Merge pull request #271329 from Blackmist/240793-default-datastores

initial writing

Author: prmerger-automator[bot]

articles/ai-studio/concepts/architecture.md

@@ -90,6 +90,20 @@ The role assignment for each AI project's service principal has a condition that
 
 For more information on Azure access-based control, see [What is Azure attribute-based access control](/azure/role-based-access-control/conditions-overview).
 
+## Containers in the storage account
+
+The default storage account for an AI hub has the following containers. These containers are created for each AI project, and the `{workspace-id}` prefix matches the unique ID for the AI project. The container is accessed by the AI project using a [connection](connections.md).
+
+> [!TIP]
+> To find the ID for your AI project, go to the AI project in the [Azure portal](https://portal.azure.com/). Expand **Settings** and then select **Properties**. The **Workspace ID** is displayed.
+
+| Container name | Connection name | Description |
+| --- | --- | --- |
+| {workspace-ID}-azureml | workspaceartifactstore | Storage for assets such as metrics, models, and components. |
+| {workspace-ID}-blobstore| workspaceblobstore | Storage for data upload, job code snapshots, and pipeline data cache. |
+| {workspace-ID}-code | NA | Storage for notebooks, compute instances, and prompt flow. |
+| {workspace-ID}-file | NA | Alternative container for data upload. |
+
 ## Encryption
 
 Azure AI Studio uses encryption to protect data at rest and in transit. By default, Microsoft-managed keys are used for encryption. However you can use your own encryption keys. For more information, see [Customer-managed keys](../../ai-services/encryption/cognitive-services-encryption-keys-portal.md?context=/azure/ai-studio/context/context).

articles/machine-learning/concept-data.md

@@ -42,6 +42,20 @@ Azure Data Lake Gen2| ✓ | ✓|
 
 See [Create datastores](how-to-datastore.md) for more information about datastores.
 
+### Default datastores
+
+Each Azure Machine Learning workspace has a default storage account (Azure storage account) that contains the following datastores:
+
+> [!TIP]
+> To find the ID for your workspace, go to the workspace in the [Azure portal](https://portal.azure.com/). Expand **Settings** and then select **Properties**. The **Workspace ID** is displayed.
+
+| Datastore name | Data storage type | Data storage name | Description |
+|---|---|---|---|
+| `workspaceblobstore` | Blob container | `azureml-blobstore-{workspace-id}` | Stores data uploads, job code snapshots, and pipeline data cache. |
+| `workspaceworkingdirectory` | File share | `code-{GUID}` | Stores data for notebooks, compute instances, and prompt flow. |
+| `workspacefilestore` | File share | `azureml-filestore-{workspace-id}` | Alternative container for data upload. |
+| `workspaceartifactstore` | Blob container | `azureml` | Storage for assets such as metrics, models, and components. |
+
 ## Data types
 
 A URI (storage location) can reference a file, a folder, or a data table. A machine learning job input and output definition requires one of the following three data types: