internal links

Author: v-thepet

articles/containers/index.yml

@@ -70,10 +70,10 @@ conceptualContent:
         - url: /azure/app-service/app-service-web-get-started-windows-container
           itemType: get-started
           text: Run a custom Windows container
-        - url: /azure/app-service/quickstart-docker
+        - url: /azure/app-service/containers/quickstart-docker
           itemType: quickstart 
           text: Run a custom Linux container
-        - url: /azure/app-service/quickstart-multi-container
+        - url: /azure/app-service/containers/quickstart-multi-container
           itemType: quickstart 
           text: Create a Linux multi-container app
       # footerLink (optional)
@@ -92,7 +92,7 @@ conceptualContent:
         - url: /azure/container-instances/container-instances-using-azure-container-registry
           itemType: how-to-guide
           text: Deploy an image from Azure Container Registry
-        - url: /azure/container-registry/container-registry-quick-task-cli
+        - url: /azure/container-registry/container-registry-quickstart-task-cli
           itemType: get-started
           text: Build, push, and run image using Azure CLI
       # footerLink (optional)
@@ -140,7 +140,7 @@ conceptualContent:
         - url: /azure/container-registry/container-registry-get-started-azure-cli
           itemType: get-started
           text: Create a private container registry using Azure CLI
-        - url: /azure/container-registry/container-registry-quick-task-cli
+        - url: /azure/container-registry/container-registry-quickstart-task-cli
           itemType: get-started
           text: Build, push, and run image using Azure CLI
       # footerLink (optional)

articles/machine-learning/concept-azure-machine-learning-architecture.md

@@ -183,7 +183,7 @@ For more information, see the following articles:
 * [Train ML models with estimators](how-to-train-ml-models.md).
 * [Train Pytorch deep learning models at scale with Azure Machine Learning](how-to-train-pytorch.md).
 * [Train and register TensorFlow models at scale with Azure Machine Learning](how-to-train-tensorflow.md).
-* [Train and register Chainer models at scale with Azure Machine Learning](how-to-train-chainer.md).
+* [Train and register Chainer models at scale with Azure Machine Learning](how-to-train-ml-models.md).
 
 ### Endpoints
 

articles/machine-learning/concept-deep-learning-vs-machine-learning.md

@@ -113,6 +113,6 @@ The following articles show you how to use deep learning technology in [Azure Ma
 
 - [Classify images by using a Pytorch model](https://docs.microsoft.com/azure/machine-learning/how-to-train-pytorch?WT.mc_id=docs-article-lazzeri)
 
-- [Classify handwritten digits by using a Chainer model](https://docs.microsoft.com/azure/machine-learning/how-to-train-chainer?WT.mc_id=docs-article-lazzeri)
+- [Classify handwritten digits by using a Chainer model](https://docs.microsoft.com/azure/machine-learning/how-to-train-ml-models)
 
 Also, use the [Machine Learning Algorithm Cheat Sheet](../synapse-analytics/sql-data-warehouse/cheat-sheet.md) to choose algorithms for your model.

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

@@ -192,7 +192,7 @@ Learn about the specific definitions of these metrics in [Understand automated m
 
 In every automated machine learning experiment, your data is [automatically scaled and normalized](concept-automated-ml.md#preprocess) to help *certain* algorithms that are sensitive to features that are on different scales.  However, you can also enable additional featurization, such as missing values imputation, encoding, and transforms. [Learn more about what featurization is included](how-to-use-automated-ml-for-ml-models.md#featurization).
 
-When configuring your experiments, you can enable the advanced setting `featurization`. The following table shows the accepted settings for featurization in the [`AutoMLConfig` class](https://docs.microsoft.com/python/api/azureml-train-automl/azureml.train.automl.automlconfig?view=azure-ml-py).
+When configuring your experiments, you can enable the advanced setting `featurization`. The following table shows the accepted settings for featurization in the [AutoMLConfig class](/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig).
 
 |Featurization Configuration | Description |
 | ------------- | ------------- |

articles/time-series-insights/time-series-insights-authentication-and-authorization.md

@@ -99,7 +99,7 @@ This section describes common HTTP request headers and parameters used to make q
 
 ### Authentication
 
-To perform authenticated queries against the [Time Series Insights REST APIs](https://docs.microsoft.com/rest/api/time-series-insights/), a valid OAuth 2.0 bearer token must be passed in the [Authorization header](/rest/api/apimanagement/2019-01-01/authorizationserver/createorupdate) using a REST client of your choice (Postman, JavaScript, C#). 
+To perform authenticated queries against the [Time Series Insights REST APIs](https://docs.microsoft.com/rest/api/time-series-insights/), a valid OAuth 2.0 bearer token must be passed in the [Authorization header](/rest/api/apimanagement/2019-12-01/authorizationserver/createorupdate) using a REST client of your choice (Postman, JavaScript, C#). 
 
 > [!TIP]
 > Read the hosted Azure Time Series Insights [client SDK sample visualization](https://tsiclientsample.azurewebsites.net/) to learn how to authenticate with the Time Series Insights APIs programmatically using the [JavaScript Client SDK](https://github.com/microsoft/tsiclient/blob/master/docs/API.md) along with charts and graphs.