Pulled enterprise version text; added links to Up Next

Author: lobrien

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

@@ -22,7 +22,7 @@ Azure Machine Learning's automated ML capability helps you discover high-perform
 
 * An Azure subscription. If you don't have an Azure subscription, create a free account before you begin. Try the [free or paid version of Azure Machine Learning](https://aka.ms/AMLFree) today.
 
-* An Azure Machine Learning workspace with a type of **Enterprise edition**. See [Create an Azure Machine Learning workspace](how-to-manage-workspace.md).  To upgrade an existing workspace to Enterprise edition, see [Upgrade to Enterprise edition](how-to-manage-workspace.md#upgrade).
+* An Azure Machine Learning workspace. See [Create an Azure Machine Learning workspace](how-to-manage-workspace.md).  
 
 * Basic familiarity with Azure's [automated machine learning](concept-automated-ml.md) and [machine learning pipelines](concept-ml-pipelines.md) facilities and SDK.
 
@@ -437,3 +437,4 @@ Finally, the actual metrics and model are downloaded to your local machine for f
 - Run this Jupyter notebook showing a [complete example of automated ML in a pipeline](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/nyc-taxi-data-regression-model-building.ipynb) that uses regression to predict taxi fares
 - [Create automated ML experiments without writing code](how-to-use-automated-ml-for-ml-models.md)
 - Explore a variety of [Jupyter notebooks demonstrating automated ML](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning)
+- Read about integrating your pipeline in to [End-to-end MLOps](https://docs.microsoft.com/azure/machine-learning/concept-model-management-and-deployment#automate-the-ml-lifecycle) or investigate the [MLOps Github repository](https://github.com/Microsoft/MLOpspython)