AIExplainer
Programming Frameworks Intermediate 2 min read

What is SavedModel?

A format for saving and loading machine learning models in TensorFlow

SavedModel is a way to store and retrieve machine learning models, including their weights, architecture, and other necessary information, making it easier to deploy and use them in different applications and environments

Think of SavedModel like a blueprint for a house. Just as a blueprint contains all the necessary information to build a house, SavedModel contains all the necessary information to recreate and use a machine learning model

A company developing a self-driving car might use SavedModel to save and load the machine learning models that power the car's autonomous driving system, allowing them to easily update and improve the system

SavedModel is used to save and load machine learning models in TensorFlow, allowing developers to deploy and use their models in a variety of settings, such as web applications, mobile apps, and embedded systems

Some people might think that SavedModel is specific to TensorFlow, but it can be used with other machine learning frameworks as well. Others might think that SavedModel is only used for deployment, but it can also be used for model sharing and collaboration

SavedModel was introduced in TensorFlow 1.0 as a way to simplify the process of saving and loading machine learning models

model checkpoint model snapshot TensorFlow model

Three products for different needs — explore what’s relevant to you.