initial writing

Author: Blackmist

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).
+
+| 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. |
+
+> [!TIP]
+> The {workspace-id}-code container is created lazily, so it doesn't exist until you've performed an action that needs it. For example, creating a compute instance.
+
+
 ## 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 +42,17 @@ 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:
+
+| Datastore name | Storage container name | Desccription |
+|---|---|---|
+| `workspaceblobstore` | `{workspace-id}-blobstore` | Stores data uploads, job code snapshots, and pipeline data cache. |
+| `workspaceworkingdirectory` | `{workspace-id}-code` | Stores data for notebooks, compute instances, and prompt flow. |
+| `workspacefilestore` | `{workspace-id}-file` | Alternative container for data upload. |
+| `workspaceartifactstore` | `{workspace-id}-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: