Merge pull request #98822 from craigcaseyMSFT/vcraic1212

megvanhuygen · web-flow · commit ae64e92625ef · 2019-12-13T09:57:00.000-08:00
User Story 1624627 - Rebranding for Azure Machine Learning
diff --git a/articles/machine-learning/algorithm-module-reference/apply-math-operation.md b/articles/machine-learning/algorithm-module-reference/apply-math-operation.md
@@ -1,7 +1,7 @@
 ---
 title:  "Apply Math Operation"
-titleSuffix: Azure Machine Learning service
-description: Learn how to use the Apply Math Operation module in Azure Machine Learning service to apply a mathematical operation to column values in a pipeline.
+titleSuffix: Azure Machine Learning
+description: Learn how to use the Apply Math Operation module in Azure Machine Learning to apply a mathematical operation to column values in a pipeline.
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
@@ -586,4 +586,4 @@ Alternatively, use one of the following modules to do all the calculations at on
   
 ## Next steps
 
-See the [set of modules available](module-reference.md) to Azure Machine Learning service. 
+See the [set of modules available](module-reference.md) to Azure Machine Learning. 
diff --git a/articles/machine-learning/algorithm-module-reference/apply-sql-transformation.md b/articles/machine-learning/algorithm-module-reference/apply-sql-transformation.md
@@ -1,7 +1,7 @@
 ---
 title:  "Apply SQL Transformation"
-titleSuffix: Azure Machine Learning service
-description: Learn how to use the Apply SQL Transformation module in Azure Machine Learning service to run a SQLite query on input datasets to transform the data.
+titleSuffix: Azure Machine Learning
+description: Learn how to use the Apply SQL Transformation module in Azure Machine Learning to run a SQLite query on input datasets to transform the data.
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
@@ -60,4 +60,4 @@ In addition to the list of non-supported functions provided on the official SQLi
     
 ## Next steps
 
-See the [set of modules available](module-reference.md) to Azure Machine Learning service. 
+See the [set of modules available](module-reference.md) to Azure Machine Learning. 
diff --git a/articles/machine-learning/algorithm-module-reference/clip-values.md b/articles/machine-learning/algorithm-module-reference/clip-values.md
@@ -1,7 +1,7 @@
 ---
 title:  "Clip Values"
-titleSuffix: Azure Machine Learning service
-description: Learn how to use the Clip Values module in Azure Machine Learning service to detect outliers and clip or replace their values.
+titleSuffix: Azure Machine Learning
+description: Learn how to use the Clip Values module in Azure Machine Learning to detect outliers and clip or replace their values.
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
@@ -116,4 +116,4 @@ Now try the same pipeline using 60 as the upper percentile threshold and 30 as t
  
 ## Next steps
 
-See the [set of modules available](module-reference.md) to Azure Machine Learning service. 
+See the [set of modules available](module-reference.md) to Azure Machine Learning. 
diff --git a/articles/machine-learning/algorithm-module-reference/convert-to-dataset.md b/articles/machine-learning/algorithm-module-reference/convert-to-dataset.md
@@ -1,7 +1,7 @@
 ---
 title: "Convert to Dataset: Module reference"
-titleSuffix: Azure Machine Learning service
-description: Learn how to use the Convert to Dataset module in the Azure Machine Learning service to convert data input to the internal dataset format used by Microsoft Azure Machine Learning.
+titleSuffix: Azure Machine Learning
+description: Learn how to use the Convert to Dataset module in Azure Machine Learning to convert data input to the internal dataset format used by Microsoft Azure Machine Learning.
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
@@ -66,4 +66,4 @@ We recommend that you use the [Edit Metadata](edit-metadata.md) module to prepar
   
 ## Next steps
 
-See the [set of modules available](module-reference.md) to the Azure Machine Learning service. 
+See the [set of modules available](module-reference.md) to Azure Machine Learning. 
diff --git a/articles/machine-learning/algorithm-module-reference/cross-validate-model.md b/articles/machine-learning/algorithm-module-reference/cross-validate-model.md
@@ -1,7 +1,7 @@
 ---
 title: "Cross Validate Model: Module reference"
-titleSuffix: Azure Machine Learning service
-description: Learn how to use the Cross-Validate Model module in the Azure Machine Learning service to cross-validate parameter estimates for classification or regression models by partitioning the data.
+titleSuffix: Azure Machine Learning
+description: Learn how to use the Cross-Validate Model module in Azure Machine Learning to cross-validate parameter estimates for classification or regression models by partitioning the data.
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
@@ -122,5 +122,5 @@ The module also includes the following metrics for each fold, depending on the t
 
