Standardize tab name

Author: sdgilley

articles/machine-learning/how-to-configure-network-isolation-with-v2.md

@@ -89,7 +89,7 @@ After the parameter has been implemented, the default value of the flag depends
 
 To update v1_legacy_mode, use the following steps:
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 To disable v1_legacy_mode, use [Workspace.update](/python/api/azureml-core/azureml.core.workspace(class)#update-friendly-name-none--description-none--tags-none--image-build-compute-none--service-managed-resources-settings-none--primary-user-assigned-identity-none--allow-public-access-when-behind-vnet-none-) and set `v1_legacy_mode=false`.
 

articles/machine-learning/how-to-create-attach-compute-cluster.md

@@ -84,7 +84,7 @@ The dedicated cores per region per VM family quota and total regional quota, whi
 
 The compute autoscales down to zero nodes when it isn't used.   Dedicated VMs are created to run your jobs as needed.
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 To create a persistent Azure Machine Learning Compute resource in Python, specify the **vm_size** and **max_nodes** properties. Azure Machine Learning then uses smart defaults for the other properties.
 
@@ -175,7 +175,7 @@ You may also choose to use [low-priority VMs](how-to-manage-optimize-cost.md#low
 
 Use any of these ways to specify a low-priority VM:
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 [!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
 
@@ -209,7 +209,7 @@ In the studio, choose **Low Priority** when you create a VM.
 
 [!INCLUDE [aml-clone-in-azure-notebook](../../includes/aml-managed-identity-intro.md)]
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 [!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
 

articles/machine-learning/how-to-create-manage-compute-instance.md

@@ -60,7 +60,7 @@ The dedicated cores per region per VM family quota and total regional quota, whi
 
 The following example demonstrates how to create a compute instance:
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 [!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
 
@@ -428,7 +428,7 @@ You can [create a schedule](#schedule-automatic-start-and-stop-preview) for the
 > [!TIP]
 > The compute instance has 120GB OS disk. If you run out of disk space, [use the terminal](how-to-access-terminal.md) to clear at least 1-2 GB before you stop or restart the compute instance. Please do not stop the compute instance by issuing sudo shutdown from the terminal. The temp disk size on compute instance depends on the VM size chosen and is mounted on /mnt.
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 [!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
 

articles/machine-learning/how-to-manage-workspace.md

@@ -37,7 +37,7 @@ As your needs change or requirements for automation increase you can also manage
 
 You can create a workspace [directly in Azure Machine Learning studio](./quickstart-create-resources.md#create-the-workspace), with limited options available. Or use one of the methods below for more control of options.
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 [!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
 
@@ -168,7 +168,7 @@ If you have problems in accessing your subscription, see [Set up authentication
 > For more information on using a private endpoint and virtual network with your workspace, see [Network isolation and privacy](how-to-network-security-overview.md).
 
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 The Azure Machine Learning Python SDK provides the [PrivateEndpointConfig](/python/api/azureml-core/azureml.core.privateendpointconfig) class, which can be used with [Workspace.create()](/python/api/azureml-core/azureml.core.workspace.workspace#create-name--auth-none--subscription-id-none--resource-group-none--location-none--create-resource-group-true--sku--basic---tags-none--friendly-name-none--storage-account-none--key-vault-none--app-insights-none--container-registry-none--adb-workspace-none--cmk-keyvault-none--resource-cmk-uri-none--hbi-workspace-false--default-cpu-compute-target-none--default-gpu-compute-target-none--private-endpoint-config-none--private-endpoint-auto-approval-true--exist-ok-false--show-output-true-) to create a workspace with a private endpoint. This class requires an existing virtual network.
 
@@ -212,7 +212,7 @@ Use the following steps to provide your own key:
 > * Create and configure an Azure Key Vault
 > * Generate a key
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 Use `cmk_keyvault` and `resource_cmk_uri` to specify the customer managed key.
 
@@ -245,7 +245,7 @@ from azureml.core import Workspace
 
 If you will be creating a [compute instance](quickstart-create-resources.md), skip this step.  The compute instance has already created a copy of this file for you.
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 If you plan to use code on your local environment that references this workspace (`ws`), write the configuration file:
 
@@ -303,7 +303,7 @@ If you have problems in accessing your subscription, see [Set up authentication
 
 See a list of all the workspaces you can use.
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 [!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
 
@@ -342,7 +342,7 @@ When you no longer need a workspace, delete it.
 
