In this session, my major aim would be to provide an overview of the different tools one could use to power their Android apps with Machine Learning and also discuss the new additions for Machine Learning in Android 11 specifically the Model Binding Plugin and ML Kit. I would first go on to explain the standard procedure of using pre-trained models with MLKit. I would show how we could take the idea of MLKit forward and use pre-trained models from TensorFlow Hub to run right in the app, which would provide support to build high-quality machine learning apps based on models contributed from the community. I would then show how we could use custom TFLite models in Android apps, I would also talk about TensorFlow Model Maker and ML Model binding plugin in Android Studio through which I plan to show how easy it is to now use custom TF Lite models in Android apps. With Android 11 the NN API now supports Asymmetric integer weights making model sizes and inferences even smaller opening up an even larger opportunities for edge ML.

1. Superpower your
Android Apps with
ML: Android 11
Rishit Dagli
High School
TEDx, TED-Ed Speaker
rishit_dagli
Rishit-dagli

2. ● 11 Grade Student
● TEDx and Ted-Ed Speaker
● ♡ Hackathons and competitions
● ♡ Research
● My coordinates - www.rishit.tech
$whoami
rishit_dagli Rishit-dagli

3. ● Sayak Paul (ML GDE)
● Khanh LeViet (Google)
● Hoi Lam (Google)
Acknowledgements

4. ● Mobile Devs looking for ways to build
smarter apps
● Mobile Devs looking for ways to
integrate ML in their existing apps
easily
Ideal Audience

5. Why care about ML in
Android?

6. Why care about on-device ML
in Android?

11. ● Power Consumption
Why should you care?

12. ● Power Consumption
● Inference Time
Why should you care?

13. ● Power Consumption
● Inference Time
● Network availability
Why should you care?

14. ● Power Consumption
● Inference Time
● Network availability
● Privacy
Why should you care?

15. ML Model
Binding Plugin

16. Easier to use
Enable Hardware acceleration
Faster Development
ML Model Binding Plugin
What’s new for on-device ML in Android?

17. Importing a
TF Lite Model

18. Importing a
TF Lite Model

19. Importing a
TF Lite Model

20. Using the
TF Lite Model

21. Creating an Instance of model

22. Processing images

23. Passing in data

24. Passing in data

25. Passing in data

26. Passing in data

27. Adding labels

28. We are done 🚀

29. GPU Acceleration 🚅

30. A new
ML Kit

31. Face
detection
Barcode
scanning
Image
labeling
Smart Reply
Language
Identification
Vision Natural Language
Object
detection
and tracking
On-device
Translation
Text recognition
Digital Ink
Recognition
Pose Detection
N
EW
N
EW

32. On-Device ML
Better customizability
Generic use cases
A new ML Kit
What does the latest ML Kit focus on?

33. Setup the model

34. Customize the model

36. ● High performance
● Super easy to use
● High customization too!
TF Lite Model Maker

37. TF Hub tfhub.dev

38. TF Hub

39. bit.ly/a11-ml
Demos!

40. Q & A
rishit_dagli Rishit-dagli

41. Thank You
rishit_dagli Rishit-dagli