Creating AI Web Apps using TensorFlow, Google Cloud Platform, and Firebase

From the Sandbox to the Cloud: How to Easily Deploy your Machine Learning Models into Production Web Applications

Introduction

Training Machine Learning models for a web app with ML functionalities is only part of the entire project’s development scope. One often overlooked aspect is going beyond the sandbox and into a production environment. In this article, I will demonstrate how to easily serve a TensorFlow model via a prediction service using Google Cloud Platform (GCP) AI Platform and Cloud Functions. Afterward, I will show how to deploy and host the web client using Firebase to query the model using HTTP requests.

The final project architecture will look similar to the figure below:

Overview of project architecture

Disclaimer: This article will help you get started. This is not a tutorial on Firebase or Google Cloud Platform. For further details, I highly encourage readers to visit the official documentation. The official TensorFlow documentation is an excellent resource for a more in-depth view and details.

Model Training and Saving

Google Colab for Model Training

One great resource for training deep learning models is Google Colab. They provide free GPUs and TPUs, both of which can significantly improve training times. These are provided at no cost, and paid options exist for more advanced use cases. After training, models can be downloaded for further processing and deployment. For details on how to use Colab and begin training your models, I recommend this tutorial.

firebase google-colab tensorflow

Embedding your <image> in google colab . This article is a quick guide to help you embed images in google colab markdown without mounting your google drive!

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