Merge pull request #97713 from john-par/1614298-clean-up-broken-azure-docs-pr-links-12-03

GitHubber17 · web-flow · commit 79b65f060ddb · 2019-12-03T17:01:38.000-08:00
1614298 clean up broken azure docs pr links 12-03
diff --git a/articles/active-directory/develop/msal-python-adfs-support.md b/articles/active-directory/develop/msal-python-adfs-support.md
@@ -24,7 +24,7 @@ ms.collection: M365-identity-device-management
 
 # Active Directory Federation Services support in MSAL for Python
 
-Active Directory Federation Services (AD FS) in Windows Server enables you to add OpenID Connect and OAuth 2.0 based authentication and authorization to your apps by using the Microsoft Authentication Library (MSAL) for Python. Using the MSAL for Python library, your app can authenticate users directly against AD FS. For more information about scenarios, see [AD FS Scenarios for Developers](https://docs.microsoft.com/windows-server/identity/ad-fs/overview/ad-fs-scenarios-for-developers).
+Active Directory Federation Services (AD FS) in Windows Server enables you to add OpenID Connect and OAuth 2.0 based authentication and authorization to your apps by using the Microsoft Authentication Library (MSAL) for Python. Using the MSAL for Python library, your app can authenticate users directly against AD FS. For more information about scenarios, see [AD FS Scenarios for Developers](/windows-server/identity/ad-fs/ad-fs-development).
 
 There are usually two ways of authenticating against AD FS:
 
diff --git a/articles/active-directory/fundamentals/active-directory-ops-guide-auth.md b/articles/active-directory/fundamentals/active-directory-ops-guide-auth.md
@@ -358,7 +358,7 @@ Having access to sign-in activity, audits and risk events for Azure AD is crucia
 - [Get data using the Azure AD Reporting API with certificates](https://docs.microsoft.com/azure/active-directory/active-directory-reporting-api-with-certificates)
 - [Microsoft Graph for Azure Active Directory Identity Protection](https://docs.microsoft.com/azure/active-directory/active-directory-identityprotection-graph-getting-started)
 - [Office 365 Management Activity API reference](https://msdn.microsoft.com/office-365/office-365-management-activity-api-reference)
-- [How to use the Azure Active Directory Power BI Content Pack](https://docs.microsoft.com/azure/active-directory/active-directory-reporting-power-bi-content-pack-how-to)
+- [How to use the Azure Active Directory Power BI Content Pack](../reports-monitoring/howto-use-azure-monitor-workbooks.md)
 
 ## Summary
 
diff --git a/articles/active-directory/saas-apps/ringcentral-provisioning-tutorial.md b/articles/active-directory/saas-apps/ringcentral-provisioning-tutorial.md
@@ -90,7 +90,7 @@ Before configuring RingCentral for automatic user provisioning with Azure AD, yo
 This section guides you through the steps to configure the Azure AD provisioning service to create, update, and disable users and/or groups in RingCentral based on user and/or group assignments in Azure AD.
 
 > [!TIP]
-> You may also choose to enable SAML-based single sign-on for RingCentral , following the instructions provided in the [RingCentral Single sign-on tutorial](https://docs.microsoft.comazure/active-directory/saas-apps/ringcentral-tutorial). Single sign-on can be configured independently of automatic user provisioning, though these two features compliment each other.
+> You may also choose to enable SAML-based single sign-on for RingCentral , following the instructions provided in the [RingCentral Single sign-on tutorial](ringcentral-tutorial.md). Single sign-on can be configured independently of automatic user provisioning, though these two features compliment each other.
 
