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# Deploy
This section focuses on deploying real-world models. It contains
the following documents:
* [Distributed TensorFlow](../deploy/distributed.md), which explains how to create
a cluster of TensorFlow servers.
* [How to run TensorFlow on Hadoop](../deploy/hadoop.md), which has a highly
self-explanatory title.
* [How to run TensorFlow with the S3 filesystem](../deploy/s3.md), which explains how
to run TensorFlow with the S3 file system.
* The entire document set for [TensorFlow serving](/serving), an open-source,
flexible, high-performance serving system for machine-learned models
designed for production environments. TensorFlow Serving provides
out-of-the-box integration with TensorFlow models.
[Source code for TensorFlow Serving](https://github.com/tensorflow/serving)
is available on GitHub.
[TensorFlow Extended (TFX)](/tfx) is an end-to-end machine learning platform for
TensorFlow. Implemented at Google, we've open sourced some TFX libraries with the
rest of the system to come.
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