucose, blood pressure, BMI, age, and more, to submit to a Flask API.

Connect the front end to a Flask backend using a fetch post request with form data for the diabetes prediction model, and display the result under the form.

Enhance the form user experience by adding a loading state that disables the submit button, shows dynamic text, a clear prediction option, and required fields with basic styling updates.

Deploy the frontend to Heroku using GitHub deployment, configure the app, connect to the Flask backend, and verify by submitting the form to confirm the live app.

Deploy your React front end to GitHub Pages by building a static build and publishing via gh-pages. Ensure GitHub Pages serve static files only, so back-end apps cannot be deployed.

Deploy your frontend with Netlify by connecting your GitHub repository, configuring build and publish options, and securing HTTPS for a production-ready site.

Deploy your frontend with AWS Amplify by connecting a GitHub repo, configuring build settings and environment variables, and managing the deployment from the Amplify dashboard.