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A flask based Handwritten character recognition web app deployed to a remote server to interact with a machine learning model. Python, Pytorch, Flask, Docker.

Python 58.44% Dockerfile 2.31% JavaScript 19.99% CSS 3.19% HTML 16.07%

deploy-ml-web-app's Introduction

Handwritten Character Recognizer

Try the app here : https://chetan-ml-app.herokuapp.com/

In this project, I will demonstratesteps to train and deploy a Machine Learning model with a web interface.

Tools required:

  • A server for inference: Cloud instances, or simply your powerful laptop.
  • Data: EMNIST Balanced an extended version of MNIST with 131,600 characters, 47 balanced classes.
  • PyTorch: To train the deep learning model.
  • Docker: Make our life easier to create a container for our application.
  • Flask: For API and user interface.

Workflow:

The steps are as follows:

  • Prepare the docker image using Dockerfile.
  • Get data and train the machine learning model on Colab.
  • Show a simple inference example.
  • Build the API and the user-interface

Now we need to think about how to “ship” our model. This what we call packaging. It is simply to put the inference steps together and have a simple API to use it later.

If you would like to deploy your model on a cloud provider, I would suggest using free heroku servers.(limitations apply). Lately they started supporting docker images to be deployted as applications.

Folow these steps:

  • heroku container:login
  • heroku create
  • heroku container:push web
  • heroku container:release web
  • heroku open

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