TensorFlow Lite is an open-source deep learning framework that allows developers to deploy machine learning models on mobile and embedded devices. It is designed to work with low-power devices and has a smaller memory footprint compared to the full version of TensorFlow.

One of the main advantages of TensorFlow Lite is its ability to run on a wide range of devices, including smartphones, smart watches, and even microcontrollers. This allows developers to build and deploy machine learning models on devices that are not powerful enough to run the full version of TensorFlow.

TensorFlow Lite also includes a number of tools and libraries that make it easy to convert existing TensorFlow models to the TensorFlow Lite format. This allows developers to use pre-trained models and fine-tune them for specific tasks, rather than having to start from scratch.

Another advantage of TensorFlow Lite is its ability to run on-device inference. This means that the model can make predictions without the need for a connection to a remote server. This is particularly useful for mobile and embedded applications where internet connectivity may be limited or unreliable.

In addition to running on-device inference, TensorFlow Lite also supports edge devices with accelerator like TPU, Edge TPU and Coral. This allows developers to run models on devices with specialized hardware that can significantly improve the performance and speed of model inference.

Overall, TensorFlow Lite is a powerful and flexible framework that allows developers to deploy machine learning models on mobile and embedded devices. Its ability to run on a wide range of devices, convert existing models, and support on-device inference and edge devices with accelerator make it an attractive option for developers looking to build machine learning applications for mobile and embedded devices.