 ## Next steps
 
-See the [set of modules available](module-reference.md) to the Azure Machine Learning service. 
+See the [set of modules available](module-reference.md) to Azure Machine Learning. 
 
diff --git a/articles/machine-learning/algorithm-module-reference/evaluate-recommender.md b/articles/machine-learning/algorithm-module-reference/evaluate-recommender.md
@@ -1,7 +1,7 @@
 ---
 title: "Evaluate Recommender: Module reference"
-titleSuffix: Azure Machine Learning service
-description: Learn how to use the Evaluate Recommender module in the Azure Machine Learning service to evaluate the accuracy of recommender model predictions.
+titleSuffix: Azure Machine Learning
+description: Learn how to use the Evaluate Recommender module in Azure Machine Learning to evaluate the accuracy of recommender model predictions.
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
@@ -85,4 +85,4 @@ Because it's impossible to know the actual "ground truth" for the recommended it
 
 ## Next steps
 
-See the [set of modules available](module-reference.md) to the Azure Machine Learning service. 
+See the [set of modules available](module-reference.md) to Azure Machine Learning. 
diff --git a/articles/machine-learning/algorithm-module-reference/filter-based-feature-selection.md b/articles/machine-learning/algorithm-module-reference/filter-based-feature-selection.md
@@ -1,7 +1,7 @@
 ---
 title: "Filter Based Feature Selection: Module reference"
-titleSuffix: Azure Machine Learning service
-description: Learn how to use the Filter Based Feature Selection module in the Azure Machine Learning service to identify the features in a dataset with the greatest predictive power.
+titleSuffix: Azure Machine Learning
+description: Learn how to use the Filter Based Feature Selection module in Azure Machine Learning to identify the features in a dataset with the greatest predictive power.
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
@@ -130,5 +130,5 @@ If you use Pearson correlation on a numeric feature and a categorical label, the
 
 ## Next steps
 
-See the [set of modules available](module-reference.md) to the Azure Machine Learning service. 
+See the [set of modules available](module-reference.md) to Azure Machine Learning. 
 
diff --git a/articles/machine-learning/algorithm-module-reference/one-vs-all-multiclass.md b/articles/machine-learning/algorithm-module-reference/one-vs-all-multiclass.md
@@ -1,7 +1,7 @@
 ---
 title: "One-vs-All Multiclass"
-titleSuffix: Azure Machine Learning service
-description: Learn how to use the One-vs-All Multiclass module in the Azure Machine Learning service to create a multiclass classification model from an ensemble of binary classification models.
+titleSuffix: Azure Machine Learning
+description: Learn how to use the One-vs-All Multiclass module in Azure Machine Learning to create a multiclass classification model from an ensemble of binary classification models.
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
@@ -56,4 +56,4 @@ Alternatively, you can pass the untrained classifier to [Cross-Validate Model](c
 
 ## Next steps
 
-See the [set of modules available](module-reference.md) to the Azure Machine Learning service. 
+See the [set of modules available](module-reference.md) to Azure Machine Learning. 
diff --git a/articles/machine-learning/algorithm-module-reference/permutation-feature-importance.md b/articles/machine-learning/algorithm-module-reference/permutation-feature-importance.md
@@ -1,7 +1,7 @@
 ---
 title: "Permutation Feature Importance: Module reference"
-titleSuffix: Azure Machine Learning service
-description: Learn how to use the Permutation Feature Importance module in the Azure Machine Learning service to compute the permutation feature importance scores of feature variables, given a trained model and a test dataset.
+titleSuffix: Azure Machine Learning
+description: Learn how to use the Permutation Feature Importance module in Azure Machine Learning to compute the permutation feature importance scores of feature variables, given a trained model and a test dataset.
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
@@ -64,4 +64,4 @@ The reason for the difference is that Permutation Feature Importance doesn't mea
   
