acrolinx and broken links

Larry Franks · Larry Franks · commit 1c35584655c8 · 2020-01-15T16:47:56.000-05:00
diff --git a/.openpublishing.redirection.json b/.openpublishing.redirection.json
@@ -170,6 +170,11 @@
       "redirect_url": "/azure/machine-learning/service/how-to-debug-parallel-run-step",
       "redirect_document_id": false
     },
+    {
+      "source_path": "articles/machine-learning/service/how-to-run-batch-predictions.md",
+      "redirect_url": "/azure/machine-learning/service/how-to-use-parallel-run-step",
+      "redirect_document_id": false
+    },
     {
       "source_path": "articles/machine-learning/service/quickstart-run-local-notebook.md",
       "redirect_url": "/azure/machine-learning/service/how-to-configure-environment#local",
diff --git a/articles/machine-learning/azure-machine-learning-release-notes.md b/articles/machine-learning/azure-machine-learning-release-notes.md
@@ -1486,7 +1486,7 @@ Azure Machine Learning Compute can be created in Python, using Azure portal, or
 + ML Pipelines
   + New and updated notebooks for getting started with pipelines, batch scoping,  and style transfer examples: https://aka.ms/aml-pipeline-notebooks
   + Learn how to [create your first pipeline](how-to-create-your-first-pipeline.md)
-  + Learn how to [run batch predictions using pipelines](how-to-run-batch-predictions.md)
+  + Learn how to [run batch predictions using pipelines](how-to-use-parallel-run-step.md)
 + Azure Machine Learning compute target
   + [Sample notebooks](https://aka.ms/aml-notebooks) are now updated to use the new managed compute.
   + [Learn about this compute](how-to-set-up-training-targets.md#amlcompute)
diff --git a/articles/machine-learning/concept-enterprise-security.md b/articles/machine-learning/concept-enterprise-security.md
@@ -330,7 +330,7 @@ Here are the details:
 
 * [Secure Azure Machine Learning web services with SSL](how-to-secure-web-service.md)
 * [Consume a Machine Learning model deployed as a web service](how-to-consume-web-service.md)
-* [How to run batch predictions](how-to-run-batch-predictions.md)
+* [How to run batch predictions](how-to-use-parallel-run-step.md)
 * [Monitor your Azure Machine Learning models with Application Insights](how-to-enable-app-insights.md)
 * [Collect data for models in production](how-to-enable-data-collection.md)
 * [Azure Machine Learning SDK](https://docs.microsoft.com/python/api/overview/azure/ml/intro?view=azure-ml-py)
diff --git a/articles/machine-learning/concept-model-management-and-deployment.md b/articles/machine-learning/concept-model-management-and-deployment.md
@@ -85,7 +85,7 @@ You also provide the configuration of the target deployment platform. For exampl
 When the image is created, components required by Azure Machine Learning are also added. For example, assets needed to run the web service and interact with IoT Edge.
 
 #### Batch scoring
-Batch scoring is supported through ML pipelines. For more information, see [Batch predictions on big data](how-to-run-batch-predictions.md).
+Batch scoring is supported through ML pipelines. For more information, see [Batch predictions on big data](how-to-use-parallel-run-step.md).
 
 #### Real-time web services
 
diff --git a/articles/machine-learning/how-to-access-data.md b/articles/machine-learning/how-to-access-data.md
@@ -340,7 +340,7 @@ Azure Machine Learning provides several ways to use your models for scoring. Som
 
 | Method | Datastore access | Description |
 | ----- | :-----: | ----- |
-| [Batch prediction](how-to-run-batch-predictions.md) | ✔ | Make predictions on large quantities of data asynchronously. |
+| [Batch prediction](how-to-use-parallel-run-step.md) | ✔ | Make predictions on large quantities of data asynchronously. |
 | [Web service](how-to-deploy-and-where.md) | &nbsp; | Deploy models as a web service. |
 | [Azure IoT Edge module](how-to-deploy-and-where.md) | &nbsp; | Deploy models to IoT Edge devices. |
 
diff --git a/articles/machine-learning/how-to-debug-parallel-run-step.md b/articles/machine-learning/how-to-debug-parallel-run-step.md
@@ -77,7 +77,7 @@ def run(mini_batch):
 
