more fixes

lgayhardt · lgayhardt · commit 4fbc71a8607b · 2023-03-14T07:29:41.000-07:00
diff --git a/articles/machine-learning/v1/how-to-change-storage-access-key.md b/articles/machine-learning/v1/how-to-change-storage-access-key.md
@@ -14,7 +14,7 @@ ms.custom: cliv1, sdkv1, event-tier1-build-2022, ignite-2022
 ---
 
 # Regenerate storage account access keys
-[!INCLUDE [cli v1](../../includes/machine-learning-dev-v1.md)]
+[!INCLUDE [cli v1](../../../includes/machine-learning-dev-v1.md)]
 
 Learn how to change the access keys for Azure Storage accounts used by Azure Machine Learning. Azure Machine Learning can use storage accounts to store data or trained models.
 
@@ -100,7 +100,7 @@ To update Azure Machine Learning to use the new key, use the following steps:
         az ml workspace sync-keys -w myworkspace -g myresourcegroup
         ```
 
-        [!INCLUDE [install extension](../../includes/machine-learning-service-install-extension.md)]
+        [!INCLUDE [install extension](../../../includes/machine-learning-service-install-extension.md)]
 
         This command automatically syncs the new keys for the Azure storage account used by the workspace.
 
diff --git a/articles/machine-learning/v1/how-to-high-availability-machine-learning.md b/articles/machine-learning/v1/how-to-high-availability-machine-learning.md
@@ -107,7 +107,7 @@ Depending on your needs, you may have more compute or data services that are use
 
 __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 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).
@@ -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](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.
@@ -177,7 +177,7 @@ Azure Machine Learning cannot sync or recover artifacts or metadata between work
 
 Depending on your recovery approach, you may need to copy artifacts such as dataset and model objects between the workspaces to continue your work. Currently, the portability of artifacts between workspaces is limited. We recommend managing artifacts as code where possible so that they can be recreated in the failover instance.
 
-The following artifacts can be exported and imported between workspaces by using the [Azure CLI extension for machine learning](v1/reference-azure-machine-learning-cli.md):
+The following artifacts can be exported and imported between workspaces by using the [Azure CLI extension for machine learning](reference-azure-machine-learning-cli.md):
 
 | Artifact | Export | Import |
 | ----- | ----- | ----- |
diff --git a/articles/machine-learning/v1/how-to-machine-learning-fairness-aml.md b/articles/machine-learning/v1/how-to-machine-learning-fairness-aml.md
@@ -15,18 +15,18 @@ ms.custom: devx-track-python, responsible-ml, sdkv1, event-tier1-build-2022
 
 # Use Azure Machine Learning with the Fairlearn open-source package to assess the fairness of ML models (preview)
 
-[!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
+[!INCLUDE [sdk v1](../../../includes/machine-learning-sdk-v1.md)]
 
 In this how-to guide, you will learn to use the [Fairlearn](https://fairlearn.github.io/) open-source Python package with Azure Machine Learning to perform the following tasks:
 
-* Assess the fairness of your model predictions. To learn more about fairness in machine learning, see the [fairness in machine learning article](concept-fairness-ml.md).
+* Assess the fairness of your model predictions. To learn more about fairness in machine learning, see the [fairness in machine learning article](../concept-fairness-ml.md).
 * Upload, list and download fairness assessment insights to/from Azure Machine Learning studio.
 * See a fairness assessment dashboard in Azure Machine Learning studio to interact with your model(s)' fairness insights.
 
 >[!NOTE]
 > Fairness assessment is not a purely technical exercise. **This package can help you assess the fairness of a machine learning model, but only you can configure and make decisions as to how the model performs.**  While this package helps to identify quantitative metrics to assess fairness, developers of machine learning models must also perform a qualitative analysis to evaluate the fairness of their own models.
 
