Superpower Your Android apps with ML: Android 11 | Devfest 2020

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.

Transcript

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