 ### How could I pass a side input such as, a file or file(s) containing a lookup table, to all my workers?
 
-Construct a [Dataset](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py) object containing the side input and register with your workspace. After that you can access it in your inference script (for example, in your init() method) as follows:
+Construct a [Dataset](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py) object containing the side input and register with your workspace. After that you can access it in your inference script (for example, in your init() method) as follows:
 
 ```python
 from azureml.core.run import Run
diff --git a/articles/machine-learning/how-to-deploy-and-where.md b/articles/machine-learning/how-to-deploy-and-where.md
@@ -181,7 +181,7 @@ To deploy the model, you need the following items:
     >
     > * The Azure Machine Learning SDK doesn't provide a way for web services or IoT Edge deployments to access your data store or datasets. If your deployed model needs to access data stored outside the deployment, like data in an Azure storage account, you must develop a custom code solution by using the relevant SDK. For example, the [Azure Storage SDK for Python](https://github.com/Azure/azure-storage-python).
     >
-    >   An alternative that might work for your scenario is [batch prediction](how-to-run-batch-predictions.md), which does provide access to data stores during scoring.
+    >   An alternative that might work for your scenario is [batch prediction](how-to-use-parallel-run-step.md), which does provide access to data stores during scoring.
 
 * **Dependencies**, like helper scripts or Python/Conda packages required to run the entry script or model.
 
diff --git a/articles/machine-learning/how-to-deploy-app-service.md b/articles/machine-learning/how-to-deploy-app-service.md
@@ -62,7 +62,7 @@ Before deploying, you must define what is needed to run the model as a web servi
     > [!IMPORTANT]
     > The Azure Machine Learning SDK does not provide a way for the web service access your datastore or data sets. If you need the deployed model to access data stored outside the deployment, such as in an Azure Storage account, you must develop a custom code solution using the relevant SDK. For example, the [Azure Storage SDK for Python](https://github.com/Azure/azure-storage-python).
     >
-    > Another alternative that may work for your scenario is [batch predictions](how-to-run-batch-predictions.md), which does provide access to datastores when scoring.
+    > Another alternative that may work for your scenario is [batch predictions](how-to-use-parallel-run-step.md), which does provide access to datastores when scoring.
 
     For more information on entry scripts, see [Deploy models with Azure Machine Learning](service/how-to-deploy-and-where.md).
 
diff --git a/articles/machine-learning/how-to-deploy-inferencing-gpus.md b/articles/machine-learning/how-to-deploy-inferencing-gpus.md
@@ -20,7 +20,7 @@ This article teaches you how to use Azure Machine Learning to deploy a GPU-enabl
 Inference, or model scoring, is the phase where the deployed model is used to make predictions. Using GPUs instead of CPUs offers performance advantages on highly parallelizable computation.
 
 > [!IMPORTANT]
-> For web service deployments, GPU inference is only supported on Azure Kubernetes Service. For inference using a __machine learning pipeline__, GPUs are only supported on Azure Machine Learning Compute. For more information on using ML pipelines, see [Run batch predictions](how-to-run-batch-predictions.md). 
+> For web service deployments, GPU inference is only supported on Azure Kubernetes Service. For inference using a __machine learning pipeline__, GPUs are only supported on Azure Machine Learning Compute. For more information on using ML pipelines, see [Run batch predictions](how-to-use-parallel-run-step.md). 
 
 > [!TIP]
 > Although the code snippets in this article use a TensorFlow model, you can apply the information to any machine learning framework that supports GPUs.
