Update phrasing

Author: Larry O'Brien

articles/machine-learning/concept-ml-pipelines.md

@@ -204,7 +204,7 @@ The key advantages of using pipelines for your machine learning workflows are:
 
 The `PythonScriptStep` is the most flexible subclass of the abstract `PipelineStep`. Other subclasses, such as `EstimatorStep` subclasses and `DataTransferStep` can accomplish specific tasks with less code. For instance, an `EstimatorStep` can be created by simply passing in a name for the step, an `Estimator`, and a compute target. Or, you can override inputs and outputs, pipeline parameters, and arguments. For more information, see [Train models with Azure Machine Learning using estimator](how-to-train-ml-models.md). 
 
-The `DataTransferStep` makes it easy to move data between data sources and sinks. This potentially complex task can be done by creating a `DataTransferStep` with a name, references to a data source and a data sink, and a compute target. The notebook [Azure Machine Learning Pipeline with DataTransferStep](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-data-transfer.ipynb) demonstrates this flexibility.
+The `DataTransferStep` makes it easy to move data between data sources and sinks. The code to do this manually is straightforward but repetitive. Instead, you can just create a `DataTransferStep` with a name, references to a data source and a data sink, and a compute target. The notebook [Azure Machine Learning Pipeline with DataTransferStep](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-data-transfer.ipynb) demonstrates this flexibility.
 
 ## Modules