Merge pull request #239692 from Blackmist/fixing-links-2

fixing links to avoid redirects

Author: prmerger-automator[bot]

articles/machine-learning/component-reference-v2/regression.md

@@ -37,7 +37,7 @@ AutoML creates a number of pipelines in parallel that try different algorithms a
     Additional configurations|Description
     ------|------
     Primary metric| Main metric used for scoring your model. [Learn more about model metrics](..//how-to-configure-auto-train.md#primary-metric).
-    Explain best model | Select to enable or disable, in order to show explanations for the recommended best model. <br> This functionality is not currently available for [certain forecasting algorithms](../how-to-machine-learning-interpretability-automl.md#interpretability-during-training-for-the-best-model). 
+    Explain best model | Select to enable or disable, in order to show explanations for the recommended best model. <br> This functionality is not currently available for [certain forecasting algorithms](../v1/how-to-machine-learning-interpretability-automl.md#interpretability-during-training-for-the-best-model). 
     Blocked algorithm| Select algorithms you want to exclude from the training job. <br><br> Allowing algorithms is only available for [SDK experiments](../how-to-configure-auto-train.md#supported-algorithms). <br> See the [supported algorithms for each task type](/python/api/azureml-automl-core/azureml.automl.core.shared.constants.supportedmodels).
     Exit criterion| When any of these criteria are met, the training job is stopped. <br> *Training job time (hours)*: How long to allow the training job to run. <br> *Metric score threshold*:  Minimum metric score for all pipelines. This ensures that if you have a defined target metric you want to reach, you do not spend more time on the training job than necessary.
     Concurrency| *Max concurrent iterations*: Maximum number of pipelines (iterations) to test in the training job. The job will not run more than the specified number of iterations. Learn more about how automated ML performs [multiple child jobs on clusters](../how-to-configure-auto-train.md#multiple-child-runs-on-clusters).

articles/machine-learning/component-reference/component-reference.md

@@ -36,7 +36,7 @@ Each component represents a set of code that can run independently and perform a
 
 For help with choosing algorithms, see 
 * [How to select algorithms](../how-to-select-algorithms.md)
-* [Azure Machine Learning Algorithm Cheat Sheet](../algorithm-cheat-sheet.md)
+* [Azure Machine Learning Algorithm Cheat Sheet](../v1/algorithm-cheat-sheet.md)
 
 > [!TIP]
 > In any pipeline in the designer, you can get information about a specific component. Select the **Learn more** link in the component card when hovering on the component in the component list, or in the right pane of the component.

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

@@ -337,7 +337,7 @@ az group delete -g <resource-group-name>
 
 For more information, see the [az ml workspace delete](/cli/azure/ml/workspace#az-ml-workspace-delete) documentation.
 
-If you accidentally deleted your workspace, are still able to retrieve your notebooks. For more information, see the [workspace deletion](./how-to-high-availability-machine-learning.md#workspace-deletion) section of the disaster recovery article.
+If you accidentally deleted your workspace, are still able to retrieve your notebooks. For more information, see the [workspace deletion](./v1/how-to-high-availability-machine-learning.md#workspace-deletion) section of the disaster recovery article.
 
 ## Troubleshooting
 

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

@@ -309,7 +309,7 @@ When you no longer need a workspace, delete it.
 
 [!INCLUDE [machine-learning-delete-workspace](../../includes/machine-learning-delete-workspace.md)]
 
-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).
+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](./v1/how-to-high-availability-machine-learning.md#workspace-deletion).
 
 # [Python SDK](#tab/python)
 

articles/machine-learning/v1/how-to-configure-auto-train-v1.md

@@ -531,7 +531,7 @@ For details on how to create a deployment configuration and deploy a registered
 
 Model interpretability allows you to understand why your models made predictions, and the underlying feature importance values. The SDK includes various packages for enabling model interpretability features, both at training and inference time, for local and deployed models.
 
-See how to [enable interpretability features](../how-to-machine-learning-interpretability-automl.md) specifically within automated ML experiments.
+See how to [enable interpretability features](how-to-machine-learning-interpretability-automl.md) specifically within automated ML experiments.
 
 For general information on how model explanations and feature importance can be enabled in other areas of the SDK outside of automated machine learning, see the [concept article on interpretability](../how-to-machine-learning-interpretability.md) .
 

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

@@ -317,7 +317,7 @@ 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
 

articles/machine-learning/v1/how-to-manage-workspace-cli.md

@@ -267,7 +267,7 @@ You can also delete the resource group, which deletes the workspace and all othe
 az group delete -g <resource-group-name>
 ```
 
-If you accidentally deleted your workspace, are still able to retrieve your notebooks. For more information, see the [workspace deletion](../how-to-high-availability-machine-learning.md#workspace-deletion) section of the disaster recovery article.
+If you accidentally deleted your workspace, are still able to retrieve your notebooks. For more information, see the [workspace deletion](how-to-high-availability-machine-learning.md#workspace-deletion) section of the disaster recovery article.
 
 ## Troubleshooting
 

articles/machine-learning/v1/how-to-manage-workspace.md

@@ -251,7 +251,7 @@ When you no longer need a workspace, delete it.
 
 [!INCLUDE [machine-learning-delete-workspace](../../../includes/machine-learning-delete-workspace.md)]
 
-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).
+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).
 
 
 Delete the workspace `ws`: