Fix typo

Author: cdpark

articles/machine-learning/v1/how-to-use-automlstep-in-pipelines.md

@@ -1,7 +1,7 @@
 ---
 title: Use Automated Machine Learning in Pipelines
 titleSuffix: Azure Machine Learning
-description: Learn how to use AutoMLStep to set up automated machine learning in your pipelines.
+description: Learn how to use AutoMLStep to set up automated machine learning in your pipeline.
 services: machine-learning
 ms.service: azure-machine-learning
 ms.subservice: automl
@@ -13,15 +13,15 @@ ms.topic: how-to
 ms.custom: UpdateFrequency5, devx-track-python, automl, sdkv1
 ---
 
-# Use AutoML in an Azure Machine Learning pipeline with Python
+# Use AutoML in an Azure Machine Learning pipeline
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 
 [!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
 
-Azure Machine Learning's automated machine learning (AutoML) capability helps you discover high-performing models without you reimplementing every possible approach. Combined with Azure Machine Learning pipelines, you can create deployable workflows that can quickly discover the algorithm that works best for your data.
+Azure Machine Learning's automated machine learning (AutoML) capability helps you discover high-performing models without reimplementing every possible approach. Combined with Azure Machine Learning pipelines, you can create deployable workflows that quickly discover the algorithm that works best for your data.
 
-This article explains how to efficiently join a data preparation step to an automated machine learning step. AutoML can quickly discover the algorithm that works best for your data, while putting you on the road to MLOps and model lifecycle operationalization with pipelines.
+This article explains how to efficiently join a data preparation step to an automated machine learning step by using Python. AutoML can quickly discover the algorithm that works best for your data, while putting you on the road to MLOps and model lifecycle operationalization with pipelines.
 
 ## Prerequisites
 
@@ -205,7 +205,7 @@ Since the `output_path` is a directory, the call to `to_csv()` specifies the fil
 
 ### Write the data preparation pipeline step (`PythonScriptStep`)
 
-The data preparation code described must be associated with a `PythonScripStep` object to be used with a pipeline. The path to which the CSV output is written is generated by a `OutputFileDatasetConfig` object. The resources prepared earlier, such as the `ComputeTarget`, the `RunConfig`, and the `'titanic_ds' Dataset` are used to complete the specification.
+The data preparation code described must be associated with a `PythonScriptStep` object to be used with a pipeline. The path to which the CSV output is written is generated by a `OutputFileDatasetConfig` object. The resources prepared earlier, such as the `ComputeTarget`, the `RunConfig`, and the `'titanic_ds' Dataset` are used to complete the specification.
 
 ```python
 from azureml.data import OutputFileDatasetConfig
@@ -232,7 +232,7 @@ Configuring an automated machine learning pipeline step is done with the `AutoML
 
 ### Send data to AutoMLStep
 
-In a machine learning pipeline, the input data must be a `Dataset` object. The highest-performing way is to provide the input data in the form of `OutputTabularDatasetConfig` objects. You create an object of that type with the  `read_delimited_files()` on a `OutputFileDatasetConfig`, such as the `prepped_data_path`, such as the `prepped_data_path` object.
+In a machine learning pipeline, the input data must be a `Dataset` object. The highest-performing way is to provide the input data in the form of `OutputTabularDatasetConfig` objects. You create an object of that type with the `read_delimited_files()` on a `OutputFileDatasetConfig`, such as the `prepped_data_path`, such as the `prepped_data_path` object.
 
 ```python
 # type(prepped_data) == OutputTabularDatasetConfig
@@ -256,7 +256,7 @@ Comparing the two techniques:
 | Registered `Dataset` | Lower performance |
 | | Can be generated in many ways | 
 | | Data persists and is visible throughout workspace |
-| | [Notebook showing registered `Dataset` technique](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb)
+| | [Notebook showing registered `Dataset` technique](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb) |
 
 ### Specify AutoML outputs