Google offers a range of solutions to use on-device ML to unlock new experiences in your apps. To tackle common challenges, we provide easy-to-use turn-key APIs. For more custom use-cases, we help you train your model, integrate it in your app and deploy it in production.

This site helps guide you to the right Google solutions and tools that meet your needs.

The benefits of On-Device Machine Learning

Unlock new user experiences by processing text, audio and video in real-time
Perform inference locally without sending user data to the cloud
No need for a network connection or running a service in the cloud

Turn-key solutions

To tackle common tasks with ML, we offer easy-to-use, production-ready APIs through the ML Kit SDK. These are built on high quality pre-trained models and are easy to integrate in Android and iOS apps.

Custom solutions and tools

We offer off-the-shelf pre-trained models that can be deployed on mobile and web apps. For more specific use cases, we offer tools for retraining existing models or train them from scratch.

Build your first on-device ML app

The learning pathways below provide a step-by-step guide to help you write your first on-device machine learning app.
Write an app that can classify sounds in the environment around you. In this example, identify birds based on their song.
Build an app that takes a picture and gives you a list of labels that describe the image. Train a model to recognize newer labels and integrate it in your app.
Detect specific objects within an image and draw bounding boxes around them. Train a model to identify new objects and integrate the model in your app.
Create an app that determines if your users are spamming your chatroom.
Use Machine Learning in your web site to help filter comment spam.
Take a picture with your camera and search for matching products.

On-device ML in the real world

Here are some examples of how on-device machine learning is used by developers to tackle real world challenges.
Lookout by Google helps make the physical world more accessible, for users who are blind or low-vision. From helping users to quickly skim text, to capturing full documents, to identifying objects and packaged food, Lookout takes advantage of on-device ML models powered by TensorFlow Lite.
Learn how adidas is using ML Kit’s Object Detection & Tracking API in their Android and iOS app to create an intuitive in-store visual search experience and make it easier for their customers to find and try-on their next set of adidas shoes. On-device ML makes it possible to seamlessly detect shoes in real-time and within seconds returns an image recognition match against hundreds of products.
Modiface uses the TensorFlow.js face detection model to identify key facial features and combine them with WebGL shaders, allowing users to digitally try on makeup for L'Oreal brand products entirely in the browser, preserving user privacy.
With hundreds of VSCO photo presets to choose from, helping users find and try new presets was a challenge. But with on device ML, the VSCO app now understands uploaded images and suggests presets that best complement them via the "For this photo" feature.