Merge pull request #213069 from Blackmist/sdk-links

prmerger-automator[bot] · web-flow · commit f8830aa34004 · 2022-09-29T18:45:24.000Z
sdk paths
diff --git a/articles/machine-learning/concept-customer-managed-keys.md b/articles/machine-learning/concept-customer-managed-keys.md
@@ -17,7 +17,7 @@ Azure Machine Learning is built on top of multiple Azure services. While the dat
 
 [!INCLUDE [machine-learning-customer-managed-keys.md](../../includes/machine-learning-customer-managed-keys.md)]
 
-In addition to customer-managed keys, Azure Machine Learning also provides a [hbi_workspace flag](/python/api/azureml-core/azureml.core.workspace%28class%29#create-name--auth-none--subscription-id-none--resource-group-none--location-none--create-resource-group-true--sku--basic---friendly-name-none--storage-account-none--key-vault-none--app-insights-none--container-registry-none--cmk-keyvault-none--resource-cmk-uri-none--hbi-workspace-false--default-cpu-compute-target-none--default-gpu-compute-target-none--exist-ok-false--show-output-true-). Enabling this flag reduces the amount of data Microsoft collects for diagnostic purposes and enables [extra encryption in Microsoft-managed environments](../security/fundamentals/encryption-atrest.md). This flag also enables the following behaviors:
+In addition to customer-managed keys, Azure Machine Learning also provides a [hbi_workspace flag](/python/api/azure-ai-ml/azure.ai.ml.entities.workspace). Enabling this flag reduces the amount of data Microsoft collects for diagnostic purposes and enables [extra encryption in Microsoft-managed environments](../security/fundamentals/encryption-atrest.md). This flag also enables the following behaviors:
 
 * Starts encrypting the local scratch disk in your Azure Machine Learning compute cluster, provided you haven’t created any previous clusters in that subscription. Else, you need to raise a support ticket to enable encryption of the scratch disk of your compute clusters.
 * Cleans up your local scratch disk between jobs.
diff --git a/articles/machine-learning/concept-data-encryption.md b/articles/machine-learning/concept-data-encryption.md
@@ -65,6 +65,8 @@ For an example of creating a workspace using an existing Azure Container Registr
 
 You may encrypt a deployed Azure Container Instance (ACI) resource using customer-managed keys. The customer-managed key used for ACI can be stored in the Azure Key Vault for your workspace. For information on generating a key, see [Encrypt data with a customer-managed key](../container-instances/container-instances-encrypt-data.md#generate-a-new-key).
 
+[!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
+
 To use the key when deploying a model to Azure Container Instance, create a new deployment configuration using `AciWebservice.deploy_configuration()`. Provide the key information using the following parameters:
 
 * `cmk_vault_base_url`: The URL of the key vault that contains the key.
diff --git a/articles/machine-learning/concept-environments.md b/articles/machine-learning/concept-environments.md
@@ -125,4 +125,4 @@ For more information on the base images, see the following links:
 ## Next steps
 
 * Learn how to [create and use environments](how-to-use-environments.md) in Azure Machine Learning.
-* See the Python SDK reference documentation for the [environment class](/python/api/azureml-core/azureml.core.environment%28class%29).
+* See the Python SDK reference documentation for the [environment class](/python/api/azure-ai-ml/azure.ai.ml.entities.environment).
diff --git a/articles/machine-learning/concept-model-management-and-deployment.md b/articles/machine-learning/concept-model-management-and-deployment.md
@@ -146,7 +146,6 @@ Microsoft Power BI supports using machine learning models for data analytics. Fo
 
 Machine Learning gives you the capability to track the end-to-end audit trail of all your machine learning assets by using metadata. For example:
 
-- Machine Learning [integrates with Git](concept-train-model-git-integration.md) to track information on which repository, branch, and commit your code came from.
 - [Machine Learning datasets](how-to-create-register-datasets.md) help you track, profile, and version data.
 - [Interpretability](how-to-machine-learning-interpretability.md) allows you to explain your models, meet regulatory compliance, and understand how models arrive at a result for specific input.
 - Machine Learning Job history stores a snapshot of the code, data, and computes used to train a model.
diff --git a/articles/machine-learning/how-to-access-azureml-behind-firewall.md b/articles/machine-learning/how-to-access-azureml-behind-firewall.md
@@ -82,7 +82,7 @@ These rule collections are described in more detail in [What are some Azure Fire
     > [!TIP]
     > * AzureContainerRegistry.region is only needed for custom Docker images. Including small modifications (such as additional packages) to base images provided by Microsoft.
     > * MicrosoftContainerRegistry.region is only needed if you plan on using the _default Docker images provided by Microsoft_, and _enabling user-managed dependencies_.
-    > * AzureKeyVault.region is only needed if your workspace was created with the [hbi_workspace](/python/api/azureml-core/azureml.core.workspace%28class%29#create-name--auth-none--subscription-id-none--resource-group-none--location-none--create-resource-group-true--sku--basic---friendly-name-none--storage-account-none--key-vault-none--app-insights-none--container-registry-none--cmk-keyvault-none--resource-cmk-uri-none--hbi-workspace-false--default-cpu-compute-target-none--default-gpu-compute-target-none--exist-ok-false--show-output-true-) flag enabled.
+    > * AzureKeyVault.region is only needed if your workspace was created with the [hbi_workspace](/python/api/azure-ai-ml/azure.ai.ml.entities.workspace) flag enabled.
     > * For entries that contain `region`, replace with the Azure region that you're using. For example, `AzureContainerRegistry.westus`.
 
