TensorFlow Serving is a high-performance serving system designed specifically for machine learning models, particularly in production environments. It is part of TensorFlow Extended (TFX) and simplifies the deployment of machine learning models, allowing for easy integration and management of different model versions and algorithms while maintaining a consistent server architecture and APIs ².

One of the key features of TensorFlow Serving is its ability to serve models via gRPC and REST protocols, enabling them to function as web services without the need for additional applications. This allows for multiple versions of multiple models to be served simultaneously, which is beneficial for A/B testing and gradual rollouts of new models ³.

To use TensorFlow Serving, models must be saved in TensorFlow’s SavedModel format, which allows for fine-tuning and modifications post-training. The recommended approach for deployment is through Docker, as it provides an easy way to manage dependencies and ensure compatibility, especially when using GPU support ³⁵.