fix blocking issues

Author: sdgilley

articles/machine-learning/how-to-r-azure-ml-deploy-r-model.md renamed to articles/machine-learning/how-to-r-deploy-r-model.md

@@ -21,9 +21,9 @@ In this article, you'll learn how to deploy an R model to a managed endpoint (We
 
 - An [Azure Machine Learning workspace](quickstart-create-resources.md).
 - Azure [CLI and ml extension installed](how-to-configure-cli.md).  Or use a [compute instance in your workspace](quickstart-create-resources.md), which has the CLI pre-installed.
-- At least one custom environment associated with your workspace. Create [an R environment](how-to-r-azure-ml-modify-script-for-production.md#create-an-environment), or any other custom environment if you don't have one.
+- At least one custom environment associated with your workspace. Create [an R environment](how-to-r-modify-script-for-production.md#create-an-environment), or any other custom environment if you don't have one.
 - An understanding of the [R `plumber` package](https://www.rplumber.io/index.html)
-- A model that you've trained and [packaged with `crate`](how-to-r-azure-ml-modify-script-for-production.md#crate-your-models-with-the-carrier-package), and [registered into your workspace](how-to-r-azure-ml-train-model.md#register-model)
+- A model that you've trained and [packaged with `crate`](how-to-r-modify-script-for-production.md#crate-your-models-with-the-carrier-package), and [registered into your workspace](how-to-r-train-model.md#register-model)
 
 ## Create a folder with this structure
 
@@ -177,7 +177,7 @@ These steps assume you have an Azure Container Registry associated with your wor
 1. On the left navigation, select **Environments**.
 1. On the top, select **Custom environments**.
 1. If you see custom environments, nothing more is needed.
-1. If you don't see any custom environments, create [an R environment](how-to-r-azure-ml-modify-script-for-production.md#create-an-environment), or any other custom environment.  (You *won't* use this environment for deployment, but you *will* use the container registry that is also created for you.)
+1. If you don't see any custom environments, create [an R environment](how-to-r-modify-script-for-production.md#create-an-environment), or any other custom environment.  (You *won't* use this environment for deployment, but you *will* use the container registry that is also created for you.)
 
 Once you have verified that you have at least one custom environment, use the following steps to build a container.
 
@@ -201,7 +201,7 @@ Once you have verified that you have at least one custom environment, use the fo
     az account set --subscription "<SUBSCRIPTION-NAME>"
     ```
 
-1. Set the default workspace.  If you're doing this from a compute instance, you can use the following command as is.  If you're on any other computer, substitute your resource group and workspace name instead.  (You can find these values in [Azure Machine Learning studio](how-to-r-azure-ml-train-model.md#submit-the-job).)
+1. Set the default workspace.  If you're doing this from a compute instance, you can use the following command as is.  If you're on any other computer, substitute your resource group and workspace name instead.  (You can find these values in [Azure Machine Learning studio](how-to-r-train-model.md#submit-the-job).)
 
     ```azurecli
     az configure --defaults group=$CI_RESOURCE_GROUP workspace=$CI_WORKSPACE
@@ -326,7 +326,7 @@ Enter the following json into the **Input data to rest real-time endpoint** text
 
 Select **Test**. You should see the following output:
 
-:::image type="content" source="media/how-to-r-azure-ml-deploy-an-r-model/test-deployment.png" alt-text="Screenshot shows results from testing a model." lightbox="media/how-to-r-azure-ml-deploy-an-r-model/test-deployment.png":::
+:::image type="content" source="media/how-to-r-deploy-an-r-model/test-deployment.png" alt-text="Screenshot shows results from testing a model." lightbox="media/how-to-r-deploy-an-r-model/test-deployment.png":::
 
