Fix minor formatting errors

Author: Kim

doc/frameworks/xgboost/using_xgboost.rst

@@ -395,10 +395,10 @@ Bring your own model
 
 You can deploy an XGBoost model that you trained outside of SageMaker by using the Amazon SageMaker XGBoost container.
 Typically, you save an XGBoost model by pickling the ``Booster`` object or calling ``booster.save_model``.
-The XGBoost `built-in algorithm mode<https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html#xgboost-modes>`_
+The XGBoost `built-in algorithm mode <https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html#xgboost-modes>`_
 supports both a pickled ``Booster`` object and a model produced by ``booster.save_model``.
 For a sample notebook that shows to use the XGBoost built-in algorith mode to load a pre-existing XGBoost model,
-see `Amazon SageMaker XGBoost Bring Your Own Model<https://github.com/awslabs/amazon-sagemaker-examples/blob/master/advanced_functionality/xgboost_bring_your_own_model/xgboost_bring_your_own_model.ipynb>`_.
+see `Amazon SageMaker XGBoost Bring Your Own Model <https://github.com/awslabs/amazon-sagemaker-examples/blob/master/advanced_functionality/xgboost_bring_your_own_model/xgboost_bring_your_own_model.ipynb>`_.
 
 You can also deploy an XGBoost model by using XGBoost as a framework.
 By using XGBoost as a framework, you have more flexibility.
@@ -407,7 +407,7 @@ To deploy an XGBoost model by using XGBoost as a framework, you need to:
 - Create the XGBoostModel object.
 
 Write an inference script
-"""""""""""""""""""""""""
+^^^^^^^^^^^^^^^^^^^^^^^^^
 
 You must create an inference script that implements (at least) the ``model_fn`` function that calls the loaded model to get a prediction.
 
@@ -417,7 +417,7 @@ For information about how to write an inference script, see `SageMaker XGBoost M
 Pass the filename of the inference script as the ``entry_point`` parameter when you create the `XGBoostModel` object.
 
 Create a XGBoostModel object
-""""""""""""""""""""""""""""
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Now call the ``sagemaker.xgboost.model.XGBoostModel`` constructor to create a model object,
 and then call its ``deploy()`` method to deploy your model for inference.
@@ -426,14 +426,14 @@ and then call its ``deploy()`` method to deploy your model for inference.
 
     from sagemaker import get_execution_role
     role = get_execution_role()
-    
+
     xgboos_model = XGBoostModel(
         model_data="s3://my-bucket/my-path/model.tar.gz",
         role=role,
         entry_point="inference.py",
         framework_version="1.0-1"
     )
-    
+
     predictor = xgboost_model.deploy(
         instance_type='ml.c4.xlarge',
         initial_instance_count=1