Style and spelling tweaks

Author: Larry O'Brien

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

@@ -39,7 +39,7 @@ The Azure cloud provides several other pipelines, each with a different purpose.
 | -------- | --------------- | -------------- | ------------ | -------------- | --------- | 
 | Model orchestration (Machine learning) | Data scientist | Azure Machine Learning Pipelines | Kubeflow Pipelines | Data -> Model | Distribution, caching, code-first, reuse | 
 | Data orchestration (Data prep) | Data engineer | [Azure Data Factory pipelines](https://docs.microsoft.com/azure/data-factory/concepts-pipelines-activities) | Apache Airflow | Data -> Data | Strongly-typed movement. Data-centric activities. |
-| Code & app orchestration (CI/CD) | App Developer / Ops | [Azure DevOps Pipelines](https://azure.microsoft.com/services/devops/pipelines/) | Jenkins | Code + Model -> App/Service | Most open and flexibile activity support, approval queues, phases with gating | 
+| Code & app orchestration (CI/CD) | App Developer / Ops | [Azure DevOps Pipelines](https://azure.microsoft.com/services/devops/pipelines/) | Jenkins | Code + Model -> App/Service | Most open and flexible activity support, approval queues, phases with gating | 
 
 
 ## What can Azure ML pipelines do?
@@ -59,7 +59,7 @@ With Azure Machine Learning, you can use various toolkits and frameworks, such a
 
 You can [track the metrics for your pipeline experiments](https://docs.microsoft.com/azure/machine-learning/how-to-track-experiments) directly in  Azure portal or your [workspace landing page (preview)](https://ml.azure.com). After a pipeline has been published, you can configure a REST endpoint, which allows you to rerun the pipeline from any platform or stack.
 
-In short, all of the complex tasks of the machine learning lifecycle can be helped with pipelines. Other Azure pipeline technologies have their own strengths, such as [Azure Data Factory pipelines](https://docs.microsoft.com/azure/data-factory/concepts-pipelines-activities) for working with data and [Azure Pipelines](https://azure.microsoft.com/services/devops/pipelines/) for continuous integration and deployment. But if your focus is machine learning, Azure Machine Learning pipelines are likely to be the best choice for your workflow needs. 
+In short, all of the complex tasks of the machine learning lifecycle can be helped with pipelines. Other Azure pipeline technologies have their own strengths. [Azure Data Factory pipelines](https://docs.microsoft.com/azure/data-factory/concepts-pipelines-activities) excels at working with data and [Azure Pipelines](https://azure.microsoft.com/services/devops/pipelines/) is the right tool for continuous integration and deployment. But if your focus is machine learning, Azure Machine Learning pipelines are likely to be the best choice for your workflow needs. 
 
 ## What are Azure ML pipelines?
 
@@ -199,9 +199,9 @@ The key advantages of using pipelines for your machine learning workflows are:
 
 ### Choosing the proper PipelineStep subclass
 
-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 `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 just by 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. 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.
+The `DataTransferStep` makes it easy to move data between data sources and sinks. The code to do this transfer 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