Merge pull request #178748 from nibaccam/access-id-preview

ML Data| remove preview for ID based data access

Author: PRMerger20

articles/machine-learning/concept-data.md

@@ -64,7 +64,7 @@ Supported cloud-based storage services in Azure that can be registered as datast
 + Azure Database for MySQL
 
 >[!TIP]
-> The generally available functionality for creating datastores requires credential-based authentication for accessing storage services, like a service principal or shared access signature (SAS) token. These credentials can be accessed by users who have *Reader* access to the workspace. <br><br>If this is a concern,  [create a datastore that uses identity-based data access to storage services (preview)](how-to-identity-based-data-access.md). This capability is an [experimental](/python/api/overview/azure/ml/#stable-vs-experimental) preview feature, and may change at any time.
+> The generally available functionality for creating datastores requires credential-based authentication for accessing storage services, like a service principal or shared access signature (SAS) token. These credentials can be accessed by users who have *Reader* access to the workspace. <br><br>If this is a concern,  [create a datastore that uses identity-based data access to storage services](how-to-identity-based-data-access.md).
 
 <a name="datasets"></a>
 ## Reference data in storage with datasets

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

@@ -27,7 +27,7 @@ To understand where datastores fit in Azure Machine Learning's overall data acce
 For a low code experience, see how to use the [Azure Machine Learning studio to create and register datastores](how-to-connect-data-ui.md#create-datastores).
 
 >[!TIP]
-> This article assumes you want to connect to your storage service with credential-based authentication credentials, like a service principal or a shared access signature (SAS) token. Keep in mind, if credentials are registered with datastores, all users with workspace *Reader* role are able to retrieve these credentials. [Learn more about workspace *Reader* role.](how-to-assign-roles.md#default-roles) <br><br>If this is a concern, learn how to [Connect to storage services with identity based access](how-to-identity-based-data-access.md). <br><br>This capability is an [experimental](/python/api/overview/azure/ml/#stable-vs-experimental) preview feature, and may change at any time. 
+> This article assumes you want to connect to your storage service with credential-based authentication credentials, like a service principal or a shared access signature (SAS) token. Keep in mind, if credentials are registered with datastores, all users with workspace *Reader* role are able to retrieve these credentials. [Learn more about workspace *Reader* role.](how-to-assign-roles.md#default-roles) <br><br>If this is a concern, learn how to [Connect to storage services with identity based access](how-to-identity-based-data-access.md).
 
 ## Prerequisites
 
@@ -90,7 +90,7 @@ We recommend creating a datastore for an [Azure Blob container](../storage/blobs
 To ensure you securely connect to your Azure storage service, Azure Machine Learning  requires that you have permission to access the corresponding data storage container. This access depends on the authentication credentials used to register the datastore. 
 
 > [!NOTE]
-> This guidance also applies to [datastores created with identity-based data access (preview)](how-to-identity-based-data-access.md). 
+> This guidance also applies to [datastores created with identity-based data access](how-to-identity-based-data-access.md). 
 
 ### Virtual network 
 

articles/machine-learning/how-to-create-register-datasets.md

@@ -97,8 +97,7 @@ If your workspace is in a virtual network, you must configure the dataset to ski
 For the data to be accessible by Azure Machine Learning, datasets must be created from paths in [Azure Machine Learning datastores](how-to-access-data.md) or web URLs. 
 
 > [!TIP] 
-> You can create datasets directly from storage urls with identity-based data access. Learn more at [Connect to storage with identity-based data access (preview)](how-to-identity-based-data-access.md)<br><br>
-This capability is an [experimental](/python/api/overview/azure/ml/#stable-vs-experimental) preview feature, and may change at any time. 
+> You can create datasets directly from storage urls with identity-based data access. Learn more at [Connect to storage with identity-based data access](how-to-identity-based-data-access.md).
 
 
 To create datasets from a datastore with the Python SDK: