links

Author: lgayhardt

articles/machine-learning/.openpublishing.redirection.machine-learning.json

@@ -3757,8 +3757,8 @@
     },
     {
         "source_path_from_root": "/articles/machine-learning/overview-what-happened-to-workbench.md",
-        "redirect_url": "/azure/machine-learning/v1/overview-what-happened-to-workbench",
-        "redirect_document_id": true
+        "redirect_url": "/azure/machine-learning",
+        "redirect_document_id": false
     }
 ]
 }

articles/machine-learning/v1/how-to-change-storage-access-key.md

@@ -21,15 +21,15 @@ Learn how to change the access keys for Azure Storage accounts used by Azure Mac
 For security purposes, you may need to change the access keys for an Azure Storage account. When you regenerate the access key, Azure Machine Learning must be updated to use the new key. Azure Machine Learning may be using the storage account for both model storage and as a datastore.
 
 > [!IMPORTANT]
-> Credentials registered with datastores are saved in your Azure Key Vault associated with the workspace. If you have [soft-delete](../key-vault/general/soft-delete-overview.md) enabled for your Key Vault, this article provides instructions for updating credentials. If you unregister the datastore and try to re-register it under the same name, this action will fail. See [Turn on Soft Delete for an existing key vault](../key-vault/general/soft-delete-change.md#turn-on-soft-delete-for-an-existing-key-vault) for how to enable soft delete in this scenario.
+> Credentials registered with datastores are saved in your Azure Key Vault associated with the workspace. If you have [soft-delete](../../key-vault/general/soft-delete-overview.md) enabled for your Key Vault, this article provides instructions for updating credentials. If you unregister the datastore and try to re-register it under the same name, this action will fail. See [Turn on Soft Delete for an existing key vault](../../key-vault/general/soft-delete-change.md#turn-on-soft-delete-for-an-existing-key-vault) for how to enable soft delete in this scenario.
 
 ## Prerequisites
 
-* An Azure Machine Learning workspace. For more information, see the [Create workspace resources](quickstart-create-resources.md) article.
+* An Azure Machine Learning workspace. For more information, see the [Create workspace resources](../quickstart-create-resources.md) article.
 
 * The [Azure Machine Learning SDK](/python/api/overview/azure/ml/install).
 
-* The [Azure Machine Learning CLI extension v1](v1/reference-azure-machine-learning-cli.md).
+* The [Azure Machine Learning CLI extension v1](reference-azure-machine-learning-cli.md).
 
 > [!NOTE]
 > The code snippets in this document were tested with version 1.0.83 of the Python SDK.
@@ -82,7 +82,7 @@ To update Azure Machine Learning to use the new key, use the following steps:
 > [!IMPORTANT]
 > Perform all steps, updating both the workspace using the CLI, and datastores using Python. Updating only one or the other may cause errors until both are updated.
 
-1. Regenerate the key. For information on regenerating an access key, see [Manage storage account access keys](../storage/common/storage-account-keys-manage.md). Save the new key.
+1. Regenerate the key. For information on regenerating an access key, see [Manage storage account access keys](../../storage/common/storage-account-keys-manage.md). Save the new key.
 
 1. The Azure Machine Learning workspace will automatically synchronize the new key and begin using it after an hour. To force the workspace to synch to the new key immediately, use the following steps:
 
@@ -92,7 +92,7 @@ To update Azure Machine Learning to use the new key, use the following steps:
         az login
         ```
 
-        [!INCLUDE [select-subscription](../../includes/machine-learning-cli-subscription.md)]
+        [!INCLUDE [select-subscription](../../../includes/machine-learning-cli-subscription.md)]
 
     1. To update the workspace to use the new key, use the following command. Replace `myworkspace` with your Azure Machine Learning workspace name, and replace `myresourcegroup` with the name of the Azure resource group that contains the workspace.
 

articles/machine-learning/v1/how-to-high-availability-machine-learning.md

@@ -72,7 +72,7 @@ The rest of this article describes the actions you need to take to make each of
 A multi-regional deployment relies on creation of Azure Machine Learning and other resources (infrastructure) in two Azure regions. If a regional outage occurs, you can switch to the other region. When planning on where to deploy your resources, consider:
 
