TensorFlow Serving is a system from Google for deploying trained machine-learning models in production environments. It was one of the early dedicated answers to a question that the research frameworks of the time largely ignored: once you have a trained model, how do you serve predictions from it reliably, at scale, and keep updating it without taking the service down.
Its design separates the serving infrastructure from the models it serves. The same server architecture and the same client APIs stay constant while the models behind them change, so a team can push a new model version, or run experiments with multiple versions side by side, without rewriting the serving layer or interrupting traffic. It provides out-of-the-box integration with TensorFlow models and is extensible to other model and data types, and it is built for high performance under production load. This versioned, framework-integrated approach influenced the later generation of more general serving systems.
Why a business reader should care: TensorFlow Serving illustrates the discipline of treating model deployment as standing infrastructure, where models are versioned, swappable artifacts behind a stable interface, which is what makes continuous improvement of a live AI service safe.