Merge pull request #99787 from MicrosoftDocs/master

12/27 AM publish again

Author: huypub

.openpublishing.redirection.json

@@ -123,8 +123,8 @@ This section provides steps for
    * **Tenant URL –** Enter the name of the SuccessFactors OData API services endpoint. Only enter the host name of server without http or https. This value should look like: **api-server-name.successfactors.com**.
 
    * **Notification Email –** Enter your email address, and check the “send email if failure occurs” checkbox.
-         > [!NOTE]
-         > The Azure AD Provisioning Service sends email notification if the provisioning job goes into a [quarantine](/azure/active-directory/manage-apps/application-provisioning-quarantine-status) state.
+    > [!NOTE]
+    > The Azure AD Provisioning Service sends email notification if the provisioning job goes into a [quarantine](/azure/active-directory/manage-apps/application-provisioning-quarantine-status) state.
 
    * Click the **Test Connection** button. If the connection test succeeds, click the **Save** button at  the top. If it fails, double-check that the SuccessFactors credentials and URL are valid.
     >[!div class="mx-imgBorder"]

articles/active-directory/saas-apps/sap-successfactors-writeback-tutorial.md

@@ -168,7 +168,7 @@ The following table details the features and limits of the Basic, Standard, and
 
 ### Azure Machine Learning limits
 
-The latest values for Azure Machine Learning Compute quotas can be found in the [Azure Machine Learning quota page](../articles/machine-learning/service/how-to-manage-quotas.md)
+The latest values for Azure Machine Learning Compute quotas can be found in the [Azure Machine Learning quota page](machine-learning/how-to-manage-quotas.md)
 
 ### Networking limits
 

articles/azure-subscription-service-limits.md

@@ -128,7 +128,7 @@ Cognitive Services that provide exported models for other machine learning tools
 ## Learn more
 
 * [Architecture Guide - What are the machine learning products at Microsoft?](https://docs.microsoft.com/azure/architecture/data-guide/technology-choices/data-science-and-machine-learning)
-* [Machine learning - Introduction to deep learning vs. machine learning](../machine-learning/service/concept-deep-learning-vs-machine-learning.md)
+* [Machine learning - Introduction to deep learning vs. machine learning](../machine-learning/concept-deep-learning-vs-machine-learning.md)
 
 ## Next steps
 

articles/cognitive-services/cognitive-services-and-machine-learning.md

@@ -173,7 +173,7 @@ Subscribe to Azure Machine Learning workspace events to respond to model registr
 
 | Title | Description |
 | ----- | ----- |
-| [Consume Azure Machine Learning events](../machine-learning/service/concept-event-grid-integration.md) | Overview of integrating Azure Machine Learning with Event Grid. |
+| [Consume Azure Machine Learning events](../machine-learning/concept-event-grid-integration.md) | Overview of integrating Azure Machine Learning with Event Grid. |
 | [Azure Event Grid event schema for Azure Machine Learning](event-schema-machine-learning.md) | Shows fields in the Azure Machine Learning events. |
 
 ## Next steps

articles/event-grid/event-sources.md

@@ -15,7 +15,7 @@ Azure Machine Learning simplifies and accelerates the building, training, and de
 
 ## Install Azure Machine Learning on an HDInsight cluster
 
-For general tutorials of automated machine learning, see [Tutorial: Use automated machine learning to build your regression model](../../machine-learning/service/tutorial-auto-train-models.md).
+For general tutorials of automated machine learning, see [Tutorial: Use automated machine learning to build your regression model](../../machine-learning/tutorial-auto-train-models.md).
 All new HDInsight-Spark clusters come pre-installed with AzureML-AutoML SDK. You can get started with AutoML on HDInsight with this [sample Jupyter notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-hdi). This Jupyter Notebook demonstrates how to use an automated machine learning classifier for a simple classification problem.
 
 > [!Note]

articles/hdinsight/spark/apache-spark-run-machine-learning-automl.md

@@ -48,7 +48,7 @@ Use the settings in the table below when setting up a classroom lab.  For more i
 
 The [Data Science Virtual Machine - Windows 2016](https://azuremarketplace.microsoft.com/marketplace/apps/microsoft-dsvm.dsvm-windows) image provides the necessary deep learning frameworks and tools required for this type of class.  The image includes Jupyter Notebooks and Visual Studio Code.  [Jupyter Notebooks](http://jupyter-notebook.readthedocs.io) is a web application that allows data scientists to take raw data, run computations, and see the results all in the same environment.  For our template machine, the web application will be running locally.  [Visual Studio Code](https://code.visualstudio.com/) is an IDE that provides a rich interactive experience when writing and testing a notebook.  For more information, see [Working with Jupyter Notebooks in Visual Studio Code](https://code.visualstudio.com/docs/python/jupyter-support).
 