 If you accidentally deleted your workspace, you may still be able to retrieve your notebooks. For details, see [Failover for business continuity and disaster recovery](./how-to-high-availability-machine-learning.md#workspace-deletion).
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 [!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
 

articles/machine-learning/how-to-responsible-ai-dashboard-sdk-cli.md

@@ -149,7 +149,7 @@ The constructor component has a single output named `rai_insights_dashboard`. Th
       categorical_column_names: '["location", "style", "job title", "OS", "Employer", "IDE", "Programming language"]' 
 ```
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 First load the component:
 
@@ -252,7 +252,7 @@ This component has a single output port, which can be connected to one of the `i
       treatment_features: `["Number of GitHub repos contributed to", "YOE"]' 
 ```
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 ```python
 #First load the component: 
@@ -299,7 +299,7 @@ This component has a single output port, which can be connected to one of the `i
 ```
 
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 ```python
 #First load the component: 
@@ -340,7 +340,7 @@ This component has a single output port, which can be connected to one of the `i
       filter_features: `["style", "Employer"]' 
 ```
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 ```python
 #First load the component: 
@@ -375,7 +375,7 @@ This component has a single output port, which can be connected to one of the `i
 ```
 
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 ```python
 #First load the component: 
@@ -417,7 +417,7 @@ There are two output ports:
 ```
 
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 ```python
 #First load the component: 
@@ -454,7 +454,7 @@ This component produces information about a registered model, which can be consu
       model_id: my_model_name:12 
 ```
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 ```python
 #First load the component: 
@@ -482,7 +482,7 @@ This component converts the tabular dataset named in its sole input parameter in
 ```
 
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 ```python
 #First load the component: 

articles/machine-learning/how-to-schedule-pipeline-job.md

@@ -34,7 +34,7 @@ In this article, you'll learn how to programmatically schedule a pipeline to run
 
 - Create an Azure Machine Learning workspace if you don't have one. For workspace creation, see [Install, set up, and use the CLI (v2)](how-to-configure-cli.md).
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 - Create an Azure Machine Learning workspace if you don't have one.
 - The [Azure Machine Learning SDK v2 for Python](/python/api/overview/azure/ml/installv2).
@@ -63,7 +63,7 @@ You can schedule a pipeline job yaml in local or an existing pipeline job in wor
 
 List continues below.
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 [!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
 
@@ -109,7 +109,7 @@ The `trigger` section defines the schedule details and contains following proper
 
 List continues below. 
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 [!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
 
@@ -160,7 +160,7 @@ When defining a schedule using an existing job, you can change the runtime setti
 
 :::code language="yaml" source="~/azureml-examples-main/cli/schedules/cron-with-settings-job-schedule.yml":::
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 [!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
 
@@ -198,7 +198,7 @@ After you create the schedule yaml, you can use the following command to create
 
 :::code language="azurecli" source="~/azureml-examples-main/cli/schedules/schedule.sh" ID="create_schedule":::
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 [!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
 
@@ -214,7 +214,7 @@ After you create the schedule yaml, you can use the following command to create
 
 :::code language="azurecli" source="~/azureml-examples-main/cli/schedules/schedule.sh" ID="show_schedule":::
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 [!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
 
@@ -230,7 +230,7 @@ After you create the schedule yaml, you can use the following command to create
 
 :::code language="azurecli" source="~/azureml-examples-main/cli/schedules/schedule.sh" ID="list_schedule":::
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 [!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
 
@@ -246,7 +246,7 @@ After you create the schedule yaml, you can use the following command to create
 
 :::code language="azurecli" source="~/azureml-examples-main/cli/schedules/schedule.sh" ID="update_schedule":::
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 [!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
 
@@ -262,7 +262,7 @@ After you create the schedule yaml, you can use the following command to create
 
 :::code language="azurecli" source="~/azureml-examples-main/cli/schedules/schedule.sh" ID="disable_schedule":::
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 [!notebook-python[] (~/azureml-examples-main/sdk/schedules/job-schedule.ipynb?name=disable_schedule)]
 
@@ -276,7 +276,7 @@ After you create the schedule yaml, you can use the following command to create
 
 :::code language="azurecli" source="~/azureml-examples-main/cli/schedules/schedule.sh" ID="enable_schedule":::
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 [!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
 
@@ -312,7 +312,7 @@ You can also apply [Azure CLI JMESPath query](/cli/azure/query-azure-cli) to que
 
