Mention the nmt-wizard-docker integration for serving
@@ -67,3 +67,5 @@ Depending on your production requirements, you might need to build a simple prox
* manage multiple TensorFlow Serving instances (possibly running on multiple hosts) and keep persistent channels to them
* apply tokenization and detokenization
+
+For example, take a look at the OpenNMT-tf integration in the project [`nmt-wizard-docker`](https://github.com/OpenNMT/nmt-wizard-docker/blob/master/frameworks/opennmt_tf/entrypoint.py) which wraps a TensorFlow serving instance with a custom processing layer and REST API.
