Merge pull request #96011 from craigcaseyMSFT/vcraic1113a

fix broken links from CATS report

Author: PRMerger13

articles/industry/agriculture/imagery-partner-integration.md

@@ -37,7 +37,7 @@ The APIs contain Swagger technical documentation. Review [Swagger](https://aka.m
 
 ## Authentication
 
-FarmBeats leverages Microsoft Azure’s [Active Directory]((https://docs.microsoft.com/azure/app-service/overview-authentication-authorization)). Authentication Azure App Service provides built-in authentication and authorization support. 
+FarmBeats leverages Microsoft Azure’s [Active Directory](https://docs.microsoft.com/azure/app-service/overview-authentication-authorization). Authentication Azure App Service provides built-in authentication and authorization support. 
 
 For more information about, see [Azure Active Directory](https://docs.microsoft.com/azure/app-service/overview-authentication-authorization).   
 

articles/machine-learning/service/azure-machine-learning-release-notes.md

@@ -223,7 +223,7 @@ See the [package website](https://azure.github.io/azureml-sdk-for-r) for complet
     + Added psutil as a dependency of `automl` and included psutil as a conda dependency in amlcompute.
     + Fixed the issue with heuristic lags and rolling window sizes on the forecasting data sets some series of which can cause linear algebra errors
       + Added print out for the heuristically determined parameters in the forecasting runs.
-  + **[azureml-contrib-datadrift](https://docs.microsoft.com/python/api/azureml-contrib-datadrift)**
+  + **azureml-contrib-datadrift**
     + Added protection while creating output metrics if dataset level drift is not in the first section.
   + **azureml-contrib-interpret**
     + azureml-contrib-explain-model package has been renamed to azureml-contrib-interpret
@@ -282,9 +282,9 @@ See the [package website](https://azure.github.io/azureml-sdk-for-r) for complet
         all_first_experiments = Experiment.list(workspace, name="First Experiment", view_type="ALL")
         ```
     + Support using environment for model deploy, and service update.
-  + **[azureml-datadrift](https://docs.microsoft.com/python/api/azureml-contrib-datadrift)**
-    + The show attribute of [DataDriftDetector](https://docs.microsoft.com/python/api/azureml-contrib-datadrift/azureml.contrib.datadrift.datadriftdetector.datadriftdetector) class won't support optional argument 'with_details' any more. The show attribute will only present data drift coefficient and data drift contribution of feature columns.
-    + DataDriftDetector function [get_output](https://docs.microsoft.com/python/api/azureml-contrib-datadrift/azureml.contrib.datadrift.datadriftdetector.datadriftdetector#get-output-start-time--end-time--run-id-none--daily-latest-only-true-) behavior changes:
+  + **[azureml-datadrift](https://docs.microsoft.com/python/api/azureml-datadrift)**
+    + The show attribute of [DataDriftDetector](https://docs.microsoft.com/python/api/azureml-datadrift/azureml.datadrift.datadriftdetector.datadriftdetector) class won't support optional argument 'with_details' any more. The show attribute will only present data drift coefficient and data drift contribution of feature columns.
+    + DataDriftDetector function [get_output]https://docs.microsoft.com/python/api/azureml-datadrift/azureml.datadrift.datadriftdetector.datadriftdetector#get-output-start-time-none--end-time-none--run-id-none-) behavior changes:
       + Input parameter start_time, end_time are optional instead of mandatory;
       + Input specific start_time and/or end_time with a specific run_id in the same invoking will result in value error exception because they are mutually exclusive; 
       + By input specific start_time and/or end_time, only results of scheduled runs will be returned; 
@@ -904,7 +904,7 @@ We reverted a change that improved performance, as it was causing issues for som
 + **Preview features**
     + Integration with [MLflow](https://mlflow.org) 1.0.0 tracking through azureml-mlflow package ([example notebooks](https://aka.ms/azureml-mlflow-examples)).
     + Submit Jupyter notebook as a run. [API Reference Documentation](https://docs.microsoft.com/python/api/azureml-contrib-notebook/azureml.contrib.notebook?view=azure-ml-py)
-    + Public Preview of [Data Drift Detector](https://docs.microsoft.com/python/api/azureml-contrib-datadrift/azureml.contrib.datadrift?view=azure-ml-py) through azureml-contrib-datadrift package ([example notebooks](https://aka.ms/azureml-datadrift-example)). Data Drift is one of the top reasons where model accuracy degrades over time. It happens when data served to model in production is different from the data that the model was trained on. AML Data Drift detector helps customer to monitor data drift and sends alert whenever drift is detected. 
+    + Public Preview of [Data Drift Detector](https://docs.microsoft.com/python/api/azureml-datadrift/azureml.datadrift.datadriftdetector(class)) through azureml-contrib-datadrift package ([example notebooks](https://aka.ms/azureml-datadrift-example)). Data Drift is one of the top reasons where model accuracy degrades over time. It happens when data served to model in production is different from the data that the model was trained on. AML Data Drift detector helps customer to monitor data drift and sends alert whenever drift is detected. 
 
 + **Breaking changes**
 

articles/machine-learning/service/concept-ml-pipelines.md

@@ -80,7 +80,7 @@ Pipelines solve this problem. Azure Machine Learning automatically orchestrates
 
 Additionally, the output of a step may, if you choose, be reused. If you specify reuse as a possibility and there are no upstream dependencies triggering recalculation, the pipeline service will use a cached version of the step's results. Such reuse can dramatically decrease development time. If you have a complex data preparation task, you probably rerun it more often than is strictly necessary. Pipelines relieve you of that worry: if necessary, the step will run, if not, it won't.
 
-All of this dependency analysis, orchestration, and activation are handled by Azure Machine Learning when you instantiate a [Pipeline](https://docs.microsoft.com/api/azureml-pipeline-core/azureml.pipeline.core.pipeline(class)?view=azure-ml-py) object, pass it to an `Experiment`, and call `submit()`. 
+All of this dependency analysis, orchestration, and activation are handled by Azure Machine Learning when you instantiate a [Pipeline](https://docs.microsoft.com/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline(class)?view=azure-ml-py) object, pass it to an `Experiment`, and call `submit()`. 
 
 ### Coordinating the steps involved
 
@@ -102,7 +102,7 @@ When you create and run a `Pipeline` object, the following high-level steps occu
 
 ## Building pipelines with the Python SDK
 
-In the [Azure Machine Learning Python SDK](https://docs.microsoft.com/python/api/overview/azure/ml/install?view=azure-ml-py), a pipeline is a Python object defined in the `azureml.pipeline.core` module. A [Pipeline](https://docs.microsoft.com/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline%28class%29?view=azure-ml-py) object contains an ordered sequence of one or more [PipelineStep](https://docs.microsoft.com/api/azureml-pipeline-core/azureml.pipeline.core.builder.pipelinestep?view=azure-ml-py) objects. The `PipelineStep` class is abstract and the actual steps will be of subclasses such as [EstimatorStep](https://docs.microsoft.com/python/api/azureml-pipeline-steps/azureml.pipeline.steps.estimatorstep?view=azure-ml-py), [PythonScriptStep](https://docs.microsoft.com/python/api/azureml-pipeline-steps/azureml.pipeline.steps.pythonscriptstep?view=azure-ml-py), or [DataTransferStep](https://docs.microsoft.com/python/api/azureml-pipeline-steps/azureml.pipeline.steps.datatransferstep?view=azure-ml-py). The [ModuleStep](https://docs.microsoft.com/python/api/azureml-pipeline-steps/azureml.pipeline.steps.modulestep?view=azure-ml-py) class holds a reusable sequence of steps that can be shared among pipelines. A `Pipeline` runs as part of an `Experiment`.
+In the [Azure Machine Learning Python SDK](https://docs.microsoft.com/python/api/overview/azure/ml/install?view=azure-ml-py), a pipeline is a Python object defined in the `azureml.pipeline.core` module. A [Pipeline](https://docs.microsoft.com/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline%28class%29?view=azure-ml-py) object contains an ordered sequence of one or more [PipelineStep](https://docs.microsoft.com/python/api/azureml-pipeline-core/azureml.pipeline.core.builder.pipelinestep?view=azure-ml-py) objects. The `PipelineStep` class is abstract and the actual steps will be of subclasses such as [EstimatorStep](https://docs.microsoft.com/python/api/azureml-pipeline-steps/azureml.pipeline.steps.estimatorstep?view=azure-ml-py), [PythonScriptStep](https://docs.microsoft.com/python/api/azureml-pipeline-steps/azureml.pipeline.steps.pythonscriptstep?view=azure-ml-py), or [DataTransferStep](https://docs.microsoft.com/python/api/azureml-pipeline-steps/azureml.pipeline.steps.datatransferstep?view=azure-ml-py). The [ModuleStep](https://docs.microsoft.com/python/api/azureml-pipeline-steps/azureml.pipeline.steps.modulestep?view=azure-ml-py) class holds a reusable sequence of steps that can be shared among pipelines. A `Pipeline` runs as part of an `Experiment`.
 
