broken links

Author: j-martens

.openpublishing.redirection.json

@@ -399,7 +399,7 @@
     {
       "source_path": "articles/machine-learning/service/setup-create-workspace.md",
       "redirect_url": "/azure/machine-learning/how-to-manage-workspace",
-      "redirect_document_id": true
+      "redirect_document_id": false
     },
     {
       "source_path": "articles/machine-learning/service/concept-accelerate-with-fpgas.md",
@@ -424,7 +424,7 @@
     {
       "source_path": "articles/machine-learning/service/quickstart-run-cloud-notebook.md",
       "redirect_url": "/azure/machine-learning/tutorial-1st-experiment-sdk-setup",
-      "redirect_document_id": true
+      "redirect_document_id": false
     },
     {
       "source_path": "articles/machine-learning/service/ui-quickstart-run-experiment.md",
@@ -599,7 +599,7 @@
     {
       "source_path": "articles/open-datasets/tutorial-opendatasets-automl.md",
       "redirect_url": "/azure/machine-learning/tutorial-auto-train-models",
-      "redirect_document_id": true
+      "redirect_document_id": false
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     {
       "source_path": "articles/virtual-machines/windows/high-performance-computing.md",

articles/data-factory/update-machine-learning-models.md

@@ -13,26 +13,26 @@ ms.topic: conceptual
 ms.date: 01/16/2018
 ---
 
-# Update Azure Machine Learning models by using Update Resource activity
+# Update ML Studio (classic)v models by using Update Resource activity
 
 [!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
-This article complements the main Azure Data Factory - Azure Machine Learning integration article: [Create predictive pipelines using Azure Machine Learning and Azure Data Factory](transform-data-using-machine-learning.md). If you haven't already done so, review the main article before reading through this article.
+This article complements the main Azure Data Factory - ML Studio (classic)integration article: [Create predictive pipelines using Azure Machine Learning and Azure Data Factory](transform-data-using-machine-learning.md). If you haven't already done so, review the main article before reading through this article.
 
 ## Overview
-As part of the process of operationalizing Azure Machine Learning models, your model is trained and saved. You then use it to create a predictive Web service. The Web service can then be consumed in web sites, dashboards, and mobile apps.
+As part of the process of operationalizing ML Studio (classic) models, your model is trained and saved. You then use it to create a predictive Web service. The Web service can then be consumed in web sites, dashboards, and mobile apps.
 
-Models you create using Machine Learning are typically not static. As new data becomes available or when the consumer of the API has their own data the model needs to be retrained. Refer to [Retrain a Machine Learning Model](../machine-learning/machine-learning-retrain-machine-learning-model.md) for details about how you can retrain a model in Azure Machine Learning.
+Models you create using Machine Learning are typically not static. As new data becomes available or when the consumer of the API has their own data the model needs to be retrained. 
 
 Retraining may occur frequently. With Batch Execution activity and Update Resource activity, you can operationalize the Azure Machine Learning model retraining and updating the predictive Web Service using Data Factory.
 
 The following picture depicts the relationship between training and predictive Web Services.
 
 ![Web services](./media/update-machine-learning-models/web-services.png)
 
-## Azure Machine Learning update resource activity
+## ML Studio (classic) update resource activity
 
-The following JSON snippet defines an Azure Machine Learning Batch Execution activity.
+The following JSON snippet defines an ML Studio (classic) Batch Execution activity.
 
 ```json
 {

articles/data-factory/v1/data-factory-azure-ml-update-resource-activity.md

@@ -39,7 +39,7 @@ Over time, the predictive models in the Azure ML scoring experiments need to be
 1. Create an experiment in [Azure Machine Learning Studio (classic)](https://studio.azureml.net).
 2. When you are satisfied with the model, use Azure Machine Learning Studio (classic) to publish web services for both the **training experiment** and scoring/**predictive experiment**.
 
-The following table describes the web services used in this example.  See [Retrain Machine Learning models programmatically](../../machine-learning/machine-learning-retrain-models-programmatically.md) for details.
+The following table describes the web services used in this example.  See [Retrain Machine Learning Studio (classic) models programmatically](../../machine-learning/studio/retrain-machine-learning-model.md) for details.
 
 - **Training web service** - Receives training data and produces trained models. The output of the retraining is an .ilearner file in an Azure Blob storage. The **default endpoint** is automatically created for you when you publish the training experiment as a web service. You can create more endpoints but the example uses only the default endpoint.
 - **Scoring web service** - Receives unlabeled data examples and makes predictions. The output of prediction could have various forms, such as a .csv file or rows in an Azure SQL database, depending on the configuration of the experiment. The default endpoint is automatically created for you when you publish the predictive experiment as a web service. 

articles/machine-learning/studio/sample-experiments.md

@@ -55,5 +55,4 @@ A new experiment is created in your Machine Learning Studio (classic) workspace
 
 ## Next steps
 * [Import data from various sources](import-data.md)
-* [Quickstart tutorial for the R language in Machine Learning](r-quickstart.md)
 * [Deploy a Machine Learning web service](deploy-a-machine-learning-web-service.md)

articles/stream-analytics/stream-analytics-scale-with-machine-learning-functions.md

@@ -53,7 +53,7 @@ In general, ***B*** for batch size, ***L*** for the web service latency at batch
 
 You can also configure the 'max concurrent calls' on the Machine Learning web service. It's recommended to set this parameter to the maximum value (200 currently).
 
-For more information on this setting, review the [Scaling article for Machine Learning Web Services](../machine-learning/studio/scaling-webservice.md).
+For more information on this setting, review the [Scaling article for Machine Learning Web Services](../machine-learning/studio/create-endpoint.md).
 
 ## Example – Sentiment Analysis
 The following example includes a Stream Analytics job with the sentiment analysis Machine Learning function, as described in the [Stream Analytics Machine Learning integration tutorial](stream-analytics-machine-learning-integration-tutorial.md).