 * __Regional availability__: Use regions that are close to your users. To check regional availability for Azure Machine Learning, see [Azure products by region](https://azure.microsoft.com/global-infrastructure/services/).
-* __Azure paired regions__: Paired regions coordinate platform updates and prioritize recovery efforts where needed. For more information, see [Azure paired regions](../availability-zones/cross-region-replication-azure.md).
+* __Azure paired regions__: Paired regions coordinate platform updates and prioritize recovery efforts where needed. For more information, see [Azure paired regions](../../availability-zones/cross-region-replication-azure.md).
 * __Service availability__: Decide whether the resources used by your solution should be hot/hot, hot/warm, or hot/cold.
 
     * __Hot/hot__: Both regions are active at the same time, with one region ready to begin use immediately.
@@ -88,10 +88,10 @@ Azure Machine Learning builds on top of other services. Some services can be con
 | ----- | ----- | ----- |
 | Machine Learning workspace | You | Create a workspace in the selected regions. |
 | Machine Learning compute | You | Create the compute resources in the selected regions. For compute resources that can dynamically scale, make sure that both regions provide sufficient compute quota for your needs. |
-| Key Vault | Microsoft | Use the same Key Vault instance with the Azure Machine Learning workspace and resources in both regions. Key Vault automatically fails over to a secondary region. For more information, see [Azure Key Vault availability and redundancy](../key-vault/general/disaster-recovery-guidance.md).|
-| Container Registry | Microsoft | Configure the Container Registry instance to geo-replicate registries to the paired region for Azure Machine Learning. Use the same instance for both workspace instances. For more information, see [Geo-replication in Azure Container Registry](../container-registry/container-registry-geo-replication.md). |
-| Storage Account | You | Azure Machine Learning does not support __default storage-account__ failover using geo-redundant storage (GRS), geo-zone-redundant storage (GZRS), read-access geo-redundant storage (RA-GRS), or read-access geo-zone-redundant storage (RA-GZRS). Create a separate storage account for the default storage of each workspace. </br>Create separate storage accounts or services for other data storage. For more information, see [Azure Storage redundancy](../storage/common/storage-redundancy.md). |
-| Application Insights | You | Create Application Insights for the workspace in both regions. To adjust the data-retention period and details, see [Data collection, retention, and storage in Application Insights](../azure-monitor/app/data-retention-privacy.md#how-long-is-the-data-kept). |
+| Key Vault | Microsoft | Use the same Key Vault instance with the Azure Machine Learning workspace and resources in both regions. Key Vault automatically fails over to a secondary region. For more information, see [Azure Key Vault availability and redundancy](../../key-vault/general/disaster-recovery-guidance.md).|
+| Container Registry | Microsoft | Configure the Container Registry instance to geo-replicate registries to the paired region for Azure Machine Learning. Use the same instance for both workspace instances. For more information, see [Geo-replication in Azure Container Registry](../../container-registry/container-registry-geo-replication.md). |
+| Storage Account | You | Azure Machine Learning does not support __default storage-account__ failover using geo-redundant storage (GRS), geo-zone-redundant storage (GZRS), read-access geo-redundant storage (RA-GRS), or read-access geo-zone-redundant storage (RA-GZRS). Create a separate storage account for the default storage of each workspace. </br>Create separate storage accounts or services for other data storage. For more information, see [Azure Storage redundancy](../../storage/common/storage-redundancy.md). |
+| Application Insights | You | Create Application Insights for the workspace in both regions. To adjust the data-retention period and details, see [Data collection, retention, and storage in Application Insights](../../azure-monitor/app/data-retention-privacy.md#how-long-is-the-data-kept). |
 
 To enable fast recovery and restart in the secondary region, we recommend the following development practices:
 