 > [!NOTE]
 > To learn more about RingCentral's SCIM endpoint, refer to [RingCentral API Reference](https://developers.ringcentral.com/api-reference).
diff --git a/articles/azure-maps/index.yml b/articles/azure-maps/index.yml
@@ -45,7 +45,7 @@ sections:
     style: unordered
     items:
     - html: <a href="/azure/azure-maps/tutorial-geofence">Set up a geofence</a>
-    - html: <a href="/azure-maps/tutorial-iot-hub-maps">Spatial analytics</a>
+    - html: <a href="/azure/azure-maps/tutorial-iot-hub-maps">Spatial analytics</a>
     - html: <a href=/azure/azure-maps/tutorial-ev-routing">EV routing using Azure Notebooks (Python)</a>
 - title: Reference
   items:
diff --git a/articles/expressroute/expressroute-locations.md b/articles/expressroute/expressroute-locations.md
@@ -78,7 +78,7 @@ The following table shows locations by service provider. If you want to view ava
 | **[Ascenty Data Centers](https://www.ascenty.com/en/cloud/microsoft-express-route)** |Supported |Supported |Sao Paulo |
 | **[AT&T NetBond](https://www.synaptic.att.com/clouduser/html/productdetail/ATT_NetBond.htm)** |Supported |Supported |Amsterdam, Chicago, Dallas, London, Silicon Valley, Singapore, Sydney, Tokyo, Toronto, Washington DC |
 | **[Bell Canada](https://business.bell.ca/shop/enterprise/cloud-connect-access-to-cloud-partner-services)** |Supported |Supported |Montreal, Toronto, Quebec City |
-| **[British Telecom](https://www.globalservices.bt.com/en/solutions/products/bt-compute-for-microsoft-azure)** |Supported |Supported |Amsterdam, Hong Kong SAR, Johannesburg, London, Newport(Wales), Sao Paulo, Silicon Valley, Singapore, Sydney, Tokyo, Washington DC |
+| **[British Telecom](https://www.globalservices.bt.com/en/solutions/products/cloud-connect-azure)** |Supported |Supported |Amsterdam, Hong Kong SAR, Johannesburg, London, Newport(Wales), Sao Paulo, Silicon Valley, Singapore, Sydney, Tokyo, Washington DC |
 | **[C3ntro](https://www.c3ntro.com/data1/express-route1.php)** |Supported |Supported |Miami |
 | **CDC** | Supported | Supported | Canberra, Canberra2 |
 | **[CenturyLink Cloud Connect](https://www.centurylink.com/cloudconnect)** |Supported |Supported |Amsterdam2, Chicago, Hong Kong, Las Vegas, New York, Paris, San Antonio, Silicon Valley, Tokyo, Toronto, Washington DC |
@@ -175,7 +175,7 @@ Azure national clouds are isolated from each other and from global commerical Az
 | **Service provider** | **Microsoft Azure** | **Office 365** | **Locations** |
 | --- | --- | --- | --- |
 | **China Telecom** |Supported |Not Supported |Beijing, Beijing2, Shanghai, Shanghai2 |
-| **[GDS](http://en.gds-services.com/news_detail/newsId=21.html)** |Supported |Not Supported |Beijing2, Shanghai2 |
+| **[GDS](http://www.gds-services.com/en/about_2.html)** |Supported |Not Supported |Beijing2, Shanghai2 |
 
 To learn more, see [ExpressRoute in China](http://www.windowsazure.cn/home/features/expressroute/).
 
diff --git a/articles/hdinsight/spark/apache-spark-run-machine-learning-automl.md b/articles/hdinsight/spark/apache-spark-run-machine-learning-automl.md
@@ -65,7 +65,7 @@ You can also register the datastore with the workspace using a one-time registra
 
 ## Experiment submission
 
-In the [automated machine learning configuration](https://docs.microsoft.com/python/api/azureml-train-automl/azureml.train.automl.automlconfig), the property `spark_context` should be set for the package to run on distributed mode. The property `concurrent_iterations`, which is the maximum number of iterations executed in parallel, should be set to a number less than the executor cores for the Spark app.
+In the [automated machine learning configuration](/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig), the property `spark_context` should be set for the package to run on distributed mode. The property `concurrent_iterations`, which is the maximum number of iterations executed in parallel, should be set to a number less than the executor cores for the Spark app.
 
