PM feedback

Author: nibaccam

articles/machine-learning/how-to-access-data.md

@@ -56,13 +56,15 @@ Datastores currently support storing connection information to the storage servi
 Azure&nbsp;SQL&nbsp;Database| SQL authentication <br>Service principal| ✓ | ✓ | ✓ |✓
 Azure&nbsp;PostgreSQL | SQL authentication| ✓ | ✓ | ✓ |✓
 Azure&nbsp;Database&nbsp;for&nbsp;MySQL | SQL authentication|  | ✓ | ✓ |✓
-Databricks&nbsp;File&nbsp;System| No authentication | | ✓`*` | ✓ `*`|✓`*`
+Databricks&nbsp;File&nbsp;System| No authentication | | ✓* | ✓ * |✓* 
 
-`*` only supported on local compute target scenarios
+*only supported on local compute target scenarios
 
 ### Storage guidance
 
-We recommend creating a datastore for an Azure blob container. Both standard and premium storage are available for blobs. Although premium storage is more expensive, its faster throughput speeds might improve the speed of your training runs, particularly if you train against a large dataset. For information about the cost of storage accounts, see the [Azure pricing calculator](https://azure.microsoft.com/pricing/calculator/?service=machine-learning-service).
+We recommend creating a datastore for an Azure blob container. As part of the workspace creation process a default blob datastore and file share datastore are created for you. 
+
+Both standard and premium storage are available for blobs. Although premium storage is more expensive, its faster throughput speeds might improve the speed of your training runs, particularly if you train against a large dataset. For information about the cost of storage accounts, see the [Azure pricing calculator](https://azure.microsoft.com/pricing/calculator/?service=machine-learning-service).
 
 <a name="access"></a>
 
@@ -129,7 +131,7 @@ file_datastore = Datastore.register_azure_file_share(workspace=ws,
 
 #### Azure Data Lake Storage Generation 2
 
-For an Azure Data Lake Storage Generation 2 (ADLS Gen 2) datastore, use [register_azure_data_lake_gen2()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.datastore.datastore?view=azure-ml-py#register-azure-data-lake-gen2-workspace--datastore-name--filesystem--account-name--tenant-id--client-id--client-secret--resource-url-none--authority-url-none--protocol-none--endpoint-none--overwrite-false-) to register a credential datastore connected to an Azure DataLake Gen 2 storage with service principal permissions. Learn more about [access control et up for ADLS Gen 2](https://docs.microsoft.com/azure/storage/blobs/data-lake-storage-access-control). 
+For an Azure Data Lake Storage Generation 2 (ADLS Gen 2) datastore, use [register_azure_data_lake_gen2()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.datastore.datastore?view=azure-ml-py#register-azure-data-lake-gen2-workspace--datastore-name--filesystem--account-name--tenant-id--client-id--client-secret--resource-url-none--authority-url-none--protocol-none--endpoint-none--overwrite-false-) to register a credential datastore connected to an Azure DataLake Gen 2 storage with service principal permissions. Learn more about [access control set up for ADLS Gen 2](https://docs.microsoft.com/azure/storage/blobs/data-lake-storage-access-control). 
 
 The following code creates and registers the `adlsgen2_datastore_name` datastore to the `ws` workspace. This datastore accesses the file system `test` on the `account_name` storage account, by using the provided service principal credentials.
 
@@ -144,14 +146,13 @@ tenant_id=os.getenv("ADLSGEN2_TENANT", "<my_tenant_id>") # tenant id of service
 client_id=os.getenv("ADLSGEN2_CLIENTID", "<my_client_id>") # client id of service principal
 client_secret=os.getenv("ADLSGEN2_CLIENT_SECRET", "<my_client_secret>") # the secret of service principal
 
-adlsgen2_datastore = Datastore.register_azure_data_lake_gen2(
-    workspace=ws,
-    datastore_name=adlsgen2_datastore_name,
-    account_name=account_name, # ADLS Gen2 account name
-    filesystem='test', # Name of ADLS Gen2 filesystem
-    tenant_id=tenant_id, # tenant id of service principal
-    client_id=client_id, # client id of service principal
-    client_secret=client_secret) # the secret of service principal
+adlsgen2_datastore = Datastore.register_azure_data_lake_gen2( workspace=ws,
+                                                              datastore_name=adlsgen2_datastore_name,
+                                                              account_name=account_name, # ADLS Gen2 account name
+                                                              filesystem='test', # ADLS Gen2 filesystem
+                                                              tenant_id=tenant_id, # tenant id of service principal
+                                                              client_id=client_id, # client id of service principal
+                                                              client_secret=client_secret) # the secret of service principal
 ```
 
 ### Azure Machine Learning studio