Introducing the Model Optimization Toolkit for TensorFlow

TensorFlow introduced a new optimization toolkit in TensorFlow: a suite of techniques that developers, both novice and advanced, can use to optimize machine learning models for deployment and execution.

While they expect that these techniques will be useful for optimizing any TensorFlow model for deployment, they are particularly important for TensorFlow Lite developers who are serving models on devices with tight memory, power constraints, and storage limitations. If you haven’t tried out TensorFlow Lite yet, you can find out more about it here.

View full article on TensorFlow’s blog.