Deploy machine learning models on mobile and IoT devices

TensorFlow Lite is an open source deep learning framework for on-device inference.

See the guide

Guides explain the concepts and components of TensorFlow Lite.

See examples

Explore TensorFlow Lite Android and iOS apps.

See tutorials

Learn how to use TensorFlow Lite for common use cases.

How it works

Pick a model

Pick a new model or retrain an existing one.


Convert a TensorFlow model into a compressed flat buffer with the TensorFlow Lite Converter.


Take the compressed .tflite file and load it into a mobile or embedded device.


Quantize by converting 32-bit floats to more efficient 8-bit integers or run on GPU.

Solutions to common problems

Explore optimized TF Lite models and on-device ML solutions for mobile and edge use cases.

Image classification

Identify hundreds of objects, including people, activities, animals, plants, and places.

Object detection

Detect multiple objects with bounding boxes. Yes, dogs and cats too.

Question answering

Use a state-of-the-art natural language model to answer questions based on the content of a given passage of text with BERT.

News & announcements

Check out our blog for additional updates, and subscribe to our monthly TensorFlow newsletter to get the latest announcements sent directly to your inbox.

May 20, 2021  
Explore TensorFlow Lite for Microcontrollers Experiments and enter the TF Micro challenge

Visit the site to see projects combining Arduino and TensorFlow to create awesome experiences and useful tools. Find helpful links for creating your own experiments and learn how you can participate in the TF Micro Challenge.

May 20, 2021  
Train your own custom object detection model with TensorFlow Lite

Learn how to train a custom object detection model and deploy it to an Android app with just a few lines of code. All you need are Android Studio and a web browser. No machine learning knowledge is required.

May 18, 2021  
Explore the On-Device Machine Learning website

Discover solutions to help you integrate machine learning in your mobile and web apps, and new Google Developers learning pathways to guide you through common ML scenarios and custom use cases.

May 18, 2021  
Easily deploy TensorFlow Lite models to the web (Google I/O)

To bridge the gap between mobile and web ML development, you can easily deploy the TensorFlow Lite Task Library to the web with the power of WebAssembly.