Thanks to everyone who joined our virtual I/O 2021 livestream! While we couldn’t meet in person, we hope we were able to make the event more accessible than ever. In this article, we’re recapping a few of the updates we shared during the keynote. You can watch the keynote below, and you can find recordings of every talk on the TensorFlow [YouTube channel](https://www.youtube.com/tensorflow). Here’s a summary of a few announcements by product area (and there’s more in the videos, so be sure to check them out, too).
## **TensorFlow for Mobile and Web**
The TensorFlow Lite runtime will be bundled with Google Play services
Let’s start with the announcement that the TensorFlow Lite runtime is going to be bundled with Google Play services, meaning you don’t need to distribute it with your app. This can greatly reduce your app’s bundle size. Now you can distribute your model without needing to worry about the runtime. You can [sign up](https://g.co/androidml-eap) for an early access program today, and we expect a full rollout later this year.
You can now run TensorFlow Lite models on the web
All your TensorFlow Lite models can now directly be run on the web in the browser with the new [TFLite Web APIs](https://github.com/tensorflow/tfjs-models/tree/master/tasks) that are unified with [TensorFlow.js](https://www.tensorflow.org/js). This task-based API supports running all [TFLite Task Library](https://www.tensorflow.org/lite/inference_with_metadata/task_library/overview) models for image classification, objection detection, image segmentation, and many NLP problems. It also supports running arbitrary, [custom TFLite models](https://github.com/tensorflow/tfjs/tree/master/tfjs-tflite) with easy, intuitive TensorFlow.js compatible APIs. With this option, you can unify your mobile and web ML development with a single stack.
A new On-Device Machine Learning site
We understand that the most effective developer path to reach Android, the Web and iOS isn’t always the most obvious. That’s why we created a new [On-Device Machine Learning site](https://g.co/on-device-ml) to help you navigate your options, from turnkey to custom models, from cross platform mobile, to in-browser. It includes pathways to take you from an idea to a deployed app, with all the steps in between.
Performance profiling
When it comes to performance, we’re also working on additional tooling for Android developers. [TensorFlow Lite ](https://www.tensorflow.org/lite/performance/measurement)includes built-in support for Systrace, integrating seamlessly with perfetto for Android 10.
And perf improvements aren’t limited to Android – for iOS developers TensorFlow Lite comes with built-in support for signpost-based profiling. When you build your app with the trace option enabled, you can run the Xcode profiler to see the signpost events, letting you dive deeper, and seeing all the way down to individual ops during execution.
