Serving Models
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Introduction
TensorFlow Serving is a flexible, high-performance serving system for machine
learning models, designed for production environments. TensorFlow Serving makes
it easy to deploy new algorithms and experiments, while keeping the same server
architecture and APIs. TensorFlow Serving provides out-of-the-box integration
with TensorFlow models, but can be easily extended to serve other types of
models and data.
Detailed developer documentation on TensorFlow Serving is available:
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Last updated 2021-01-28 UTC.
[null,null,["Last updated 2021-01-28 UTC."],[],[],null,["# Serving Models\n\n\u003cbr /\u003e\n\nIntroduction\n------------\n\nTensorFlow Serving is a flexible, high-performance serving system for machine\nlearning models, designed for production environments. TensorFlow Serving makes\nit easy to deploy new algorithms and experiments, while keeping the same server\narchitecture and APIs. TensorFlow Serving provides out-of-the-box integration\nwith TensorFlow models, but can be easily extended to serve other types of\nmodels and data.\n\nDetailed developer documentation on TensorFlow Serving is available:\n\n- [Architecture Overview](https://www.tensorflow.org/tfx/serving/architecture)\n- [Server API](https://www.tensorflow.org/tfx/serving/api_docs/cc/)\n- [REST Client API](https://www.tensorflow.org/tfx/serving/api_rest)"]]
