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Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter

You're reading from   Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter Build scalable real-world projects to implement end-to-end neural networks on Android and iOS

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Product type Paperback
Published in Apr 2020
Publisher Packt
ISBN-13 9781789611212
Length 380 pages
Edition 1st Edition
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Authors (2):
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Rimjhim Bhadani Rimjhim Bhadani
Author Profile Icon Rimjhim Bhadani
Rimjhim Bhadani
Anubhav Singh Anubhav Singh
Author Profile Icon Anubhav Singh
Anubhav Singh
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Table of Contents (13) Chapters Close

Preface 1. Introduction to Deep Learning for Mobile 2. Mobile Vision - Face Detection Using On-Device Models FREE CHAPTER 3. Chatbot Using Actions on Google 4. Recognizing Plant Species 5. Generating Live Captions from a Camera Feed 6. Building an Artificial Intelligence Authentication System 7. Speech/Multimedia Processing - Generating Music Using AI 8. Reinforced Neural Network-Based Chess Engine 9. Building an Image Super-Resolution Application 10. Road Ahead 11. Other Books You May Enjoy Appendix

Deploying a webhook to Cloud Functions for Firebase

Now that we're done creating the logic of the webhook, it is pretty simple to deploy it using Cloud Functions on Firebase. Follow these steps to do so:

  1. Click on the Fulfillment button on the left navigation of the Dialogflow Console. Enable the Inline Editor to be able to add your webhook and deploy it directly to Cloud Functions.

You will have to clear out the default boilerplate code that is in the Inline Editor to do this.

  1. Paste the code in the editor from the previous section into the index.js tabbed navigation pill and click on Deploy.
Remember that the environment that's being used for deployment is Node.js, so index.js is the file that shall contain all your business logic. The package.json file manages the required packages for your project. 

Using Cloud Functions comes with the advantage of the...

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