This section focuses on deploying real-world models. It contains the following documents:
- Distributed TensorFlow, which explains how to create a cluster of TensorFlow servers.
- How to run TensorFlow on Hadoop, which has a highly self-explanatory title.
- How to run TensorFlow with the S3 filesystem, which explains how to run TensorFlow with the S3 file system.
- The entire document set for TensorFlow 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 is available on GitHub.
TensorFlow Extended (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.