Small TOC change + refresh

Author: nibaccam

articles/machine-learning/concept-data.md

@@ -1,15 +1,15 @@
 ---
 title: Secure data access in the cloud
 titleSuffix: Azure Machine Learning
-description: Learn how to securely connect to your data from Azure Machine Learning, and how to use datasets and datastores for ML tasks. Datastores can store data from an Azure Blob, Azure Data Lake Gen 1 & 2, SQL db, Databricks,...
+description: Learn how to securely connect to your data from Azure Machine Learning, and how to use datasets and datastores for ML tasks. Datastores can store data from an Azure Blob, Azure Data Lake Gen 1 & 2, SQL db, and Azure Databricks.
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
 ms.topic: conceptual
 ms.reviewer: nibaccam
 author: nibaccam
 ms.author: nibaccam
-ms.date: 04/24/2020
+ms.date: 08/31/2020
 ms.custom: devx-track-python
 
 # Customer intent: As an experienced Python developer, I need to securely access my data in my Azure storage solutions and use it to accomplish my machine learning tasks.
@@ -19,11 +19,11 @@ ms.custom: devx-track-python
 
 Azure Machine Learning makes it easy to connect to your data in the cloud.  It provides an abstraction layer over the underlying storage service, so you can securely access and work with your data without having to write code specific to your storage type. Azure Machine Learning also provides the following data capabilities:
 
+*    Interoperability with Pandas and Spark DataFrames
 *    Versioning and tracking of data lineage
 *    Data labeling 
 *    Data drift monitoring
-*    Interoperability with Pandas and Spark DataFrames
-
+    
 ## Data workflow
 
 When you're ready to use the data in your cloud-based storage solution, we recommend the following data delivery workflow. This workflow assumes you have an [Azure storage account](https://docs.microsoft.com/azure/storage/common/storage-quickstart-create-account?tabs=azure-portal) and data in a cloud-based storage service in Azure. 
@@ -68,7 +68,7 @@ Azure Machine Learning datasets are references that point to the data in your st
 
  To interact with your data in storage, [create a dataset](how-to-create-register-datasets.md) to package your data into a consumable object for machine learning tasks. Register the dataset to your workspace to share and reuse it across different experiments without data ingestion complexities.
 
-Datasets can be created from local files, public urls, [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/), or Azure storage services via datastores. To create a dataset from an in memory pandas dataframe, write the data to a local file, like a parquet, and create your dataset from that file.  
+Datasets can be created from local files, public urls, [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/), or Azure storage services via datastores. 
 
 We support 2 types of datasets: 
 

articles/machine-learning/toc.yml

@@ -267,7 +267,7 @@
         - name: Access data
           items: 
             - name: Connect to Azure Storage
-              displayName: blob, get, fileshare, access, mount, download, data lake
+              displayName: blob, get, fileshare, access, mount, download, data lake, datastore
               href: how-to-access-data.md
             - name: Get data from a datastore
               displayName: data, data set, register, access data