-[!INCLUDE [machine-learning-preview-generic-disclaimer](../../includes/machine-learning-preview-generic-disclaimer.md)]
+[!INCLUDE [machine-learning-preview-generic-disclaimer](../../../includes/machine-learning-preview-generic-disclaimer.md)]
 
 ## Azure Machine Learning Fairness SDK 
 
@@ -348,6 +348,6 @@ Uploading multiple models' fairness insights in a single Run allows for comparis
 
 ## Next steps
 
-[Learn more about model fairness](concept-fairness-ml.md)
+[Learn more about model fairness](../concept-fairness-ml.md)
 
 [Check out Azure Machine Learning Fairness sample notebooks](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness)
diff --git a/articles/machine-learning/v1/how-to-machine-learning-interpretability-aml.md b/articles/machine-learning/v1/how-to-machine-learning-interpretability-aml.md
@@ -32,7 +32,7 @@ In this how-to guide, you learn to use the interpretability package of the Azure
 
 * Deploy a scoring explainer alongside your model to observe explanations during inferencing.
 
-[!INCLUDE [machine-learning-preview-generic-disclaimer](../../includes/machine-learning-preview-generic-disclaimer.md)]
+[!INCLUDE [machine-learning-preview-generic-disclaimer](../../../includes/machine-learning-preview-generic-disclaimer.md)]
 
 For more information on the supported interpretability techniques and machine learning models, see [Model interpretability in Azure Machine Learning](../how-to-machine-learning-interpretability.md) and [sample notebooks](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/explain-model).
 
diff --git a/articles/machine-learning/v1/media/migrate-register-dataset/create-data-asset-2.png b/articles/machine-learning/v1/media/migrate-register-dataset/create-data-asset-2.png
diff --git a/articles/machine-learning/v1/migrate-overview.md b/articles/machine-learning/v1/migrate-overview.md
@@ -50,7 +50,7 @@ To migrate to Azure Machine Learning, we recommend the following approach:
     >[!NOTE]
     > The **designer** feature in Azure Machine Learning provides a similar drag-and-drop experience to Studio (classic). However, Azure Machine Learning also provides robust [code-first workflows](../concept-model-management-and-deployment.md) as an alternative. This migration series focuses on the designer, since it's most similar to the Studio (classic) experience.
 
-    [!INCLUDE [aml-compare-classic](../../includes/machine-learning-compare-classic-aml.md)]
+    [!INCLUDE [aml-compare-classic](../../../includes/machine-learning-compare-classic-aml.md)]
 
 3. Verify that your critical Studio (classic) modules are supported in Azure Machine Learning designer. For more information, see the [Studio (classic) and designer component-mapping](#studio-classic-and-designer-component-mapping) table below.
 
diff --git a/articles/machine-learning/v1/migrate-rebuild-web-service.md b/articles/machine-learning/v1/migrate-rebuild-web-service.md
@@ -23,7 +23,7 @@ Use Azure Machine Learning pipeline endpoints to make predictions, retrain model
 This article is part of the Studio (classic) to Azure Machine Learning migration series. For more information on migrating to Azure Machine Learning, see the [migration overview article](migrate-overview.md).
 
 > [!NOTE]
-> This migration series focuses on the drag-and-drop designer. For more information on deploying models programmatically, see [Deploy machine learning models in Azure](v1/how-to-deploy-and-where.md).
+> This migration series focuses on the drag-and-drop designer. For more information on deploying models programmatically, see [Deploy machine learning models in Azure](how-to-deploy-and-where.md).
 
 ## Prerequisites
 
diff --git a/articles/machine-learning/v1/migrate-register-dataset.md b/articles/machine-learning/v1/migrate-register-dataset.md
@@ -82,7 +82,7 @@ After you download the data file, you can register it as a data asset in Azure M
 1. Navigate to [Azure Machine Learning studio](https://ml.azure.com)
 
 1. Under __Assets__ in the left navigation, select __Data__. On the Data assets tab, select Create
-:::image type="content" source="./media/how-to-create-data-assets/data-assets-create.png" alt-text="Screenshot highlights Create in the Data assets tab.":::
+:::image type="content" source="./media/migrate-register-dataset/data-assets-create-2.png" alt-text="Screenshot highlights Create in the Data assets tab.":::
 
 1. Give your data asset a name and optional description. Then, select the **Tabular** option under **Type**, in the **Dataset types** section of the dropdown.
     > [!NOTE]