diff --git a/articles/machine-learning/how-to-run-batch-predictions-designer.md b/articles/machine-learning/how-to-run-batch-predictions-designer.md
@@ -18,7 +18,7 @@ ms.custom: Ignite2019
 
 In this how-to, you learn how to use the designer to train a model and set up a batch prediction pipeline and web service. Batch prediction allows for continuous and on-demand scoring of trained models on large data sets, optionally configured as a web service that can be triggered from any HTTP library. 
 
-For setting up batch scoring services using the SDK, see the accompanying [how-to](how-to-run-batch-predictions.md).
+For setting up batch scoring services using the SDK, see the accompanying [how-to](how-to-use-parallel-run-step.md).
 
 In this how-to, you learn the following tasks:
 
diff --git a/articles/machine-learning/how-to-use-parallel-run-step.md b/articles/machine-learning/how-to-use-parallel-run-step.md
@@ -357,7 +357,7 @@ pipeline_run.wait_for_completion(show_output=True)
 
 To see this process working end to end, try the [batch inference notebook](https://aka.ms/batch-inference-notebooks). 
 
-For debugging and troubleshooting guidance for ParallelRunStep, see the [how-to guide](how-to-debug-parallel-run.md).
+For debugging and troubleshooting guidance for ParallelRunStep, see the [how-to guide](how-to-debug-parallel-run-step.md).
 
 For debugging and troubleshooting guidance for pipelines, see the [how-to guide](how-to-debug-pipelines.md).
 
diff --git a/articles/machine-learning/overview-what-is-azure-ml.md b/articles/machine-learning/overview-what-is-azure-ml.md
@@ -80,7 +80,7 @@ When you have the right model, you can easily use it in a web service, on an IoT
 
 Then you can manage your deployed models by using the [Azure Machine Learning SDK for Python](https://aka.ms/aml-sdk), [Azure Machine Learning studio](https://ml.azure.com), or the [machine learning CLI](reference-azure-machine-learning-cli.md).
 
-These models can be consumed and return predictions in [real time](how-to-consume-web-service.md) or [asynchronously](how-to-run-batch-predictions.md) on large quantities of data.
+These models can be consumed and return predictions in [real time](how-to-consume-web-service.md) or [asynchronously](how-to-use-parallel-run-step.md) on large quantities of data.
 
 And with advanced [machine learning pipelines](concept-ml-pipelines.md), you can collaborate on each step from data preparation, model training and evaluation, through deployment. Pipelines allow you to:
 
diff --git a/articles/machine-learning/toc.yml b/articles/machine-learning/toc.yml
@@ -343,7 +343,7 @@
       href: how-to-retrain-designer.md
     - name: Use parallel run step
       displayName: score scoring batch consume pipeline parallelrunstep inference
-      href: how-to-use-paralle-run-step.md
+      href: how-to-use-parallel-run-step.md
     - name: Debug & troubleshoot parallel run step
       displayName: debug_batch consume pipeline parallelrunstep inference
       href: how-to-debug-parallel-run-step.md
diff --git a/articles/machine-learning/tutorial-deploy-models-with-aml.md b/articles/machine-learning/tutorial-deploy-models-with-aml.md
@@ -386,6 +386,6 @@ service.delete()
 
 + Learn about all of the [deployment options for Azure Machine Learning](how-to-deploy-and-where.md).
 + Learn how to [create clients for the web service](how-to-consume-web-service.md).
-+  [Make predictions on large quantities of data](how-to-run-batch-predictions.md) asynchronously.
++  [Make predictions on large quantities of data](how-to-use-parallel-run-step.md) asynchronously.
 + Monitor your Azure Machine Learning models with [Application Insights](how-to-enable-app-insights.md).
 + Try out the [automatic algorithm selection](tutorial-auto-train-models.md) tutorial. 
diff --git a/includes/aml-compute-target-deploy.md b/includes/aml-compute-target-deploy.md
@@ -17,7 +17,7 @@ ms.date: 08/23/2019
 | [Azure Machine Learning compute instance&nbsp;web&nbsp;service](../articles/machine-learning/how-to-deploy-and-where.md#notebookvm) | Testing/debugging | &nbsp; | &nbsp; | Use for limited testing and troubleshooting.
