Merge pull request #212637 from lgayhardt/sept2022movev1

 RAI automl and private python package move to v1 toc

Author: Maggiemouse1

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

@@ -3444,6 +3444,16 @@
         "source_path_from_root": "/articles/machine-learning/v1/how-to-differential-privacy.md",
         "redirect_url": "/azure/machine-learning/concept-responsible-ml#privacy-and-security",
         "redirect_document_id": false
+    },
+    {
+        "source_path_from_root": "/articles/machine-learning/how-to-machine-learning-interpretability-automl.md",
+        "redirect_url": "/azure/machine-learning/v1/how-to-machine-learning-interpretability-automl",
+        "redirect_document_id": true
+    },
+    {
+        "source_path_from_root": "/articles/machine-learning/how-to-use-private-python-packages.md",
+        "redirect_url": "/azure/machine-learning/v1/how-to-use-private-python-packages",
+        "redirect_document_id": true
     }
 ]
 }

articles/machine-learning/toc.yml

@@ -376,9 +376,6 @@
         - name: Set up software environments
           displayName: pip, Conda, anaconda
           href: how-to-use-environments.md
-        - name: Use private Python packages
-          displayName: pip, Conda, anaconda
-          href: how-to-use-private-python-packages.md
         - name: Set input & output directories
           displayName: large data, write, experiment files, size limit
           href: how-to-save-write-experiment-files.md
@@ -529,9 +526,6 @@
             - name: Inference image models with ONNX model
               displayName: automl, image, image model, computer vision
               href: how-to-inference-onnx-automl-image-models.md
-            - name: Explain automated ML models
-              displayName: SDK, interpret, interpret, explain, explainability, interpretability, automated ML, automl, auto ml
-              href: how-to-machine-learning-interpretability-automl.md
             - name: Troubleshoot automated ML
               href: how-to-troubleshoot-auto-ml.md
     - name: Deploy models

articles/machine-learning/how-to-machine-learning-interpretability-automl.md renamed to articles/machine-learning/v1/how-to-machine-learning-interpretability-automl.md

@@ -14,7 +14,7 @@ ms.date: 10/21/2021
 
 # Interpretability: Model explainability in automated ML (preview)
 
-[!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
+[!INCLUDE [sdk v1](../../../includes/machine-learning-sdk-v1.md)]
 
 In this article, you learn how to get explanations for automated machine learning (automated ML) models in Azure Machine Learning using the Python SDK. Automated ML helps you understand feature importance of the models that are generated. 
 
@@ -30,7 +30,7 @@ In this article, you learn how to:
 ## Prerequisites
 
 - Interpretability features. Run `pip install azureml-interpret` to get the necessary package.
-- Knowledge of building automated ML experiments. For more information on how to use the Azure Machine Learning SDK, complete this [object detection model tutorial](tutorial-auto-train-image-models.md) or see how to [configure automated ML experiments](how-to-configure-auto-train.md).
+- Knowledge of building automated ML experiments. For more information on how to use the Azure Machine Learning SDK, complete this [object detection model tutorial](../tutorial-auto-train-image-models.md) or see how to [configure automated ML experiments](../how-to-configure-auto-train.md).
 
 ## Interpretability during training for the best model
 
@@ -315,8 +315,8 @@ You can visualize the feature importance chart in your workspace in [Azure Machi
 
 [![Machine Learning Interpretability Architecture](./media/how-to-machine-learning-interpretability-automl/automl-explanation.png)](./media/how-to-machine-learning-interpretability-automl/automl-explanation.png#lightbox)
 
-For more information on the explanation dashboard visualizations and specific plots, please refer to the [how-to doc on interpretability](how-to-machine-learning-interpretability-aml.md).
+For more information on the explanation dashboard visualizations and specific plots, please refer to the [how-to doc on interpretability](../how-to-machine-learning-interpretability-aml.md).
 
 ## Next steps
 
-For more information about how you can enable model explanations and feature importance in areas other than automated ML, see [more techniques for model interpretability](how-to-machine-learning-interpretability.md).
+For more information about how you can enable model explanations and feature importance in areas other than automated ML, see [more techniques for model interpretability](../how-to-machine-learning-interpretability.md).

articles/machine-learning/how-to-use-private-python-packages.md renamed to articles/machine-learning/v1/how-to-use-private-python-packages.md

@@ -15,7 +15,7 @@ ms.custom: sdkv1, event-tier1-build-2022
 
 # Use private Python packages with Azure Machine Learning
 
-[!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
+[!INCLUDE [sdk v1](../../../includes/machine-learning-sdk-v1.md)]
 
 In this article, learn how to use private Python packages securely within Azure Machine Learning. Use cases for private Python packages include:
 
@@ -29,7 +29,7 @@ The private packages are used through [Environment](/python/api/azureml-core/azu
 ## Prerequisites
 
  * The [Azure Machine Learning SDK for Python](/python/api/overview/azure/ml/install)
- * An [Azure Machine Learning workspace](quickstart-create-resources.md)
+ * An [Azure Machine Learning workspace](../quickstart-create-resources.md)
 
 ## Use small number of packages for development and testing
 
@@ -89,7 +89,7 @@ The environment is now ready to be used in training runs or web service endpoint
 
 You can consume packages from an Azure storage account within your organization's firewall. The storage account can hold a curated set of packages or an internal mirror of publicly available packages.
 
-To set up such private storage, see [Secure an Azure Machine Learning workspace and associated resources](how-to-secure-workspace-vnet.md#secure-azure-storage-accounts). You must also [place the Azure Container Registry (ACR) behind the VNet](how-to-secure-workspace-vnet.md#enable-azure-container-registry-acr).
+To set up such private storage, see [Secure an Azure Machine Learning workspace and associated resources](../how-to-secure-workspace-vnet.md#secure-azure-storage-accounts). You must also [place the Azure Container Registry (ACR) behind the VNet](../how-to-secure-workspace-vnet.md#enable-azure-container-registry-acr).
 
 > [!IMPORTANT]
 > You must complete this step to be able to train or deploy models using the private package repository.
@@ -98,4 +98,4 @@ After completing these configurations, you can reference the packages in the Azu
 
 ## Next steps
 
- * Learn more about [enterprise security in Azure Machine Learning](concept-enterprise-security.md)
+ * Learn more about [enterprise security in Azure Machine Learning](../concept-enterprise-security.md)

articles/machine-learning/media/how-to-machine-learning-interpretability-automl/automl-explanation.png renamed to articles/machine-learning/v1/media/how-to-machine-learning-interpretability-automl/automl-explanation.png

@@ -118,6 +118,9 @@
     href: how-to-manage-workspace-cli.md
   - name: Set up software environments CLI (v1)
     href: how-to-use-environments.md
+  - name: Use private Python packages
+    displayName: pip, Conda, anaconda
+    href: how-to-use-private-python-packages.md
   - name: Create & manage compute resources
   - name: Workspace Diagnostics
     href: how-to-workspace-diagnostic-api.md
@@ -265,6 +268,9 @@
       href: how-to-prepare-datasets-for-automl-images-v1.md
     - name: Reinforcement learning
       href: how-to-use-reinforcement-learning.md
+    - name: Explain automated ML models
+      displayName: SDK, interpret, interpret, explain, explainability, interpretability, automated ML, automl, auto ml
+      href: how-to-machine-learning-interpretability-automl.md
   - name: Deploy models
     items:
     - name: Where and how to deploy