First, let's look at data binding. This allows you to seamlessly load TensorFlow Lite models (and others) and add them to your view hierarchy. Learn how to add models to your project, bind them to views, handle input/output tensors, and more. Next, work together to implement a sample app for image classification using ML model binding.

Next, we'll look at how data binding works, binding UI components in an XML layout directly to a data source in code. Extend data binding to also bind machine learning model inputs and outputs to integrate ML models into your apps. Learn best practices for updating your UI when model data changes.

We also discuss considerations for optimizing and validating the model for use on large-scale devices. Learn how tools like TensorFlow Lite Converter, ML Kit, and AutoML streamline retraining and keep your models up-to-date.

Finally, we discuss our continued investment in machine learning for Android and highlights of recent releases. Discover new components and frameworks to integrate smarter AI into your apps. Get a glimpse into the future of on-device machine learning on Android and the possibilities it brings.

In this session, you'll learn everything you need to know to integrate machine learning models into your own Android apps using Jetpack. Developers of all levels will benefit from an overview of how Jetpack enables the next generation of intelligent apps.