 ## Next steps
 
diff --git a/articles/machine-learning/service/azure-machine-learning-release-notes.md b/articles/machine-learning/service/azure-machine-learning-release-notes.md
@@ -216,7 +216,7 @@ See the [package website](https://azure.github.io/azureml-sdk-for-r) for complet
     + Change [`Dataset.get_by_id`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset%28class%29#get-by-id-workspace--id-) to return registration name and version if the dataset is registered.
     + Fix a bug that ScriptRunConfig with dataset as argument cannot be used repeatedly to submit experiment run.
     + Datasets retrieved during a run will be tracked and can be seen in the run details page or by calling [`run.get_details()`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.run%28class%29#get-details--) after the run is complete.
-    + Allow intermediate data in Azure Machine Learning Pipeline to be converted to tabular dataset and used in [`AutoMLStep`](https://docs.microsoft.com/python/api/azureml-train-automl/azureml.train.automl.automlstep).
+    + Allow intermediate data in Azure Machine Learning Pipeline to be converted to tabular dataset and used in [`AutoMLStep`](/python/api/azureml-train-automl-runtime/azureml.train.automl.runtime.automlstep).
     + Added support for deploying and packaging supported models (ONNX, scikit-learn, and TensorFlow) without an InferenceConfig instance.
     + Added overwrite flag for service deployment (ACI and AKS) in SDK and CLI. If provided, will overwrite the existing service if service with name already exists. If service doesn't exist, will create new service.
     +  Models can be registered with two new frameworks, Onnx and Tensorflow. Model registration accepts sample input data, sample output data and resource configuration for the model.
@@ -233,7 +233,7 @@ See the [package website](https://azure.github.io/azureml-sdk-for-r) for complet
     + Various bug fixes 
   + [**azureml-pipeline-core**](https://docs.microsoft.com/python/api/azureml-pipeline-core)
     + azureml-dataprep is no longer needed to submit an Azure Machine Learning Pipeline run from the pipeline `yaml` file.
-  + [**azureml-train-automl**](https://docs.microsoft.com/python/api/azureml-train-automl)
+  + [**azureml-train-automl**](/python/api/azureml-train-automl-runtime/)
     + Add azureml-defaults to auto generated conda env to solve the model deployment failure
     + AutoML remote training now includes azureml-defaults to allow reuse of training env for inference.
   + **azureml-train-core**
@@ -345,7 +345,7 @@ See the [package website](https://azure.github.io/azureml-sdk-for-r) for complet
     + Improved performance for large Pipeline creation.
   + **[azureml-train-core](https://docs.microsoft.com/python/api/azureml-train-core)**
     + Added TensorFlow 2.0 support in [TensorFlow](https://docs.microsoft.com/python/api/azureml-train-core/azureml.train.dnn.tensorflow) Estimator.
-  + **[azureml-train-automl](https://docs.microsoft.com/python/api/azureml-train-automl)**
+  + **[azureml-train-automl](/python/api/azureml-train-automl-runtime/)**
     + The parent run will no longer be failed when setup iteration failed, as the orchestration already takes care of it.
     + Added local-docker and local-conda support for AutoML experiments
     + Added local-docker and local-conda support for AutoML experiments.
@@ -369,12 +369,12 @@ The Experiment tab in the [new workspace portal](https://ml.azure.com) has been
     + Added curated environments. These environments have been pre-configured with libraries for common machine learning tasks, and have been pre-build and cached as Docker images for faster execution. They appear by default in [Workspace](https://docs.microsoft.com/python/api/azureml-core/azureml.core.workspace%28class%29)'s list of environment, with prefix "AzureML".
   
   + **azureml-train-automl**
-  + **[azureml-train-automl](https://docs.microsoft.com/python/api/azureml-train-automl)**
+  + **[azureml-train-automl](/python/api/azureml-train-automl-runtime/)**
     + Added the ONNX conversion support for the ADB and HDI
 
