Deploying a Deep Learning Model on Web and Mobile Applications

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prerequisites
intermediate Python • basic Git • basics of TensorFlow • basic machine learning techniques • basics of neural networks
skills learned
train a deep learning model • deploy a deep learning model using TensorFlow.js • create web applications • deploy your application using Docker
Nidhin Pattaniyil and Reshama Shaikh
3 weeks · 7-10 hours per week · INTERMEDIATE
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catalog / Data Science

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Look inside

In this liveProject, you’ll take on the role of a data engineer working for an app development company. Your boss has a great idea for a new app that can automatically identify what food is shown in an image—perfect for when you want to post your lunch on social media! They have asked you to build it for them.

Your challenge is to train an image classification system using TensorFlow and then deploy that system to production so that it can run in the web browser and as a mobile application. To do this, you’ll make use of the Python and TensorFlow ecosystems to create your model, then package it for deployment using Docker and Expo.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

prerequisites

The liveProject is for intermediate Python programmers who know the basics of data science and some experience training an image classifier using deep learning who want to improve their experience of deploying models into production.

TOOLS
  • Intermediate Python
  • Basic Git
  • Basics of TensorFlow
TECHNIQUES
  • Basic machine learning techniques
  • Basics of neural networks

Note: This project uses Heroku to run the completed app in the cloud. Heroko incurs a cost. The "Eco and Basic" option, which starts at $5 per month, will be sufficient for this project.

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