comments from Santhosh

Author: sdgilley

articles/machine-learning/service/how-to-create-your-first-pipeline.md

@@ -97,7 +97,7 @@ blob_input_data = DataReference(
     path_on_datastore="20newsgroups/20news.pkl")
 ```
 
-Intermediate data (or output of a step) is represented by a [PipelineData](https://docs.microsoft.com/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py) object. `output_data1` is produced as the output of a step, and used as the input of one or more future steps. `PipelineData` introduces a data dependency between steps, and creates an implicit execution order in the pipeline.
+Intermediate data (or output of a step) is represented by a [PipelineData](https://docs.microsoft.com/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py) object. `output_data1` is produced as the output of a step, and used as the input of one or more future steps. `PipelineData` introduces a data dependency between steps, and creates an implicit execution order in the pipeline. This object will be used later when creating pipeline steps.
 
 ```python
 from azureml.pipeline.core import PipelineData
@@ -110,7 +110,7 @@ output_data1 = PipelineData(
 
 ## Set up compute target
 
-In Azure Machine Learning, the term computes__ (or __compute target__) refers to the machines or clusters that perform the computational steps in your machine learning pipeline.   See [compute targets for model training](how-to-set-up-training-targets.md) for a full list of compute targets and how to create and attach them to your workspace.  The process for creating and or attaching a compute target is the same regardless of whether you are training a model or running a pipeline step. After you create and attach your compute target, use the `ComputeTarget` object in your [pipeline step](#steps).
+In Azure Machine Learning, the term __computes__ (or __compute target__) refers to the machines or clusters that perform the computational steps in your machine learning pipeline.   See [compute targets for model training](how-to-set-up-training-targets.md) for a full list of compute targets and how to create and attach them to your workspace.  The process for creating and or attaching a compute target is the same regardless of whether you are training a model or running a pipeline step. After you create and attach your compute target, use the `ComputeTarget` object in your [pipeline step](#steps).
 
 > [!IMPORTANT]
 > Performing management operations on compute targets is not supported from inside remote jobs. Since machine learning pipelines are submitted as a remote job, do not use management operations on compute targets from inside the pipeline.