 + **Preview features**  
   + **azureml-train-automl**
-  + **[azureml-train-automl](https://docs.microsoft.com/python/api/azureml-train-automl)**
+  + **[azureml-train-automl](/python/api/azureml-train-automl-runtime/)**
     + Supported BERT and BiLSTM as text featurizer (preview only)
     + Supported featurization customization for column purpose and transformer parameters (preview only)
     + Supported raw explanations when user enables model explanation during training (preview only)
@@ -386,7 +386,7 @@ The Experiment tab in the [new workspace portal](https://ml.azure.com) has been
 + **Bug fixes and improvements**
   + **azureml-automl-core**
     + Introduced FeaturizationConfig to AutoMLConfig and AutoMLBaseSettings
-    + Introduced FeaturizationConfig to [AutoMLConfig](https://docs.microsoft.com/python/api/azureml-train-automl/azureml.train.automl.automlconfig) and AutoMLBaseSettings
+    + Introduced FeaturizationConfig to [AutoMLConfig](/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig) and AutoMLBaseSettings
       + Override Column Purpose for Featurization with given column and feature type
       + Override transformer parameters
     + Added deprecation message for explain_model() and retrieve_model_explanations()
@@ -431,9 +431,9 @@ The Experiment tab in the [new workspace portal](https://ml.azure.com) has been
   + **[azureml-pipeline-steps](https://docs.microsoft.com/python/api/azureml-pipeline-steps)**
     + Added [RScriptStep](https://docs.microsoft.com/python/api/azureml-pipeline-steps/azureml.pipeline.steps.rscriptstep) to support R script run via AML pipeline.
     + Fixed metadata parameters parsing in [AzureBatchStep](https://docs.microsoft.com/python/api/azureml-pipeline-steps/azureml.pipeline.steps.azurebatchstep) which was causing the error message "assignment for parameter SubscriptionId is not specified".
-  + **[azureml-train-automl](https://docs.microsoft.com/python/api/azureml-train-automl)**
+  + **[azureml-train-automl](/python/api/azureml-train-automl-runtime/)**
     + Supported training_data, validation_data, label_column_name, weight_column_name as data input format.
-    + Added deprecation message for [explain_model()](https://docs.microsoft.com/python/api/azureml-train-automl/azureml.train.automl.automlexplainer#explain-model-fitted-model--x-train--x-test--best-run-none--features-none--y-train-none----kwargs-) and [retrieve_model_explanations()](https://docs.microsoft.com/python/api/azureml-train-automl/azureml.train.automl.automlexplainer#retrieve-model-explanation-child-run-).
+    + Added deprecation message for [explain_model()](/python/api/azureml-train-automl-runtime/azureml.train.automl.runtime.automlexplainer#explain-model-fitted-model--x-train--x-test--best-run-none--features-none--y-train-none----kwargs-) and [retrieve_model_explanations()](/python/api/azureml-train-automl-runtime/azureml.train.automl.runtime.automlexplainer#retrieve-model-explanation-child-run-).
 
   
 ## 2019-09-16
diff --git a/articles/machine-learning/service/concept-automated-ml.md b/articles/machine-learning/service/concept-automated-ml.md
@@ -96,7 +96,7 @@ Additional advanced preprocessing and featurization are also available, such as
 
 + Azure Machine Learning studio : Selecting the **View featurization settings** in the **Configuration Run** section [with these steps](how-to-create-portal-experiments.md).
 
-+ Python SDK: Specifying `"feauturization": auto' / 'off' / FeaturizationConfig` for the [`AutoMLConfig` class](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig?view=azure-ml-py).
++ Python SDK: Specifying `"feauturization": auto' / 'off' / FeaturizationConfig` for the [`AutoMLConfig` class](/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig).
 
 ## Prevent over-fitting
 
diff --git a/articles/machine-learning/service/how-to-auto-train-forecast.md b/articles/machine-learning/service/how-to-auto-train-forecast.md
@@ -18,7 +18,7 @@ ms.date: 11/04/2019
 In this article, you learn how to train a time-series forecasting regression model using automated machine learning in Azure Machine Learning. Configuring a forecasting model is similar to setting up a standard regression model using automated machine learning, but certain configuration options and pre-processing steps exist for working with time-series data. The following examples show you how to:
 
 * Prepare data for time series modeling
-* Configure specific time-series parameters in an [`AutoMLConfig`](/python/api/azureml-train-automl/azureml.train.automl.automlconfig) object
+* Configure specific time-series parameters in an [`AutoMLConfig`](/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig) object
 * Run predictions with time-series data
 
 > [!VIDEO https://www.microsoft.com/videoplayer/embed/RE2X1GW]
@@ -122,7 +122,7 @@ The `AutoMLConfig` object defines the settings and data necessary for an automat
 |`target_rolling_window_size`|*n* historical periods to use to generate forecasted values, <= training set size. If omitted, *n* is the full training set size. Specify this parameter when you only want to consider a certain amount of history when training the model.||
 |`enable_dnn`|Enable Forecasting DNNs.||
 
-See the [reference documentation](https://docs.microsoft.com/python/api/azureml-train-automl/azureml.train.automl.automlconfig?view=azure-ml-py) for more information.
+See the [reference documentation](/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig) for more information.
 