 | [Azure Kubernetes Service (AKS)](../articles/machine-learning/how-to-deploy-and-where.md#aks) | Real-time inference |  [Yes](../articles/machine-learning/how-to-deploy-inferencing-gpus.md) (web service deployment) | [Yes](../articles/machine-learning/how-to-deploy-fpga-web-service.md)   |Use for high-scale production deployments. Provides fast response time and autoscaling of the deployed service. Cluster autoscaling isn't supported through the Azure Machine Learning SDK. To change the nodes in the AKS cluster, use the UI for your AKS cluster in the Azure portal. AKS is the only option available for the designer. |
 | [Azure Container Instances](../articles/machine-learning/how-to-deploy-and-where.md#aci) | Testing or development | &nbsp;  | &nbsp; | Use for low-scale CPU-based workloads that require less than 48 GB of RAM. |
-| [Azure Machine Learning compute clusters](../articles/machine-learning/how-to-run-batch-predictions.md) | (Preview) Batch&nbsp;inference | [Yes](../articles/machine-learning/how-to-run-batch-predictions.md) (machine learning pipeline) | &nbsp;  | Run batch scoring on serverless compute. Supports normal and low-priority VMs. |
+| [Azure Machine Learning compute clusters](../articles/machine-learning/how-to-use-parallel-run-step.md) | (Preview) Batch&nbsp;inference | [Yes](../articles/machine-learning/how-to-use-parallel-run-step.md) (machine learning pipeline) | &nbsp;  | Run batch scoring on serverless compute. Supports normal and low-priority VMs. |
 | [Azure Functions](../articles/machine-learning/how-to-deploy-functions.md) | (Preview) Real-time inference | &nbsp; | &nbsp; | &nbsp; |
 | [Azure IoT Edge](../articles/machine-learning/how-to-deploy-and-where.md#iotedge) | (Preview) IoT&nbsp;module |  &nbsp; | &nbsp; | Deploy and serve ML models on IoT devices. |
 | [Azure Data Box Edge](../articles/databox-online/data-box-edge-overview.md)   | Via IoT Edge |  &nbsp; | Yes | Deploy and serve ML models on IoT devices. |

Original file line number	Diff line number	Diff line change
`@@ -181,7 +181,7 @@ To deploy the model, you need the following items:`
`181`	`181`	`>`
`182`	`182`	`> * The Azure Machine Learning SDK doesn't provide a way for web services or IoT Edge deployments to access your data store or datasets. If your deployed model needs to access data stored outside the deployment, like data in an Azure storage account, you must develop a custom code solution by using the relevant SDK. For example, the [Azure Storage SDK for Python](https://github.com/Azure/azure-storage-python).`
`183`	`183`	`>`
`184`		`- > An alternative that might work for your scenario is [batch prediction](how-to-run-batch-predictions.md), which does provide access to data stores during scoring.`
	`184`	`+ > An alternative that might work for your scenario is [batch prediction](how-to-use-parallel-run-step.md), which does provide access to data stores during scoring.`
`185`	`185`
`186`	`186`	`* Dependencies, like helper scripts or Python/Conda packages required to run the entry script or model.`
`187`	`187`
Original file line number	Diff line number	Diff line change
`@@ -62,7 +62,7 @@ Before deploying, you must define what is needed to run the model as a web servi`
`62`	`62`	`> [!IMPORTANT]`
`63`	`63`	`> The Azure Machine Learning SDK does not provide a way for the web service access your datastore or data sets. If you need the deployed model to access data stored outside the deployment, such as in an Azure Storage account, you must develop a custom code solution using the relevant SDK. For example, the [Azure Storage SDK for Python](https://github.com/Azure/azure-storage-python).`
`64`	`64`	`>`
`65`		`- > Another alternative that may work for your scenario is [batch predictions](how-to-run-batch-predictions.md), which does provide access to datastores when scoring.`
	`65`	`+ > Another alternative that may work for your scenario is [batch predictions](how-to-use-parallel-run-step.md), which does provide access to datastores when scoring.`
`66`	`66`
`67`	`67`	`For more information on entry scripts, see [Deploy models with Azure Machine Learning](service/how-to-deploy-and-where.md).`
`68`	`68`