From 9a721ed71a134c3adf483d3cbfca12dece8352da Mon Sep 17 00:00:00 2001
From: Edward Oakes <ed.nmi.oakes@gmail.com>
Date: Mon, 18 May 2020 11:29:38 -0500
Subject: [PATCH] Link to serve in tune overview (#8487)

---
 doc/source/tune.rst | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/doc/source/tune.rst b/doc/source/tune.rst
index e0de269b0..5c837f521 100644
--- a/doc/source/tune.rst
+++ b/doc/source/tune.rst
@@ -14,10 +14,12 @@ Tune is a Python library for experiment execution and hyperparameter tuning at a
   * Natively `integrates with optimization libraries <tune-searchalg.html>`_ such as `HyperOpt <https://github.com/hyperopt/hyperopt>`_, `Bayesian Optimization <https://github.com/fmfn/BayesianOptimization>`_, and `Facebook Ax <http://ax.dev>`_.
   * Choose among `scalable algorithms <tune-schedulers.html>`_ such as `Population Based Training (PBT)`_, `Vizier's Median Stopping Rule`_, `HyperBand/ASHA`_.
   * Visualize results with `TensorBoard <https://www.tensorflow.org/get_started/summaries_and_tensorboard>`__.
+  * Move your models from training to serving on the same infrastructure with `Ray Serve`_.
 
 .. _`Population Based Training (PBT)`: tune-schedulers.html#population-based-training-pbt
 .. _`Vizier's Median Stopping Rule`: tune-schedulers.html#median-stopping-rule
 .. _`HyperBand/ASHA`: tune-schedulers.html#asynchronous-hyperband
+.. _`Ray Serve`: rayserve/overview.html
 
 **Want to get started?** Head over to the :ref:`60 second Tune tutorial <tune-60-seconds>`.