Book Image

Mastering Firebase for Android Development

By : Ashok Kumar S
Book Image

Mastering Firebase for Android Development

By: Ashok Kumar S

Overview of this book

Firebase offers a wide spectrum of tools and services to help you develop high-quality apps in a short period of time. It also allows you to build web and mobile apps quickly without managing the infrastructure.Mastering Firebase for Android Development takes you through the complete toolchain of Firebase,including the latest tools announced in Google IO 2018 such as Firebase ML-Kit, FireStore, and Firebase Predictions. The book begins by teaching you to configure your development environment with Firebase and set up a different structure for a Firebase real-time database. As you make your way through the chapters, you’ll establish the authentication feature in Android and explore email and phone authentication for managing the on-boarding of users. You’ll be taken through topics on Firebase crash reporting, Firebase functions, Firebase Cloud, Firebase Hosting, and Cloud Messaging for push notifications and explore other key areas in depth. In the concluding chapters, you will learn to use Firebase Test Lab to test your application before using Firebase Performance Monitoring to trace performance setbacks. By the end of the book, you will be well equipped with the Firebase ecosystem, which will help you find solutions to your common application development challenges.
Table of Contents (23 chapters)
Title Page
Copyright and Credits
Packt Upsell
Application Usage Measuring and Notification, Firebase Analytics, and Cloud Messaging
Bringing Everyone on the Same Page, Firebase Invites, and Firebase App Indexing
Making a Monetary Impact and Firebase AdMob and AdWords
Flexible NoSQL and Cloud Firestore
Analytics Data, Clairvoyant, Firebase Predictions

Custom models 

ML developers who are skilled in the area of writing Machine Learning code can use TensorFlow Lite and can write models with ML Kit. The models can be hosted in a Firebase cloud. The few key capabilities of custom models are Firebase cloud hosting, on-device ML inference, automatic model fallbacks, automatic model updates, and so on. 

To build custom model ML Kit projects, the following steps need to be carried out: 

  • Train your ML model 
  • Convert the model to TensorFlow Lite for working with ML Kit 
  • Host the model in the Firebase console 
  • Use the models for inference

Before we focus on the custom model, we need to make sure that the project is connected to Firebase SDK and also add the following dependency:

dependencies {
// ...

  implementation ''

Now we are almost ready to explore the custom models. To host the models in a Firebase cloud, follow these instructions:

  • Visit the Firebase console, go to the ML Kit section, and...