 An Azure ML pipeline is associated with an Azure Machine Learning workspace and a pipeline step is associated with a compute target available within that workspace. For more information, see [Create and manage Azure Machine Learning workspaces in the Azure portal](https://docs.microsoft.com/azure/machine-learning/service/how-to-manage-workspace) or [What are compute targets in Azure Machine Learning?](https://docs.microsoft.com/azure/machine-learning/service/concept-compute-target).
 

articles/machine-learning/service/concept-model-management-and-deployment.md

@@ -164,6 +164,6 @@ Learn more by reading and exploring the following resources:
 
 + Create clients that [consume a deployed model](how-to-consume-web-service.md)
 
-+ [Machine learning at scale](/architecture/data-guide/big-data/machine-learning-at-scale)
++ [Machine learning at scale](/azure/architecture/data-guide/big-data/machine-learning-at-scale)
 
 + [Azure AI reference architectures & best practices rep](https://github.com/microsoft/AI)

articles/machine-learning/service/how-to-monitor-data-drift.md

@@ -32,7 +32,7 @@ With Azure Machine Learning, you can monitor the inputs to a model deployed on A
 + Send alerts to data drift by email.
 
 > [!Note]
-> This service is in (preview) and limited in configuration options. Please see our [API Documentation](https://docs.microsoft.com/python/api/azureml-contrib-datadrift/?view=azure-ml-py) and [Release Notes](azure-machine-learning-release-notes.md) for details and updates. 
+> This service is in (preview) and limited in configuration options. Please see our [API Documentation](https://docs.microsoft.com/python/api/azureml-datadrift/) and [Release Notes](azure-machine-learning-release-notes.md) for details and updates. 
 
 ### How data drift is monitored in Azure Machine Learning
 
@@ -93,7 +93,7 @@ print('Details of Datadrift Object:\n{}'.format(datadrift))
 
