TensorFlow Serving is a powerful and flexible system designed for serving machine learning models in production environments. It simplifies the deployment of models, allowing developers to easily manage different versions and algorithms while maintaining a consistent server architecture and API. TensorFlow Serving is particularly beneficial for applications requiring high performance and scalability, as it can leverage powerful computational resources like GPUs and TPUs for faster inference. With its out-of-the-box integration with TensorFlow models, it also supports the extension to serve other types of models, making it a versatile choice for machine learning operations.