@@ -109,20 +109,20 @@ __Compute resources__
 
 * **Azure Kubernetes Service**: See [Best practices for business continuity and disaster recovery in Azure Kubernetes Service (AKS)](../aks/operator-best-practices-multi-region.md) and [Create an Azure Kubernetes Service (AKS) cluster that uses availability zones](../aks/availability-zones.md). If the AKS cluster was created by using the Azure Machine Learning Studio, SDK, or CLI, cross-region high availability is not supported.
 * **Azure Databricks**: See [Regional disaster recovery for Azure Databricks clusters](/azure/databricks/scenarios/howto-regional-disaster-recovery).
-* **Container Instances**: An orchestrator is responsible for failover. See [Azure Container Instances and container orchestrators](../container-instances/container-instances-orchestrator-relationship.md).
-* **HDInsight**: See [High availability services supported by Azure HDInsight](../hdinsight/hdinsight-high-availability-components.md).
+* **Container Instances**: An orchestrator is responsible for failover. See [Azure Container Instances and container orchestrators](../../container-instances/container-instances-orchestrator-relationship.md).
+* **HDInsight**: See [High availability services supported by Azure HDInsight](../../hdinsight/hdinsight-high-availability-components.md).
 
 __Data services__
 
-* **Azure Blob container / Azure Files / Data Lake Storage Gen2**: See [Azure Storage redundancy](../storage/common/storage-redundancy.md).
-* **Data Lake Storage Gen1**: See [High availability and disaster recovery guidance for Data Lake Storage Gen1](../data-lake-store/data-lake-store-disaster-recovery-guidance.md).
+* **Azure Blob container / Azure Files / Data Lake Storage Gen2**: See [Azure Storage redundancy](../../storage/common/storage-redundancy.md).
+* **Data Lake Storage Gen1**: See [High availability and disaster recovery guidance for Data Lake Storage Gen1](../../data-lake-store/data-lake-store-disaster-recovery-guidance.md).
 * **SQL Database**: See [High availability for Azure SQL Database and SQL Managed Instance](/azure/azure-sql/database/high-availability-sla).
-* **Azure Database for PostgreSQL**: See [High availability concepts in Azure Database for PostgreSQL - Single Server](../postgresql/concepts-high-availability.md).
-* **Azure Database for MySQL**: See [Understand business continuity in Azure Database for MySQL](../mysql/concepts-business-continuity.md).
+* **Azure Database for PostgreSQL**: See [High availability concepts in Azure Database for PostgreSQL - Single Server](../../postgresql/concepts-high-availability.md).
+* **Azure Database for MySQL**: See [Understand business continuity in Azure Database for MySQL](../../mysql/concepts-business-continuity.md).
 * **Azure Databricks File System**: See [Regional disaster recovery for Azure Databricks clusters](/azure/databricks/scenarios/howto-regional-disaster-recovery).
 
 > [!TIP]
-> If you provide your own customer-managed key to deploy an Azure Machine Learning workspace, Azure Cosmos DB is also provisioned within your subscription. In that case, you're responsible for configuring its high-availability settings. See [High availability with Azure Cosmos DB](../cosmos-db/high-availability.md).
+> If you provide your own customer-managed key to deploy an Azure Machine Learning workspace, Azure Cosmos DB is also provisioned within your subscription. In that case, you're responsible for configuring its high-availability settings. See [High availability with Azure Cosmos DB](../../cosmos-db/high-availability.md).
 
 ## Design for high availability
 
@@ -153,7 +153,7 @@ Jobs in Azure Machine Learning are defined by a job specification. This specific
 
 * Manage configurations as code.
 