 ## Submit a DataDriftDetector run
 
-With the `DataDriftDetector` object configured, you can submit a [data drift run](https://docs.microsoft.com/python/api/azureml-contrib-datadrift/azureml.contrib.datadrift.datadriftdetector%28class%29?view=azure-ml-py#run-target-date--services--compute-target-name-none--create-compute-target-false--feature-list-none--drift-threshold-none-) on a given date for the model. As part of the run, enable DataDriftDetector alerts by setting the `drift_threshold` parameter. If the [datadrift_coefficient](#metrics) is above the given `drift_threshold`, an email is sent.
+With the `DataDriftDetector` object configured, you can submit a [data drift run](https://docs.microsoft.com/python/api/azureml-datadrift/azureml.datadrift.datadriftdetector.datadriftdetector#run-target-date--services-none--compute-target-none--create-compute-target-false--feature-list-none--drift-threshold-none-) on a given date for the model. As part of the run, enable DataDriftDetector alerts by setting the `drift_threshold` parameter. If the [datadrift_coefficient](#visualize-drift-metrics) is above the given `drift_threshold`, an email is sent.
 
 ```python
 # adhoc run today

articles/media-services/latest/streaming-locators-concept.md

@@ -90,7 +90,7 @@ To get Streaming Locators based on the associated Asset name, use the following
 |CLI|[az ams asset list-streaming-locators](https://docs.microsoft.com/cli/azure/ams/asset?view=azure-cli-latest#az-ams-asset-list-streaming-locators)|
 |.NET|[ListStreamingLocators](https://docs.microsoft.com/dotnet/api/microsoft.azure.management.media.assetsoperationsextensions.liststreaminglocators?view=azure-dotnet#Microsoft_Azure_Management_Media_AssetsOperationsExtensions_ListStreamingLocators_Microsoft_Azure_Management_Media_IAssetsOperations_System_String_System_String_System_String_)|
 |Java|[AssetStreamingLocator](https://docs.microsoft.com/java/api/com.microsoft.azure.management.mediaservices.v2018_07_01.assetstreaminglocator?view=azure-java-stable)|
-|Node.js|[listStreamingLocators](https://docs.microsoft.com/javascript/api/azure-arm-mediaservices/assets?view=azure-node-latest#liststreaminglocators-string--string--string--object-)|
+|Node.js|[listStreamingLocators](https://docs.microsoft.com/javascript/api/@azure/arm-mediaservices/assets#liststreaminglocators-string--string--string--msrest-requestoptionsbase-)|
 
 ## Also see
 

articles/service-bus-messaging/service-bus-nodejs-how-to-use-topics-subscriptions.md

@@ -167,7 +167,7 @@ queue.
 > check if the subscription exists by using the
 > `getSubscription` method.
 >
-> You can have the subscriptions automatically deleted by setting the [AutoDeleteOnIdle property](https://docs.microsoft.com/javascript/api/azure-arm-sb/sbsubscription?view=azure-node-latest#autodeleteonidle).
+> You can have the subscriptions automatically deleted by setting the [AutoDeleteOnIdle property](https://docs.microsoft.com/javascript/api/@azure/arm-servicebus/sbsubscription?view=azure-node-latest#autodeleteonidle).
 
 ### Create a subscription with the default (MatchAll) filter
 The **MatchAll** filter is the default filter used when a subscription is created. When you use the **MatchAll**
@@ -406,7 +406,7 @@ to handle duplicate message delivery. You can use the
 delivery attempts.
 
 ## Delete topics and subscriptions
-Topics and subscriptions are persistent unless the [autoDeleteOnIdle property](https://docs.microsoft.com/javascript/api/azure-arm-sb/sbsubscription?view=azure-node-latest#autodeleteonidle) is set, and must be explicitly deleted
+Topics and subscriptions are persistent unless the [autoDeleteOnIdle property](https://docs.microsoft.com/javascript/api/@azure/arm-servicebus/sbsubscription?view=azure-node-latest#autodeleteonidle) is set, and must be explicitly deleted
 either through the [Azure portal][Azure portal] or programmatically.
 The following example demonstrates how to delete the topic named `MyTopic`:
 

articles/sql-database-edge/onnx-overview.md

@@ -41,7 +41,7 @@ To obtain a model in the ONNX format:
 
 Currently, not all ONNX models are supported by Azure SQL Database Edge. The support is limited to models with **numeric data types**:
 
-- [int and bigint](https://docs.microsoft.com/sql/t-sql/data-types/int-bigint-smallint-and-tinyint-transact-sql5)
+- [int and bigint](https://docs.microsoft.com/sql/t-sql/data-types/int-bigint-smallint-and-tinyint-transact-sql)
 - [real and float](https://docs.microsoft.com/sql/t-sql/data-types/float-and-real-transact-sql).
 
 Other numeric types can be converted to supported types by using [CAST and CONVERT](https://docs.microsoft.com/sql/t-sql/functions/cast-and-convert-transact-sql).

articles/sql-database-edge/stream-analytics.md

@@ -32,7 +32,7 @@ Azure SQL Database Edge exposes two optional parameters through the *module twin
 |Field | Description |
 |------|-------------|
 | SQLPackage | Azure Blob Storage URI for the *.zip file containing the SQL Database DAC package.
-| ASAJobInfo | Azure Blob Storage URI for the ASA Edge job. For more information on publishing the ASA Edge job, refer [Publishing an ASA Edge job for SQL Database Edge]().
+| ASAJobInfo | Azure Blob Storage URI for the ASA Edge job. For more information on publishing the ASA Edge job, refer [Publishing an ASA Edge job for SQL Database Edge](/azure/sql-database-edge/stream-analytics#using-streaming-jobs-with-sql-database-edge).
 
 ## Using SQL Database DAC packages with SQL Database Edge
 

articles/storage/blobs/storage-blob-change-feed-how-to.md

@@ -31,9 +31,9 @@ To learn more about the change feed, see [Change feed in Azure Blob Storage (Pre
 
 ## Connect to the storage account
 
-Parse the connection string by calling the [CloudStorageAccount.TryParse](/dotnet/api/microsoft.windowsazure.storage.cloudstorageaccount.tryparse) method. 
+Parse the connection string by calling the [CloudStorageAccount.TryParse](/dotnet/api/microsoft.azure.storage.cloudstorageaccount.tryparse) method. 
 
-Then, create an object that represents Blob Storage in your storage account by calling the [CloudStorageAccount.CreateCloudBlobClient](https://docs.microsoft.com/dotnet/api/microsoft.windowsazure.storage.cloudstorageaccount.createcloudblobclient?view=azure-dotnet) method.
+Then, create an object that represents Blob Storage in your storage account by calling the [CloudStorageAccount.CreateCloudBlobClient](https://docs.microsoft.com/dotnet/api/microsoft.azure.storage.blob.blobaccountextensions.createcloudblobclient) method.
 
 ```cs
 public bool GetBlobClient(ref CloudBlobClient cloudBlobClient, string storageConnectionString)