 1. Add __Application rules__ for the following hosts:
@@ -305,7 +305,7 @@ The hosts in the following tables are owned by Microsoft, and provide services r
 **Azure Machine Learning compute instance and compute cluster hosts**
 
 > [!TIP]
-> * The host for __Azure Key Vault__ is only needed if your workspace was created with the [hbi_workspace](/python/api/azureml-core/azureml.core.workspace%28class%29#create-name--auth-none--subscription-id-none--resource-group-none--location-none--create-resource-group-true--sku--basic---friendly-name-none--storage-account-none--key-vault-none--app-insights-none--container-registry-none--cmk-keyvault-none--resource-cmk-uri-none--hbi-workspace-false--default-cpu-compute-target-none--default-gpu-compute-target-none--exist-ok-false--show-output-true-) flag enabled.
+> * The host for __Azure Key Vault__ is only needed if your workspace was created with the [hbi_workspace](/python/api/azure-ai-ml/azure.ai.ml.entities.workspace) flag enabled.
 > * Ports 8787 and 18881 for __compute instance__ are only needed when your Azure Machine workspace has a private endpoint.
 > * In the following table, replace `<storage>` with the name of the default storage account for your Azure Machine Learning workspace.
 > * Websocket communication must be allowed to the compute instance. If you block websocket traffic, Jupyter notebooks won't work correctly.
diff --git a/articles/machine-learning/how-to-access-terminal.md b/articles/machine-learning/how-to-access-terminal.md
@@ -61,8 +61,6 @@ Access all Git operations from the terminal. All Git files and folders will be s
 > [!NOTE]
 > Add your files and folders anywhere under the **~/cloudfiles/code/Users** folder so they will be visible in all your Jupyter environments.
 
-Learn more about [cloning Git repositories into your workspace file system](concept-train-model-git-integration.md#clone-git-repositories-into-your-workspace-file-system).
-
 ## Install packages
 
  Install packages from a terminal window. Install Python packages into the **Python 3.8 - AzureML** environment.  Install R packages into the **R** environment.
diff --git a/articles/machine-learning/how-to-configure-network-isolation-with-v2.md b/articles/machine-learning/how-to-configure-network-isolation-with-v2.md
@@ -19,7 +19,7 @@ In this article, you'll learn about network isolation changes with our new v2 AP
 
 ## Prerequisites
 
-* The [Azure Machine Learning Python SDK](/python/api/overview/azure/ml/install) or [Azure CLI extension for machine learning v1](reference-azure-machine-learning-cli.md).
+* The [Azure Machine Learning Python SDK v1](/python/api/overview/azure/ml/install) or [Azure CLI extension for machine learning v1](reference-azure-machine-learning-cli.md).
 
     > [!IMPORTANT]
     > The v1 extension (`azure-cli-ml`) version must be 1.41.0 or greater. Use the `az version` command to view version information.
diff --git a/articles/machine-learning/how-to-create-attach-compute-cluster.md b/articles/machine-learning/how-to-create-attach-compute-cluster.md
@@ -35,7 +35,7 @@ In this article, learn how to:
 
 * An Azure Machine Learning workspace. For more information, see [Create an Azure Machine Learning workspace](how-to-manage-workspace.md).
 
-* The [Azure CLI extension for Machine Learning service (v2)](reference-azure-machine-learning-cli.md), [Azure Machine Learning Python SDK](/python/api/overview/azure/ml/intro), or the [Azure Machine Learning Visual Studio Code extension](how-to-setup-vs-code.md).
+* The [Azure CLI extension for Machine Learning service (v2)](reference-azure-machine-learning-cli.md), [Azure Machine Learning Python SDK](/python/api/overview/azure/ai-ml-readme), or the [Azure Machine Learning Visual Studio Code extension](how-to-setup-vs-code.md).
 
 * If using the Python SDK, [set up your development environment with a workspace](how-to-configure-environment.md).  Once your environment is set up, attach to the workspace in your Python script:
 
@@ -89,7 +89,7 @@ To create a persistent Azure Machine Learning Compute resource in Python, specif
 
 [!notebook-python[](~/azureml-examples-v2samplesreorg/sdk/python/resources/compute/compute.ipynb?name=cluster_basic)]
 
-You can also configure several advanced properties when you create Azure Machine Learning Compute. The properties allow you to create a persistent cluster of fixed size, or within an existing Azure Virtual Network in your subscription.  See the [AmlCompute class](/python/api/azureml-core/azureml.core.compute.amlcompute.amlcompute) for details.
+You can also configure several advanced properties when you create Azure Machine Learning Compute. The properties allow you to create a persistent cluster of fixed size, or within an existing Azure Virtual Network in your subscription.  See the [AmlCompute class](/python/api/azure-ai-ml/azure.ai.ml.entities.amlcompute) for details.
 
 > [!WARNING]
 > When setting the `location` parameter, if it is a different region than your workspace or datastores you may see increased network latency and data transfer costs. The latency and costs can occur when creating the cluster, and when running jobs on it.
diff --git a/articles/machine-learning/how-to-manage-environments-in-studio.md b/articles/machine-learning/how-to-manage-environments-in-studio.md
@@ -14,7 +14,7 @@ ms.custom: devx-track-python
 
 # Manage software environments in Azure Machine Learning studio
 
-In this article, learn how to create and manage Azure Machine Learning [environments](/python/api/azureml-core/azureml.core.environment.environment) in the Azure Machine Learning studio. Use the environments to track and reproduce your projects' software dependencies as they evolve.
+In this article, learn how to create and manage Azure Machine Learning [environments](/python/api/azure-ai-ml/azure.ai.ml.entities.environment) in the Azure Machine Learning studio. Use the environments to track and reproduce your projects' software dependencies as they evolve.
 
 The examples in this article show how to:
 