 # [Azure CLI](#tab/cli)
 
@@ -361,4 +361,4 @@ az ml online-endpoint delete --name r-endpoint-forecast
 
 ## Next steps
 
-For more information about using R with Azure Machine Learning, see [Overview of R capabilities in Azure Machine Learning](how-to-r-azure-ml-overview-r-capabilities.md)
+For more information about using R with Azure Machine Learning, see [Overview of R capabilities in Azure Machine Learning](how-to-r-overview-r-capabilities.md)

articles/machine-learning/how-to-r-azure-ml-interactive-development.md renamed to articles/machine-learning/how-to-r-interactive-development.md

@@ -33,7 +33,7 @@ Many R users also use RStudio, a popular IDE. You can install RStudio or Posit W
 You'll use a notebook in your Azure Machine Learning workspace, on a compute instance.  
 
 1. Sign in to [Azure Machine Learning studio](https://ml.azure.com)
-1. Open your workspace if isn't already open
+1. Open your workspace if it isn't already open
 1. On the left navigation, select **Notebooks**
 1. Create a new notebook, named **RunR.ipynb**
 
@@ -44,7 +44,7 @@ You'll use a notebook in your Azure Machine Learning workspace, on a compute ins
 1. On the notebook toolbar, make sure your compute instance is running.  If not, start it now.
 1. On the notebook toolbar, switch the kernel to **R**.
 
-    :::image type="content" source="media/how-to-r-azure-ml-interactive-development/r-kernel.png" alt-text="Screenshot: Switch the notebook kernel to use R." lightbox="media/how-to-r-azure-ml-interactive-development/r-kernel.png":::
+    :::image type="content" source="media/how-to-r-interactive-development/r-kernel.png" alt-text="Screenshot: Switch the notebook kernel to use R." lightbox="media/how-to-r-interactive-development/r-kernel.png":::
 
 Your notebook is now ready for you to run R commands.
 
@@ -156,4 +156,4 @@ Other than the above issues, use R as you would in any other environment, such a
 
 ## Next steps
 
-* [Adapt your R script to run in production](how-to-r-azure-ml-modify-script-for-production.md)
+* [Adapt your R script to run in production](how-to-r-modify-script-for-production.md)

articles/machine-learning/how-to-r-azure-ml-modify-script-for-production.md renamed to articles/machine-learning/how-to-r-modify-script-for-production.md

@@ -302,4 +302,4 @@ Some additional suggestions you may want to consider:
 
 ## Next steps
 
-* [How to train R models in Azure Machine Learning](how-to-r-azure-ml-train-model.md)
+* [How to train R models in Azure Machine Learning](how-to-r-train-model.md)

articles/machine-learning/how-to-r-azure-ml-overview-r-capabilities.md renamed to articles/machine-learning/how-to-r-overview-r-capabilities.md

@@ -23,27 +23,27 @@ This article outlines the key scenarios for R that are supported in Azure Machin
 
 A typical workflow for using R with Azure Machine Learning:
 
-- [Develop R scripts interactively](how-to-r-azure-ml-interactive-development.md) using Jupyter Notebooks on a compute instance.  (While you can also add Posit or RStudio to a compute instance, you can't currently access data assets in the workspace from these applications on the compute instance. So for now, interactive work is best done in a Jupyter notebook.)
+- [Develop R scripts interactively](how-to-r-interactive-development.md) using Jupyter Notebooks on a compute instance.  (While you can also add Posit or RStudio to a compute instance, you can't currently access data assets in the workspace from these applications on the compute instance. So for now, interactive work is best done in a Jupyter notebook.)
 
     - Read tabular data from a registered data asset or datastore
     - Install additional R libraries
     - Save artifacts to the workspace file storage
 
-- [Adapt your script](how-to-r-azure-ml-modify-script-for-production.md) to run as a production job in Azure Machine Learning
+- [Adapt your script](how-to-r-modify-script-for-production.md) to run as a production job in Azure Machine Learning
 
     - Remove any code that may require user interaction
     - Add command line input parameters to the script as necessary
     - Include and source the `azureml_utils.R` script in the same working directory of the R script to be executed
     - Use `crate` to package the model
     - Include the R/MLflow functions in the script to **log** artifacts, models, parameters, and/or tags to the job on MLflow
 
-- [Submit remote asynchronous R jobs](how-to-r-azure-ml-train-model.md) (you submit jobs via the CLI or Python SDK, not R)
+- [Submit remote asynchronous R jobs](how-to-r-train-model.md) (you submit jobs via the CLI or Python SDK, not R)
 
     - Build an environment
     - Log job artifacts, parameters, tags and models
 
-- [Register your model](how-to-r-azure-ml-train-model.md#register-model) using Azure Machine Learning studio
-- [Deploy registered R models](how-to-r-azure-ml-deploy-r-model.md) to managed online endpoints
+- [Register your model](how-to-r-train-model.md#register-model) using Azure Machine Learning studio
+- [Deploy registered R models](how-to-r-deploy-r-model.md) to managed online endpoints
     - Use the deployed endpoints for real-time inferencing/scoring
 
 ## Known limitations
@@ -59,15 +59,15 @@ A typical workflow for using R with Azure Machine Learning:
 | Programmatic model registering/recording from a running job with R isn't supported. |  |
 | Zero code deployment (that is, automatic deployment) of an R MLflow model is currently not supported. | Create a custom container with `plumber` for deployment. |
 | Scoring an R model with batch endpoints isn't supported. |  |
-| AzureML online deployment yml can only use image URIs directly from the registry for the environment specification; not pre-built environments from the same Dockerfile. | Follow the steps in [How to deploy a registered R model to an online (real time) endpoint](how-to-r-azure-ml-deploy-r-model.md) for the correct way to deploy. |
+| AzureML online deployment yml can only use image URIs directly from the registry for the environment specification; not pre-built environments from the same Dockerfile. | Follow the steps in [How to deploy a registered R model to an online (real time) endpoint](how-to-r-deploy-r-model.md) for the correct way to deploy. |
 
 
 
 ## Next steps
 
 Learn more about R in Azure Machine Learning:
 
-* [Interactive R development](how-to-r-azure-ml-interactive-development.md)
-* [Adapt your R script to run in production](how-to-r-azure-ml-modify-script-for-production.md)
-* [How to train R models in Azure Machine Learning](how-to-r-azure-ml-train-model.md)
-* [How to deploy an R model to an online (real time) endpoint](how-to-r-azure-ml-deploy-r-model.md)
+* [Interactive R development](how-to-r-interactive-development.md)
+* [Adapt your R script to run in production](how-to-r-modify-script-for-production.md)
+* [How to train R models in Azure Machine Learning](how-to-r-train-model.md)
+* [How to deploy an R model to an online (real time) endpoint](how-to-r-deploy-r-model.md)

articles/machine-learning/how-to-r-azure-ml-train-model.md renamed to articles/machine-learning/how-to-r-train-model.md

@@ -15,7 +15,7 @@ ms.devlang: r
 
 [!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
 
-This article explains how to take the R script that you [adapted to run in production](how-to-r-azure-ml-modify-script-for-production.md) and set it up to run as an R job using the AzureML CLI V2.
+This article explains how to take the R script that you [adapted to run in production](how-to-r-modify-script-for-production.md) and set it up to run as an R job using the AzureML CLI V2.
 
 > [!NOTE]
 > Although the title of this article refers to _training_ a model, you can actually run any kind of R script as long as it meets the requirements listed in the adapting article.
@@ -26,7 +26,7 @@ This article explains how to take the R script that you [adapted to run in produ
 - [A registered data asset](how-to-create-data-assets.md) that your training job will use.
 - Azure [CLI and ml extension installed](how-to-configure-cli.md).  Or use a [compute instance in your workspace](quickstart-create-resources.md), which has the CLI pre-installed.
 - [A compute cluster](how-to-create-attach-compute-cluster.md) or [compute instance](quickstart-create-resources.md#create-compute-instance) to run your training job.
-- [An R environment](how-to-r-azure-ml-modify-script-for-production.md#create-an-environment) for the compute cluster to use to run the job.
+- [An R environment](how-to-r-modify-script-for-production.md#create-an-environment) for the compute cluster to use to run the job.
 