 :::code language="azurecli" source="~/azureml-examples-main/cli/schedules/schedule.sh" ID="delete_schedule":::  
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 [!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
 

articles/machine-learning/how-to-use-managed-identities.md

@@ -110,7 +110,7 @@ If you don't bring your own ACR, Azure Machine Learning service will create one
 
 To access the workspace ACR, create machine learning compute cluster with system-assigned managed identity enabled. You can enable the identity from  Azure portal or Studio when creating compute, or from Azure CLI using the below. For more information, see [using managed identity with compute clusters](how-to-create-attach-compute-cluster.md#set-up-managed-identity).
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 When creating a compute cluster with the [AmlComputeProvisioningConfiguration](/python/api/azureml-core/azureml.core.compute.amlcompute.amlcomputeprovisioningconfiguration), use the `identity_type` parameter to set the managed identity type.
 

articles/machine-learning/v1/how-to-configure-private-link.md

@@ -65,7 +65,7 @@ Use one of the following methods to create a workspace with a private endpoint.
 > [!TIP]
 > If you'd like to create a workspace, private endpoint, and virtual network at the same time, see [Use an Azure Resource Manager template to create a workspace for Azure Machine Learning](../how-to-create-workspace-template.md).
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 The Azure Machine Learning Python SDK provides the [PrivateEndpointConfig](/python/api/azureml-core/azureml.core.privateendpointconfig) class, which can be used with [Workspace.create()](/python/api/azureml-core/azureml.core.workspace.workspace#create-name--auth-none--subscription-id-none--resource-group-none--location-none--create-resource-group-true--sku--basic---tags-none--friendly-name-none--storage-account-none--key-vault-none--app-insights-none--container-registry-none--adb-workspace-none--cmk-keyvault-none--resource-cmk-uri-none--hbi-workspace-false--default-cpu-compute-target-none--default-gpu-compute-target-none--private-endpoint-config-none--private-endpoint-auto-approval-true--exist-ok-false--show-output-true-) to create a workspace with a private endpoint. This class requires an existing virtual network.
 
@@ -156,7 +156,7 @@ You can remove one or all private endpoints for a workspace. Removing a private
 
 To remove a private endpoint, use the following information:
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 To remove a private endpoint, use [Workspace.delete_private_endpoint_connection](/python/api/azureml-core/azureml.core.workspace(class)#delete-private-endpoint-connection-private-endpoint-connection-name-). The following example demonstrates how to remove a private endpoint:
 
@@ -201,7 +201,7 @@ To enable public access, use the following steps:
 >
 > Microsoft recommends using `public_network_access` to enable or disable public access to a workspace.
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 To enable public access, use [Workspace.update](/python/api/azureml-core/azureml.core.workspace(class)#update-friendly-name-none--description-none--tags-none--image-build-compute-none--service-managed-resources-settings-none--primary-user-assigned-identity-none--allow-public-access-when-behind-vnet-none-) and set `allow_public_access_when_behind_vnet=True`.
 

articles/machine-learning/v1/how-to-consume-web-service.md

@@ -45,7 +45,7 @@ The [azureml.core.Webservice](/python/api/azureml-core/azureml.core.webservice%2
 
 There are several ways to retrieve this information for deployed web services:
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 [!INCLUDE [sdk v1](../../../includes/machine-learning-sdk-v1.md)]
 

articles/machine-learning/v1/how-to-create-attach-kubernetes.md

@@ -162,7 +162,7 @@ Creating or attaching an AKS cluster is a one time process for your workspace. Y
 
 The following example demonstrates how to create a new AKS cluster using the SDK and CLI:
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 [!INCLUDE [sdk v1](../../../includes/machine-learning-sdk-v1.md)]
 
@@ -238,7 +238,7 @@ For more information on creating an AKS cluster using the Azure CLI or portal, s
 
 The following example demonstrates how to attach an existing AKS cluster to your workspace:
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 [!INCLUDE [sdk v1](../../../includes/machine-learning-sdk-v1.md)]
 
@@ -415,7 +415,7 @@ To detach a cluster from your workspace, use one of the following methods:
 > [!WARNING]
 > Using the Azure Machine Learning studio, SDK, or the Azure CLI extension for machine learning to detach an AKS cluster **does not delete the AKS cluster**. To delete the cluster, see [Use the Azure CLI with AKS](../../aks/learn/quick-kubernetes-deploy-cli.md#delete-the-cluster).
 
-# [Python](#tab/python)
+# [Python SDK](#tab/python)
 
 [!INCLUDE [sdk v1](../../../includes/machine-learning-sdk-v1.md)]