Book Image

Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter

By : Anubhav Singh, Rimjhim Bhadani
Book Image

Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter

By: Anubhav Singh, Rimjhim Bhadani

Overview of this book

Deep learning is rapidly becoming the most popular topic in the mobile app industry. This book introduces trending deep learning concepts and their use cases with an industrial and application-focused approach. You will cover a range of projects covering tasks such as mobile vision, facial recognition, smart artificial intelligence assistant, augmented reality, and more. With the help of eight projects, you will learn how to integrate deep learning processes into mobile platforms, iOS, and Android. This will help you to transform deep learning features into robust mobile apps efficiently. You’ll get hands-on experience of selecting the right deep learning architectures and optimizing mobile deep learning models while following an application oriented-approach to deep learning on native mobile apps. We will later cover various pre-trained and custom-built deep learning model-based APIs such as machine learning (ML) Kit through Firebase. Further on, the book will take you through examples of creating custom deep learning models with TensorFlow Lite. Each project will demonstrate how to integrate deep learning libraries into your mobile apps, right from preparing the model through to deployment. By the end of this book, you’ll have mastered the skills to build and deploy deep learning mobile applications on both iOS and Android.
Table of Contents (13 chapters)

Deploying the model in Flutter

At this point, we have our Firebase authentication application running along with ReCaptcha protection. Now, let's add the final layer of security that won't allow any malicious users to enter the application.

We already know that the model is hosted at the endpoint: http://34.67.126.237:8000/login. We will simply make an API call from within the application, passing in the email and password provided by the user, and get the result value from the model. The value will assist us in judging whether the login was malicious by using a threshold result value.

If the value is less than 0.20, the login will be considered malicious and the following message will be shown on the screen:

Let's now look at the steps to deploy the model in the Flutter application:

  1. First of all, since we are fetching data and will be using network calls, that...