Address comments from Eric

Author: yangaws

src/sagemaker/workflow/README.rst

@@ -10,11 +10,11 @@ is a platform that enables you to programmatically author, schedule, and monitor
 you can build a workflow for SageMaker training, hyperparameter tuning, batch transform and endpoint deployment.
 You can use any SageMaker deep learning framework or Amazon algorithms to perform above operations in Airflow.
 
-There are two ways to build SageMaker workflow. Using Airflow SageMaker operators or using Airflow PythonOperator.
+There are two ways to build a SageMaker workflow. Using Airflow SageMaker operators or using Airflow PythonOperator.
 
-1. SageMaker Operators: Since Airflow 1.10.1, we contributed special operators just for SageMaker operations.
-Each operator takes a configuration dictionary that defines the corresponding operation. And we provide APIs to
-generate the configuration dictionary in SageMaker Python SDK. Current supported SageMaker operators are:
+1. SageMaker Operators: In Airflow 1.10.1, the SageMaker team contributed special operators for SageMaker operations.
+Each operator takes a configuration dictionary that defines the corresponding operation. We provide APIs to generate
+the configuration dictionary in the SageMaker Python SDK. Currently, the following SageMaker operators are supported:
 
 * ``SageMakerTrainingOperator``
 * ``SageMakerTuningOperator``
@@ -23,20 +23,20 @@ generate the configuration dictionary in SageMaker Python SDK. Current supported
 * ``SageMakerEndpointConfigOperator``
 * ``SageMakerEndpointOperator``
 
-2. PythonOperator: Airflow built-in operator that could execute Python callables. You could use SageMaker Python SDK to
-customize your workflow with PythonOperator.
+2. PythonOperator: Airflow built-in operator that executes Python callables. You can use the PythonOperator to execute
+operations in the SageMaker Python SDK to create a SageMaker workflow.
 
 Using Airflow on AWS
 ~~~~~~~~~~~~~~~~~~~~
 
-Turbine is an open source AWS CloudFormation template to create Airflow resources stack on AWS.
+Turbine is an open-source AWS CloudFormation template that enables you to create an Airflow resource stack on AWS.
 You can get it here: https://github.com/villasv/aws-airflow-stack
 
 Using Airflow SageMaker Operators
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Starting from Airflow 1.10.1, you could use SageMaker operators in Airflow. All SageMaker operators take a
-configuration dictionary that can be easily generated by SageMaker Python SDK. For example:
+Starting with Airflow 1.10.1, you can use SageMaker operators in Airflow. All SageMaker operators take a configuration
+dictionary that can be generated by the SageMaker Python SDK. For example:
 
 .. code:: python
 
@@ -63,7 +63,7 @@ configuration dictionary that can be easily generated by SageMaker Python SDK. F
                                                    data=your_transform_data_s3_uri,
                                                    content_type='text/csv')
 
-Now we can pass these configurations to related SageMaker operators and create the workflow:
+Now you can pass these configurations to the corresponding SageMaker operators and create the workflow:
 
 .. code:: python
 
@@ -99,8 +99,8 @@ Using Airflow Python Operator
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 `Airflow PythonOperator <https://airflow.apache.org/howto/operator.html?#pythonoperator>`_
-is a built-in operator that can execute any Python callables. If you want to build the SageMaker workflow in a more
-flexible way,  you could write your python callables for SageMaker operations using SageMaker Python SDK. For example:
+is a built-in operator that can execute any Python callable. If you want to build the SageMaker workflow in a more
+flexible way, write your python callables for SageMaker operations by using the SageMaker Python SDK.
 
 .. code:: python
 
@@ -125,7 +125,7 @@ flexible way,  you could write your python callables for SageMaker operations us
         transformer = estimator.transformer(instance_count=1, instance_type='ml.c4.xlarge')
         transformer.transform(data, content_type='text/csv')
 
-Then you could build your workflow using PythonOperator with Python callables defined above:
+Then build your workflow by using the PythonOperator with the Python callables defined above:
 
 .. code:: python
 
@@ -158,5 +158,5 @@ Then you could build your workflow using PythonOperator with Python callables de
 
     transform_op.set_upstream(train_op)
 
-A workflow with SageMaker training and batch transform is finished! In this way, you could customize your Python
-callables with SageMaker Python SDK according to your needs and build more flexible and powerful workflow.
+A workflow that runs a SageMaker training job and a batch transform job is finished. You can customize your Python
+callables with the SageMaker Python SDK according to your needs, and build more flexible and powerful workflows.