TensorFlow Serving: Deploy Models Easily

TensorFlow Serving is an open-source software library used to deploy machine learning models and provide real-time predictions. It allows for easy deployment of trained TensorFlow models to production environments, offering predictive services through RESTful API or gRPC interface.

To deploy a TensorFlow model, you first need to export the model as a SavedModel format, then load the SavedModel onto a server using TensorFlow Serving. You can then send input data to the server and retrieve the model’s predictions through HTTP requests or gRPC calls. TensorFlow Serving also supports model versioning, dynamic scaling, and load balancing, making it easier and more efficient to deploy and manage models in a production environment.