Training a TensorFlow model represents a major milestone, but the work doesn't end there. To generate value, models must be put into operation efficiently and reliably. This chapter addresses the critical phase of moving models from the development environment to production systems or edge devices.

You will learn the standard methods for packaging TensorFlow models for deployment using the SavedModel format. We will examine TensorFlow Serving as a dedicated solution for high-performance model serving, including how to interact with deployed models using common protocols. Furthermore, we'll cover optimization strategies, such as quantization, aimed at reducing model size and improving inference speed. Finally, we introduce TensorFlow Lite for converting and preparing models for deployment on mobile, embedded, and other resource-constrained environments. The focus is on the practical steps required to make your trained models accessible and performant for real-world applications.