[docs] Fix typos in documentation (#11414)

doc/source/serve/tutorials/batch.rst

@@ -4,7 +4,7 @@ Batching Tutorial
 =================
 
 In this guide, we will deploy a simple vectorized adder that takes
-a batch of queries and add them at once. In particular, we show:
+a batch of queries and adds them at once. In particular, we show:
 
 - How to implement and deploy Ray Serve model that accepts batches.
 - How to configure the batch size.
@@ -60,7 +60,7 @@ the input value, convert them into an array, and use NumPy to add 1 to each elem
 
 Let's deploy it. Note that in the ``config`` section of ``create_backend``, we
 are specifying the maximum batch size via ``config={"max_batch_size": 4}``. This
-configuration option limits the maximum possible batch size send to the backend.
+configuration option limits the maximum possible batch size sent to the backend.
 
 .. note::
     Ray Serve performs *opportunistic batching*. When a worker is free to evaluate

doc/source/serve/tutorials/pytorch.rst

@@ -12,7 +12,7 @@ In particular, we show:
 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 framework agnostic and work with any version of PyTorch.
+is framework agnostic and works with any version of PyTorch.
 
 .. code-block:: bash
 

doc/source/serve/tutorials/tensorflow.rst

@@ -11,7 +11,7 @@ In particular, we show:
 
 Please see the :doc:`../key-concepts` to learn more general information about Ray Serve.
 
-Ray Serve is framework agnostic you can use any version of Tensorflow.
+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.