Merge pull request #113847 from MicrosoftDocs/j-martens-patch-22

Update concept-data-ingestion.md

Author: American-Dipper

articles/machine-learning/concept-data-ingestion.md

@@ -1,5 +1,5 @@
 ---
-title: Data ingestion options 
+title: Data ingestion & automation 
 titleSuffix: Azure Machine Learning
 description: Learn about data ingestion options  for training your machine learning models.
 services: machine-learning
@@ -13,18 +13,20 @@ ms.date: 02/26/2020
 
 ---
 
-# Data ingestion in Azure Machine Learning
+# Data ingestion options for Azure Machine Learning workflows
 
-In this article, you learn the pros and cons of the following data ingestion options available with Azure Machine Learning. 
+In this article, you learn the pros and cons of data ingestion options available with Azure Machine Learning. 
 
-1. [Azure Data Factory](#use-azure-data-factory) pipelines
-2. [Azure Machine Learning Python SDK](#use-the-python-sdk)
+Choose from:
++ [Azure Data Factory](#azure-data-factory) pipelines, specifically built to extract, load, and transform data
 
-Data ingestion is the process in which unstructured data is extracted from one or multiple sources and then prepared for training machine learning models. It's also time intensive, especially if done manually, and if you have large amounts of data from multiple sources. Automating this effort frees up resources and ensures your models use the most recent and applicable data.
++ [Azure Machine Learning Python SDK](#azure-machine-learning-python-sdk), providing a custom code solution for basic data ingestion tasks.
+
++ a combination of both
 
-Azure Data Factory (ADF) is specifically built to extract, load, and transform data, however the Python SDK let's you develop a custom code solution for basic data ingestion tasks. If neither are quite what you need, you can also use ADF and the Python SDK together to create an overall data ingestion workflow that meets your needs. 
+Data ingestion is the process in which unstructured data is extracted from one or multiple sources and then prepared for training machine learning models. It's also time intensive, especially if done manually, and if you have large amounts of data from multiple sources. Automating this effort frees up resources and ensures your models use the most recent and applicable data.
 
-## Use Azure Data Factory
+## Azure Data Factory
 
 [Azure Data Factory](https://docs.microsoft.com/azure/data-factory/introduction) offers native support for data source monitoring and triggers for data ingestion pipelines.  
 
@@ -51,7 +53,7 @@ These steps and the following diagram illustrate Azure Data Factory's data inges
 
 Learn how to build a data ingestion pipeline for Machine Learning with [Azure Data Factory](how-to-data-ingest-adf.md).
 
-## Use the Python SDK 
+## Azure Machine Learning Python SDK 
 
 With the [Python SDK](https://docs.microsoft.com/python/api/overview/azure/ml), you can incorporate data ingestion tasks into an [Azure Machine Learning pipeline](how-to-create-your-first-pipeline.md) step.
 
@@ -69,5 +71,7 @@ In the following diagram, the Azure Machine Learning pipeline consists of two st
 
 ## Next steps
 
-* Learn how to build a data ingestion pipeline for Machine Learning with [Azure Data Factory](how-to-data-ingest-adf.md)
-* Learn how to automate and manage the development life cycles of your data ingestion pipelines with [Azure Pipelines](how-to-cicd-data-ingestion.md).
+Follow these how-to articles:
+* [Build a data ingestion pipeline with Azure Data Factory](how-to-data-ingest-adf.md)
+
+* [Automate and manage data ingestion pipelines with Azure Pipelines](how-to-cicd-data-ingestion.md).