[docs] Ray Serve Documentation Overhaul (#8524)

doc/source/serve/tutorials/index.rst

@@ -0,0 +1,19 @@
+=========
+Tutorials
+=========
+
+Below are a list of tutorials that you can use to learn more about the different pieces of 
+Ray Serve functionality and how to integrate different modeling frameworks.
+
+.. toctree::
+   :caption: Serve Tutorials
+   :name: serve-tutorials
+   :maxdepth: -1
+
+   tensorflow.rst
+   pytorch.rst
+   sklearn.rst
+
+
+Other Topics:
+- :doc:`../deployment`

doc/source/serve/tutorials/pytorch.rst

@@ -9,10 +9,10 @@ In particular, we show:
 - How to load the model from PyTorch's pre-trained modelzoo.
 - How to parse the JSON request, transform the payload and evaluated in the model.
 
-Please see the :ref:`overview <rayserve-overview>` to learn more general information about Ray Serve.
+Please see the :doc:`../key-concepts` to learn more general information about Ray Serve.
 
 This tutorial requires Pytorch and Torchvision installed in your system. Ray Serve
-is :ref:`framework agnostic <serve_frameworks>` and work with any version of PyTorch.
+is framework agnostic and work with any version of PyTorch.
 
 .. code-block:: bash
 

doc/source/serve/tutorials/sklearn.rst

@@ -9,9 +9,9 @@ In particular, we show:
 - How to load the model from file system in your Ray Serve definition
 - How to parse the JSON request and evaluated in sklearn model
 
-Please see the :ref:`overview <rayserve-overview>` to learn more general information about Ray Serve.
+Please see the :doc:`../key-concepts` to learn more general information about Ray Serve.
 
-Ray Serve supports :ref:`arbitrary frameworks <serve_frameworks>`. You can use any version of sklearn.
+Ray Serve is framework agnostic. You can use any version of sklearn.
 
 .. code-block:: bash
 

doc/source/serve/tutorials/tensorflow.rst

@@ -9,9 +9,9 @@ In particular, we show:
 - How to load the model from file system in your Ray Serve definition
 - How to parse the JSON request and evaluated in Tensorflow
 
-Please see the :ref:`overview <rayserve-overview>` to learn more general information about Ray Serve.
+Please see the :doc:`../key-concepts` to learn more general information about Ray Serve.
 
-Ray Serve makes it easy to deploy models from :ref:`all popular frameworks <serve_frameworks>`.
+Ray Serve is framework agnostic you can use any version of Tensorflow.
 However, for this tutorial, we use Tensorflow 2 and Keras. Please make sure you have
 Tensorflow 2 installed.