message: Adding documentation on how to load & serve a model with the
  TensorFlow Serving Model Server.
diff: @@ -285,7 +285,7 @@ with tf.Session(graph=tf.Graph()) as sess:
  ```
  -### Loading a Savedmodel in C++
  +### Loading a SavedModel in C++
  The C++ version of the SavedModel
  [loader](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/cc/saved_model/loader.h)
  @@ -303,6 +303,30 @@ LoadSavedModel(session_options, run_options, export_dir, {kSavedModelTagTrain},
  &bundle);
  ```
  +### Loading and Serving a SavedModel in TensorFlow Serving
  +
  +You can easily load and serve a SavedModel with the TensorFlow Serving Model
  +Server binary. See [instructions](https://www.tensorflow.org/serving/setup#installing_using_apt-get)
  +on how to install the server, or build it if you wish.
  +
  +Once you have the Model Server, run it with:
  +```
  +tensorflow_model_server --port=port-numbers --model_name=your-model-name --model_base_path=your_model_base_path
  +```
  +Set the port and model_name flags to values of your choosing. The
  +model_base_path flag expects to be to a base directory, with each version of
  +your model residing in a numerically named subdirectory. If you only have a
  +single version of your model, simply place it in a subdirectory like so:
  +* Place the model in /tmp/model/0001
  +* Set model_base_path to /tmp/model
  +
  +Store different versions of your model in numerically named subdirectories of a
  +common base directory. For example, suppose the base directory is `/tmp/model`.
  +If you have only one version of your model, store it in `/tmp/model/0001`. If
  +you have two versions of your model, store the second version in
  +`/tmp/model/0002`, and so on. Set the `--model-base_path` flag to the base
  +directory (`/tmp/model`, in this example). TensorFlow Model Server will serve
  +the model in the highest numbered subdirectory of that base directory.
  ### Standard constants
  