-    * Avoid hardcoded references to the workspace. Instead, configure a reference to the workspace instance using a [config file](how-to-configure-environment.md#local-and-dsvm-only-create-a-workspace-configuration-file) and use [Workspace.from_config()](/python/api/azureml-core/azureml.core.workspace.workspace#remarks) to initialize the workspace. To automate the process, use the [Azure CLI extension for machine learning](v1/reference-azure-machine-learning-cli.md) command [az ml folder attach](/cli/azure/ml(v1)/folder#az-ml(v1)-folder-attach).
+    * Avoid hardcoded references to the workspace. Instead, configure a reference to the workspace instance using a [config file](../how-to-configure-environment.md#local-and-dsvm-only-create-a-workspace-configuration-file) and use [Workspace.from_config()](/python/api/azureml-core/azureml.core.workspace.workspace#remarks) to initialize the workspace. To automate the process, use the [Azure CLI extension for machine learning](v1/reference-azure-machine-learning-cli.md) command [az ml folder attach](/cli/azure/ml(v1)/folder#az-ml(v1)-folder-attach).
     * Use job submission helpers such as [ScriptRunConfig](/python/api/azureml-core/azureml.core.scriptrunconfig) and [Pipeline](/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline(class)).
     * Use [Environments.save_to_directory()](/python/api/azureml-core/azureml.core.environment(class)#save-to-directory-path--overwrite-false-) to save your environment definitions.
     * Use a Dockerfile if you use custom Docker images.
@@ -164,7 +164,7 @@ Jobs in Azure Machine Learning are defined by a job specification. This specific
 
 ### Continue work in the failover workspace
 
-When your primary workspace becomes unavailable, you can switch over the secondary workspace to continue experimentation and development. Azure Machine Learning does not automatically submit jobs to the secondary workspace if there is an outage. Update your code configuration to point to the new workspace resource. We recommend to avoiding hardcoding workspace references. Instead, use a [workspace config file](how-to-configure-environment.md#local-and-dsvm-only-create-a-workspace-configuration-file) to minimize manual user steps when changing workspaces. Make sure to also update any automation, such as continuous integration and deployment pipelines to the new workspace.
+When your primary workspace becomes unavailable, you can switch over the secondary workspace to continue experimentation and development. Azure Machine Learning does not automatically submit jobs to the secondary workspace if there is an outage. Update your code configuration to point to the new workspace resource. We recommend to avoiding hardcoding workspace references. Instead, use a [workspace config file](../how-to-configure-environment.md#local-and-dsvm-only-create-a-workspace-configuration-file) to minimize manual user steps when changing workspaces. Make sure to also update any automation, such as continuous integration and deployment pipelines to the new workspace.
 
 Azure Machine Learning cannot sync or recover artifacts or metadata between workspace instances. Dependent on your application deployment strategy, you might have to move artifacts or recreate experimentation inputs such as dataset objects in the failover workspace in order to continue job submission. In case you have configured your primary workspace and secondary workspace resources to share associated resources with geo-replication enabled, some objects might be directly available to the failover workspace. For example, if both workspaces share the same docker images, configured datastores, and Azure Key Vault resources. The following diagram shows a configuration where two workspaces share the same images (1), datastores (2), and Key Vault (3).
 
@@ -187,7 +187,7 @@ The following artifacts can be exported and imported between workspaces by using
 
 > [!TIP]
 > * __Registered datasets__ cannot be downloaded or moved. This includes datasets generated by Azure Machine Learning, such as intermediate pipeline datasets. However datasets that refer to a shared file location that both workspaces can access, or where the underlying data storage is replicated, can be registered on both workspaces. Use the [az ml dataset register](/cli/azure/ml(v1)/dataset#ml-az-ml-dataset-register) to register a dataset.
-> * __Job outputs__ are stored in the default storage account associated with a workspace. While job outputs might become inaccessible from the studio UI in the case of a service outage, you can directly access the data through the storage account. For more information on working with data stored in blobs, see [Create, download, and list blobs with Azure CLI](../storage/blobs/storage-quickstart-blobs-cli.md).
+> * __Job outputs__ are stored in the default storage account associated with a workspace. While job outputs might become inaccessible from the studio UI in the case of a service outage, you can directly access the data through the storage account. For more information on working with data stored in blobs, see [Create, download, and list blobs with Azure CLI](../../storage/blobs/storage-quickstart-blobs-cli.md).
 
 ## Recovery options
 
@@ -202,4 +202,4 @@ If you accidentally deleted your workspace it is currently not possible to recov
 
 ## Next steps
 
-To learn about repeatable infrastructure deployments with Azure Machine Learning, use an [Azure Resource Manager template](./tutorial-create-secure-workspace-template.md).
+To learn about repeatable infrastructure deployments with Azure Machine Learning, use an [Azure Resource Manager template](../tutorial-create-secure-workspace-template.md).