How to Save, Load, and Deploy Models Using TensorFlow SavedModel

This in-depth guide explores TensorFlow’s SavedModel format—a versatile way to save, share, and deploy complete ML models across environments. It walks through exporting models from Keras, using TensorFlow Serving, loading models in C++ and Python, and customizing export signatures. The article also covers command-line tools (saved_model_cli), proto-splitting for large models, and fine-tuning workflows—making it a one-stop resource for any developer deploying ML in production.

Content Overview

• Creating a SavedModel from Keras
• Running a SavedModel in TensorFlow Serving
• The SavedModel format on disk
• Saving a custom model
• Loading and using a custom model
• Basic fine-tuning
• General fine-tuning
• Specifying signatures during export
• Proto-splitting
• Load a SavedModel in C++
• Details of the SavedModel command line interface
• Install the SavedModel CLI
• Overview of commands
• show command
• run command

A SavedModel contains a complete TensorFlow program, including trained parameters (i.e, tf.Variables ...