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 an even larger opportunities for edge ML.
If time persists, I would also show demos about the above topics.
Rishit Dagli is a grade 10 student and is a TEDx, TED-Ed Speaker. To share his knowledge with everyone he mentors at TensorFlow UserGroup Mumbai and organizes groups Global AI Mumbai and Kotlin Mumbai and is an international speaker. He is also a Google AI ExploreML facilitator and tries to help spread his knowledge. He is also interested in research and has published multiple research papers in the field of AI and Maths. He has also represented his country in various Hackathons and competitions and even won a few while representing his country.