 ## Next steps
 
-See the [set of modules available](module-reference.md) to the Azure Machine Learning service. 
+See the [set of modules available](module-reference.md) to Azure Machine Learning. 
diff --git a/articles/machine-learning/algorithm-module-reference/score-svd-recommender.md b/articles/machine-learning/algorithm-module-reference/score-svd-recommender.md
@@ -1,7 +1,7 @@
 ---
 title: "Score SVD Recommender: Module reference"
-titleSuffix: Azure Machine Learning service
-description: Learn how to use the Score SVD Recommender module in the Azure Machine Learning service to score recommendation predictions for a dataset.
+titleSuffix: Azure Machine Learning
+description: Learn how to use the Score SVD Recommender module in Azure Machine Learning to score recommendation predictions for a dataset.
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
@@ -118,4 +118,4 @@ When you operationalize the model, you typically change the prediction mode to m
 
 ## Next steps
 
-See the [set of modules available](module-reference.md) to the Azure Machine Learning service. 
+See the [set of modules available](module-reference.md) to Azure Machine Learning. 
diff --git a/articles/machine-learning/algorithm-module-reference/select-columns-transform.md b/articles/machine-learning/algorithm-module-reference/select-columns-transform.md
@@ -1,7 +1,7 @@
 ---
 title: "Select Columns Transform: Module reference"
-titleSuffix: Azure Machine Learning service
-description: Learn how to use the Select Columns Transform module in the Azure Machine Learning service to create a transformation that selects the same subset of columns as in the given dataset.
+titleSuffix: Azure Machine Learning
+description: Learn how to use the Select Columns Transform module in Azure Machine Learning to create a transformation that selects the same subset of columns as in the given dataset.
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
@@ -50,4 +50,4 @@ This process of saving and then applying a column selection ensures that the sam
 
 ## Next steps
 
-See the [set of modules available](module-reference.md) to the Azure Machine Learning service. 
+See the [set of modules available](module-reference.md) to Azure Machine Learning. 
diff --git a/articles/machine-learning/algorithm-module-reference/smote.md b/articles/machine-learning/algorithm-module-reference/smote.md
@@ -1,7 +1,7 @@
 ---
 title: "SMOTE"
-titleSuffix: Azure Machine Learning service
-description: Learn how to use the SMOTE module in the Azure Machine Learning service to increase the number of low-incidence examples in a dataset by using oversampling.
+titleSuffix: Azure Machine Learning
+description: Learn how to use the SMOTE module in Azure Machine Learning to increase the number of low-incidence examples in a dataset by using oversampling.
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
@@ -94,5 +94,5 @@ To increase the number of cases, you can set the value of **SMOTE percentage**,
 
 ## Next steps
 
-See the [set of modules available](module-reference.md) to the Azure Machine Learning service. 
+See the [set of modules available](module-reference.md) to Azure Machine Learning. 
 
diff --git a/articles/machine-learning/algorithm-module-reference/summarize-data.md b/articles/machine-learning/algorithm-module-reference/summarize-data.md
@@ -1,7 +1,7 @@
 ---
 title:  "Summarize Data"
-titleSuffix: Azure Machine Learning service
-description: Learn how to use the Summarize Data module in Azure Machine Learning service to generate a basic descriptive statistics report for the columns in a dataset.
+titleSuffix: Azure Machine Learning
+description: Learn how to use the Summarize Data module in Azure Machine Learning to generate a basic descriptive statistics report for the columns in a dataset.
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
@@ -69,4 +69,4 @@ The report from the module can include the following statistics.
 
 ## Next steps
 
-See the [set of modules available](module-reference.md) to Azure Machine Learning service.  
+See the [set of modules available](module-reference.md) to Azure Machine Learning.  
diff --git a/articles/machine-learning/algorithm-module-reference/train-svd-recommender.md b/articles/machine-learning/algorithm-module-reference/train-svd-recommender.md
@@ -1,7 +1,7 @@
 ---
 title: "Train SVD Recommender: Module Reference"
-titleSuffix: Azure Machine Learning service
-description: Learn how to use the Train SVD Recommender module in the Azure Machine Learning service to train a Bayesian recommender by using the SVD algorithm.
+titleSuffix: Azure Machine Learning
+description: Learn how to use the Train SVD Recommender module in Azure Machine Learning to train a Bayesian recommender by using the SVD algorithm.
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
@@ -72,4 +72,4 @@ From this sample, you can see that a single user has rated two separate restaura
 
