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Superpower Your Android apps with ML: Android 11

Rishit Dagli
September 14, 2020

Superpower Your Android apps with ML: Android 11

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 even larger opportunities for edge ML.
If time persists, I would also show demos about the above topics.

Rishit Dagli

September 14, 2020
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  1. rishit.tech $whoami • 11 Grade Student • TEDx and Ted-Ed

    Speaker • ♡ Hackathons and competitions • ♡ Research • My coordinates - https://www.rishit.tech rishit_dagli Rishit-dagli
  2. rishit.tech Ideal Audience • Mobile Devs looking for ways to

    build smarter apps • Mobile Devs looking for ways to integrate ML in their existing apps easily
  3. Created by Rishit Dagli for his talk at GDG Ahmedabad

    This slide is skipped while presenting
  4. Created by Rishit Dagli for his talk at GDG Ahmedabad

    This slide is skipped while presenting
  5. rishit.tech ML Model Binding Plugin What’s new for on-device ML

    in Android? Easier to use Enable Hardware acceleration Faster Development
  6. rishit.tech A new ML Kit What does the latest ML

    Kit focus on? On-Device ML Better customizability Generic use cases
  7. rishit.tech 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
  8. rishit.tech TF Lite Model Maker • High performance • Super

    easy to use • High customization too!