 Create the time-series settings as a dictionary object. Set the `time_column_name` to the `day_datetime` field in the data set. Define the `grain_column_names` parameter to ensure that **two separate time-series groups** are created for the data; one for store A and B. Lastly, set the `max_horizon` to 50 in order to predict for the entire test set. Set a forecast window to 10 periods with `target_rolling_window_size`, and specify a single lag on the target values for 2 periods ahead with the `target_lags` parameter.
 
diff --git a/articles/machine-learning/service/how-to-deploy-functions.md b/articles/machine-learning/service/how-to-deploy-functions.md
@@ -231,7 +231,7 @@ At this point, the function app begins loading the image.
 
 ## Next steps
 
-* Learn to configure your Functions App in the [Functions](https://docs.microsoft.com/azure/azure-functions/functions-create-function-linux-custom-imag) documentation.
+* Learn to configure your Functions App in the [Functions](/azure/azure-functions/functions-create-function-linux-custom-image) documentation.
 * Learn more about Blob storage triggers [Azure Blob storage bindings](https://docs.microsoft.com/azure/azure-functions/functions-bindings-storage-blob).
 * [Deploy your model to Azure App Service](how-to-deploy-app-service.md).
 * [Consume a ML Model deployed as a web service](how-to-consume-web-service.md)
diff --git a/articles/service-fabric/service-fabric-best-practices-security.md b/articles/service-fabric/service-fabric-best-practices-security.md
@@ -161,7 +161,7 @@ To give your application access to secrets, include the certificate by adding a
 ```
 ## Authenticate Service Fabric applications to Azure Resources using Managed Service Identity (MSI)
 
-To learn about managed identities for Azure resources, see [What is managed identities for Azure resources?](https://docs.microsoft.com/azure/active-directory/managed-identities-azure-resources/overview#how-does-it-work).
+To learn about managed identities for Azure resources, see [What is managed identities for Azure resources?](../active-directory/managed-identities-azure-resources/overview.md#how-does-the-managed-identities-for-azure-resources-work).
 Azure Service Fabric clusters are hosted on Virtual Machine Scale Sets, which support [Managed Service Identity](https://docs.microsoft.com/azure/active-directory/managed-identities-azure-resources/services-support-msi#azure-services-that-support-managed-identities-for-azure-resources).
 To get a list of services that MSI can be used to authenticate to, see [Azure Services that support Azure Active Directory Authentication](https://docs.microsoft.com/azure/active-directory/managed-identities-azure-resources/services-support-msi#azure-services-that-support-azure-ad-authentication).
 
diff --git a/articles/sql-database/azure-sql-database-hangs-importing-exporting.md b/articles/sql-database/azure-sql-database-hangs-importing-exporting.md
@@ -17,7 +17,7 @@ When you use the Azure SQL Database Import/Export service, the process might tak
 
 ## Azure SQL Database Import/Export service
 
-The Azure SQL Database Import/Export service is a REST-based web service that runs in every Azure data center. This service is called when you use either the [Import database](https://docs.microsoft.com/azure/sql-database/sql-database-import#import-from-a-bacpac-file-in-the-azure-portal) or [Export](https://docs.microsoft.com/azure/sql-database/sql-database-export#export-to-a-bacpac-file-using-the-azure-portal) option to move your SQL database in the Azure portal. The service provides free request queuing and compute services to perform imports and exports between an Azure SQL database and Azure Blob storage.
+The Azure SQL Database Import/Export service is a REST-based web service that runs in every Azure data center. This service is called when you use either the [Import database](sql-database-import.md#using-azure-portal) or [Export](https://docs.microsoft.com/azure/sql-database/sql-database-export#export-to-a-bacpac-file-using-the-azure-portal) option to move your SQL database in the Azure portal. The service provides free request queuing and compute services to perform imports and exports between an Azure SQL database and Azure Blob storage.
 
 The import and export operations don't represent a traditional physical database backup but instead a logical backup of the database that uses a special BACPAC format. The BACPAC format lets you avoid having to use a physical format that might vary between versions of Microsoft SQL Server and Azure SQL Database. Therefore, you can use it to safely restore the database to a SQL Server database and to a SQL database.
 
diff --git a/articles/virtual-machines/maintenance-and-updates.md b/articles/virtual-machines/maintenance-and-updates.md
diff --git a/articles/virtual-machines/maintenance-notifications.md b/articles/virtual-machines/maintenance-notifications.md