 ## Create a folder with this structure
 
@@ -44,7 +44,7 @@ Create this folder structure for your project:
 > All source code goes in the `src` directory.
 
 * The **r-source.R** file is the R script that you adapted to run in production
-* The **azureml_utils.R** file is necessary. The source code is shown [here](how-to-r-azure-ml-modify-script-for-production.md#source-the-azureml_utilsr-helper-script)
+* The **azureml_utils.R** file is necessary. The source code is shown [here](how-to-r-modify-script-for-production.md#source-the-azureml_utilsr-helper-script)
 
 
 
@@ -103,7 +103,7 @@ Find these values from [Azure Machine Learning studio](https://ml.azure.com):
 1. In the upper right Azure Machine Learning studio toolbar, select your workspace name.
 1. You can copy the values from the section that appears.  
 
-:::image type="content" source="media/how-to-r-azure-ml-train-model/find-values.png" alt-text="Screenshot: Find the values to use in your CLI command." lightbox="media/how-to-r-azure-ml-train-model/find-values.png":::
+:::image type="content" source="media/how-to-r-train-model/find-values.png" alt-text="Screenshot: Find the values to use in your CLI command." lightbox="media/how-to-r-train-model/find-values.png":::
 
 To submit the job, run the following commands in a terminal window:
 
@@ -165,4 +165,4 @@ You'll see a confirmation that the model is registered.
 
 ## Next steps
 
-Now that you have a registered model, learn [How to deploy an R model to an online (real time) endpoint](how-to-r-azure-ml-deploy-r-model.md).
+Now that you have a registered model, learn [How to deploy an R model to an online (real time) endpoint](how-to-r-deploy-r-model.md).

articles/machine-learning/index.yml

@@ -73,7 +73,7 @@ landingContent:
           - text: Use designer (drag-n-drop) in studio
             url: tutorial-designer-automobile-price-train-score.md
           - text: Train with R
-            url: how-to-r-azure-ml-train-model.md
+            url: how-to-r-train-model.md
   # Card
   - title: Deploy models
     linkLists:
@@ -86,7 +86,7 @@ landingContent:
         - text: Batch scoring with batch endpoints 
           url: batch-inference/how-to-use-batch-endpoint.md
         - text: Deploy R models
-          url: how-to-r-azure-ml-deploy-r-model.md
+          url: how-to-r-deploy-r-model.md
 
   # Card
   - title: "Manage the ML lifecycle (MLOps)"

articles/machine-learning/media/how-to-r-azure-ml-deploy-an-r-model/test-deployment.png renamed to articles/machine-learning/media/how-to-r-azure-machine-learning-deploy-an-r-model/test-deployment.png

@@ -773,15 +773,15 @@
     - name: Use R with Azure ML
       items:
         - name: Bring R workloads
-          href: how-to-r-azure-ml-overview-r-capabilities.md
+          href: how-to-r-overview-r-capabilities.md
         - name: Interactive R development
-          href: how-to-r-azure-ml-interactive-development.md
+          href: how-to-r-interactive-development.md
         - name: Adapt R scripts for production
-          href: how-to-r-azure-ml-modify-script-for-production.md
+          href: how-to-r-modify-script-for-production.md
         - name: Train R model
-          href: how-to-r-azure-ml-train-model.md
+          href: how-to-r-train-model.md
         - name: Deploy R model
-          href: how-to-r-azure-ml-deploy-r-model.md
+          href: how-to-r-deploy-r-model.md
 - name: Troubleshoot & debug
   items:
     - name: Troubleshoot secure workspace connectivity