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Machine Learning for Mobile

You're reading from  Machine Learning for Mobile

Product type Book
Published in Dec 2018
Publisher Packt
ISBN-13 9781788629355
Pages 274 pages
Edition 1st Edition
Languages
Authors (2):
Revathi Gopalakrishnan Revathi Gopalakrishnan
Profile icon Revathi Gopalakrishnan
Avinash Venkateswarlu Avinash Venkateswarlu
Profile icon Avinash Venkateswarlu
View More author details

Table of Contents (19) Chapters

Title Page
Copyright and Credits
About Packt
Contributors
Preface
1. Introduction to Machine Learning on Mobile 2. Supervised and Unsupervised Learning Algorithms 3. Random Forest on iOS 4. TensorFlow Mobile in Android 5. Regression Using Core ML in iOS 6. The ML Kit SDK 7. Spam Message Detection 8. Fritz 9. Neural Networks on Mobile 10. Mobile Application Using Google Vision 11. The Future of ML on Mobile Applications 1. Question and Answers 2. Other Books You May Enjoy Index

Creating a text recognition app using Firebase on-cloud APIs


In this section, we are going to convert the on-device app to a cloud app. The difference is that on-device apps download the model and store it on the device. This allows for a lower inference time, allowing the app to make quick predictions.

By contrast, cloud-based apps upload the image to the Google server, meaning inference will happen there. It won't work if you are not connected to the internet.

In this case, why use a cloud-based model? Because on-device, the model has limited space and processing hardware, whereas Google's servers are scalable. The Google on-cloud text recognizer model is also able to decode multiple languages.

To get started, you need a Google Cloud subscription. Follow these steps:

  • Go to your Firebase project console
  • In the menu on the left, you will see that you are currently on the Spark Plan (the free tier)
  • Click Upgrade, and follow the instructions to upgrade to the Blaze Plan, which is pay-as-you-go
  • You...
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