Deploying TensorFlow Models

Deploying machine learning models to production environments is a critical step that determines their real-world impact. This chapter shifts its focus from building and training models to the deployment phase, where models are integrated into applications and made accessible for inference in live environments. Effective model deployment ensures that your machine learning solutions can scale, perform, and adapt to real-world demands.

Throughout this chapter, you will gain insights into various methods and best practices for deploying TensorFlow models. You will explore tools and frameworks that facilitate seamless integration, such as TensorFlow Serving and TensorFlow Lite, each tailored for different deployment scenarios. Whether you're deploying in the cloud, on mobile devices, or at the edge, understanding these tools will enable you to choose the right strategy for your specific needs.

Additionally, you will learn how to optimize model performance and manage model versions in production environments. By the end of this chapter, you will be equipped with the knowledge to deploy TensorFlow models effectively, ensuring they deliver value and insights in any environment.