Merge pull request #114331 from j-martens/master

Fixing file paths to avoid redirection

Author: Jak-MS

.openpublishing.redirection.json

@@ -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/update-machine-learning-models.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/data-factory/v1/data-factory-azure-ml-update-resource-activity.md

@@ -30,7 +30,7 @@ A Field Programmable Gate Array (FPGA) is included on every Azure Stack Edge dev
 
 | Specification           | Value                  |
 |-------------------------|----------------------------|
-| FPGA   | Intel Arria 10 <br> Available Deep Neural Network (DNN) models are the same as those [supported by cloud FPGA instances](https://docs.microsoft.com/azure/machine-learning/service/how-to-deploy-fpga-web-service#whats-supported-on-azure).| 
+| FPGA   | Intel Arria 10 <br> Available Deep Neural Network (DNN) models are the same as those [supported by cloud FPGA instances](https://docs.microsoft.com/azure/machine-learning/how-to-deploy-fpga-web-service#whats-supported-on-azure).| 
 
 
 ## Power supply unit specifications

articles/databox-online/azure-stack-edge-technical-specifications-compliance.md

@@ -263,5 +263,5 @@ The data object has the following properties for each event type:
 
 * For an introduction to Azure Event Grid, see [What is Event Grid?](overview.md)
 * For more information about creating an Azure Event Grid subscription, see [Event Grid subscription schema](subscription-creation-schema.md)
-* For an introduction to using Azure Event Grid with Azure Machine Learning, see [Consume Azure Machine Learning events](/azure/machine-learning/service/concept-event-grid-integration)
-* For an example of using Azure Event Grid with Azure Machine Learning, see [Create event driven machine learning workflows](/azure/machine-learning/service/how-to-use-event-grid)
+* For an introduction to using Azure Event Grid with Azure Machine Learning, see [Consume Azure Machine Learning events](/azure/machine-learning/concept-event-grid-integration)
+* For an example of using Azure Event Grid with Azure Machine Learning, see [Create event driven machine learning workflows](/azure/machine-learning/how-to-use-event-grid)

articles/event-grid/event-schema-machine-learning.md

@@ -580,7 +580,7 @@ See the [package website](https://azure.github.io/azureml-sdk-for-r) for complet
 
 ### Azure Machine Learning integration with Event Grid 
 
-Azure Machine Learning is now a resource provider for Event Grid, you can configure machine learning events through the Azure portal or Azure CLI. Users can create events for run completion, model registration, model deployment and data drift detected. These events can be routed to event handlers supported by Event Grid for consumption. See machine learning event [schema](https://docs.microsoft.com/azure/event-grid/event-schema-machine-learning), [concepts](https://docs.microsoft.com/azure/machine-learning/concept-event-grid-integration) and [tutorial](https://docs.microsoft.com/azure/machine-learning/how-to-use-event-grid) articles for more details.
+Azure Machine Learning is now a resource provider for Event Grid, you can configure machine learning events through the Azure portal or Azure CLI. Users can create events for run completion, model registration, model deployment and data drift detected. These events can be routed to event handlers supported by Event Grid for consumption. See machine learning event [schema](https://docs.microsoft.com/azure/event-grid/event-schema-machine-learning) and [tutorial](how-to-use-event-grid.md) articles for more details.
 
 ## 2019-10-31
 

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

@@ -19,7 +19,7 @@
     items:
     - name: Service
       tocHref: /azure/open-datasets/
-      topicHref: /azure/machine-learning/service/index
+      topicHref: /azure/machine-learning/index
       items: 
       - name: Open Datasets
         tocHref: /azure/open-datasets/
@@ -35,7 +35,7 @@
     items:
     - name: Service
       tocHref: /azure/iot-edge/
-      topicHref: /azure/machine-learning/service/index
+      topicHref: /azure/machine-learning/index
       items:
       - name: IoT Edge
         tocHref: /azure/iot-edge/
@@ -51,7 +51,7 @@
     items:
     - name: Service
       tocHref: /azure/architecture/data-guide/technology-choices/
-      topicHref: /azure/machine-learning/service/index
+      topicHref: /azure/machine-learning/index
       items:
       - name: Data Architecture Guide
         tocHref: /azure/architecture/data-guide/
@@ -67,7 +67,7 @@
     items:
     - name: Service
       tocHref: /azure/devops/pipelines/
-      topicHref: /azure/machine-learning/service/index
+      topicHref: /azure/machine-learning/index
       items:
       - name: Pipelines
         tocHref: /azure/devops/pipelines/languages/

articles/machine-learning/breadcrumb/toc.yml

@@ -148,7 +148,7 @@ Azure ML gives you the capability to track the end-to-end audit trail of all of
 - [Interpretability](how-to-machine-learning-interpretability.md) allows you to explain your models, meet regulatory compliance, and understand how models arrive at a result for given input.
 - Azure ML Run history stores a snapshot of the code, data, and computes used to train a model.
 - The Azure ML Model Registry captures all of the metadata associated with your model (which experiment trained it, where it is being deployed, if its deployments are healthy).
-- [Integration with Azure Event Grid](concept-event-grid-integration.md) allows you to act on events in the ML lifecycle. For example, model registration, deployment, data drift, and training (run) events.
+- [Integration with Azure](how-to-use-event-grid.md)  allows you to act on events in the ML lifecycle. For example, model registration, deployment, data drift, and training (run) events.
 
 > [!TIP]
 > While some information on models and datasets is automatically captured, you can add additional information by using __tags__. When looking for registered models and datasets in your workspace, you can use tags as a filter.

articles/machine-learning/concept-event-grid-integration.md

@@ -145,6 +145,6 @@ You can also use the [Permutation Feature Importance module](https://docs.micros
 
 ## Next steps
 
- - [Learn more about Azure Machine Learning designer](https://docs.microsoft.com/azure/machine-learning/service/concept-designer?WT.mc_id=docs-article-lazzeri)
+ - [Learn more about Azure Machine Learning designer](https://docs.microsoft.com/azure/machine-learning/concept-designer?WT.mc_id=docs-article-lazzeri)
  - For descriptions of all the machine learning algorithms available in Azure Machine Learning designer, see [Machine Learning designer algorithm and module reference](https://docs.microsoft.com/azure/machine-learning/algorithm-module-reference/module-reference?WT.mc_id=docs-article-lazzeri)
- - To explore the relationship between deep learning, machine learning, and AI, see [Deep Learning vs. Machine Learning](https://docs.microsoft.com/azure/machine-learning/service/concept-deep-learning-vs-machine-learning?WT.mc_id=docs-article-lazzeri)
+ - To explore the relationship between deep learning, machine learning, and AI, see [Deep Learning vs. Machine Learning](https://docs.microsoft.com/azure/machine-learning/concept-deep-learning-vs-machine-learning?WT.mc_id=docs-article-lazzeri)