-The remaining task to set up the class is to provide local notebooks.  For instructions how to use the Azure Machine Learning samples, see [how to configure an environment with Jupyter Notebooks](../../machine-learning/service/how-to-configure-environment.md#jupyter).  You can also provide your own notebooks on the template machine.  The notebooks will be copied to all student machines when the template is published.
+The remaining task to set up the class is to provide local notebooks.  For instructions how to use the Azure Machine Learning samples, see [how to configure an environment with Jupyter Notebooks](../../machine-learning/how-to-configure-environment.md#jupyter).  You can also provide your own notebooks on the template machine.  The notebooks will be copied to all student machines when the template is published.
 
 ## Cost estimate
 

articles/lab-services/classroom-labs/class-type-jupyter-notebook.md

@@ -51,10 +51,10 @@ In reinforcement learning, the algorithm gets to choose an action in response to
 
 ## Next steps
 
-* [Learn about studio in Azure Machine Learning and the Azure portal](service/overview-what-is-azure-ml.md).
+* [Learn about studio in Azure Machine Learning and the Azure portal](overview-what-is-azure-ml.md).
 
 * See a list of algorithms and modules in the [Algorithm and module reference](algorithm-module-reference/module-reference.md).
 
 * [Tutorial: Build a prediction model in Azure Machine Learning designer](service/ui-tutorial-automobile-price-train-score.md).
 
-* [Learn about deep learning vs. machine learning](service/concept-deep-learning-vs-machine-learning.md).
+* [Learn about deep learning vs. machine learning](concept-deep-learning-vs-machine-learning.md).

articles/machine-learning/algorithm-cheat-sheet.md

@@ -24,7 +24,7 @@ This module supports exporting your data to the following cloud data services:
 - Azure Data Lake
 - Azure Data Lake Gen2
 
-Before exporting your data, you need to first register a datastore in your Azure Machine Learning workspace first. For more information, see [How to Access Data](../service/how-to-access-data.md).
+Before exporting your data, you need to first register a datastore in your Azure Machine Learning workspace first. For more information, see [How to Access Data](../how-to-access-data.md).
 
 ## How to configure Export Data
 
@@ -34,7 +34,7 @@ Before exporting your data, you need to first register a datastore in your Azure
 
 1. Select **Export Data** to open the **Properties** pane.
 
-1. For **Datastore**, select an existing datastore from the dropdown list. You can also create a new datastore. Check how by visiting [how-to-access-data](../service/how-to-access-data.md)
+1. For **Datastore**, select an existing datastore from the dropdown list. You can also create a new datastore. Check how by visiting [how-to-access-data](../how-to-access-data.md)
 
 1. Define the path in the datastore to write the data to. 
 

articles/machine-learning/algorithm-module-reference/export-data.md

@@ -18,22 +18,22 @@ This article describes a module in Azure Machine Learning designer (preview).
 Use this module to load data into a machine learning pipeline from existing cloud data services. 
 
 > [!Note]
-> All functionality provided by this module can be done by **datastore** and **datasets** in the worksapce landing page. We recommend you use **datastore** and **dataset** which includes additional features like data monitoring. To learn more, see [How to Access Data](../service/how-to-access-data.md) and [How to Register Datasets](../service/how-to-create-register-datasets.md) article.
+> All functionality provided by this module can be done by **datastore** and **datasets** in the worksapce landing page. We recommend you use **datastore** and **dataset** which includes additional features like data monitoring. To learn more, see [How to Access Data](../how-to-access-data.md) and [How to Register Datasets](../how-to-create-register-datasets.md) article.
 >  After you register a dataset, you can find it in the **Datasets** -> **My Datasets** category in designer interface. This module is reserved for Studio(classic) users to for a familiar experience. 
 >
 
 First, choose the source you are reading from, and finish the additional settings. The **Import Data** module support read data from following sources:
 
 - URL via HTTP
-- Azure cloud storages through [**Datastores**](../service/how-to-access-data.md))
+- Azure cloud storages through [**Datastores**](../how-to-access-data.md))
     - Azure Blob Container
     - Azure File Share
     - Azure Data Lake
     - Azure Data Lake Gen2
     - Azure SQL Database
     - Azure PostgreSQL    
 
-Before using cloud storage, you need to register a datastore in your Azure Machine Learning workspace first. For more information, see [How to Access Data](../service/how-to-access-data.md). 
+Before using cloud storage, you need to register a datastore in your Azure Machine Learning workspace first. For more information, see [How to Access Data](../how-to-access-data.md). 
 
 After you define the data you want and connect to the source, **[Import Data](./import-data.md)** infers the data type of each column based on the values it contains, and loads the data into your designer pipeline. The output of **Import Data** is a dataset that can be used with any designer pipeline.