 ## Next steps
 
-See the [set of modules available](module-reference.md) to the Azure Machine Learning service. 
+See the [set of modules available](module-reference.md) to Azure Machine Learning. 
diff --git a/articles/machine-learning/algorithm-module-reference/tune-model-hyperparameters.md b/articles/machine-learning/algorithm-module-reference/tune-model-hyperparameters.md
@@ -1,7 +1,7 @@
 ---
 title: "Tune Model Hyperparameters"
-titleSuffix: Azure Machine Learning service
-description: Learn how to use the Tune Model Hyperparameters module in the Azure Machine Learning service to perform a parameter sweep on a model to determine the optimum parameter settings.
+titleSuffix: Azure Machine Learning
+description: Learn how to use the Tune Model Hyperparameters module in Azure Machine Learning to perform a parameter sweep on a model to determine the optimum parameter settings.
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
@@ -150,5 +150,5 @@ Almost all learners in Azure Machine Learning support cross-validation with an i
 
 ## Next steps
 
-See the [set of modules available](module-reference.md) to the Azure Machine Learning service. 
+See the [set of modules available](module-reference.md) to Azure Machine Learning. 
 
diff --git a/articles/machine-learning/service/how-to-machine-learning-interpretability-aml.md b/articles/machine-learning/service/how-to-machine-learning-interpretability-aml.md
@@ -24,7 +24,7 @@ In this article, you learn to use the interpretability package of the Azure Mach
 * View interpretability visualizations in [Azure Machine Learning studio](https://ml.azure.com).
 * Deploy a scoring explainer with your model.
 
-For more information, see [Model interpretability in Azure Machine Learning service](how-to-machine-learning-interpretability.md).
+For more information, see [Model interpretability in Azure Machine Learning](how-to-machine-learning-interpretability.md).
 
 ## Local interpretability
 
@@ -351,7 +351,7 @@ If both global and local explanations are available, data populates both tabs. I
 Follow one of these paths to access the visualization dashboard in Azure Machine Learning studio:
 
 * **Experiments** pane (Preview)
-  1. Select **Experiments** in the left pane to see a list of experiments that you've run on Azure Machine Learning service.
+  1. Select **Experiments** in the left pane to see a list of experiments that you've run on Azure Machine Learning.
   1. Select a particular experiment to view all the runs in that experiment.
   1. Select a run, and then the **Explanations** tab to the explanation visualization dashboard.
 
diff --git a/articles/machine-learning/service/how-to-machine-learning-interpretability-automl.md b/articles/machine-learning/service/how-to-machine-learning-interpretability-automl.md
@@ -17,7 +17,7 @@ ms.date: 10/25/2019
 
 [!INCLUDE [applies-to-skus](../../../includes/aml-applies-to-basic-enterprise-sku.md)]
 
-In this article, you learn how to enable the interpretability features for automated machine learning (ML) in Azure Machine Learning service. Automated ML helps you understand both raw and engineered feature importance. In order to use model interpretability, set `model_explainability=True` in the `AutoMLConfig` object.  
+In this article, you learn how to enable the interpretability features for automated machine learning (ML) in Azure Machine Learning. Automated ML helps you understand both raw and engineered feature importance. In order to use model interpretability, set `model_explainability=True` in the `AutoMLConfig` object.  
 
 In this article, you learn how to:
 
diff --git a/articles/machine-learning/service/how-to-machine-learning-interpretability.md b/articles/machine-learning/service/how-to-machine-learning-interpretability.md
@@ -1,5 +1,5 @@
 ---
-title: Model interpretability in Azure Machine Learning service
+title: Model interpretability in Azure Machine Learning
 titleSuffix: Azure Machine Learning
 description: Learn how to explain why your model makes predictions using the Azure Machine Learning SDK. It can be used during training and inference to understand how your model makes predictions.
 services: machine-learning
@@ -13,7 +13,7 @@ ms.reviewer: trbye
 ms.date: 10/25/2019
 ---
 
-# Model interpretability in Azure Machine Learning service
+# Model interpretability in Azure Machine Learning
 [!INCLUDE [applies-to-skus](../../../includes/aml-applies-to-basic-enterprise-sku.md)]
 
 ## Overview of model interpretability
@@ -107,7 +107,7 @@ The explanation functions accept both models and pipelines as input. If a model
 
 ### Local and remote compute target
 
-The `explain` package is designed to work with both local and remote compute targets. If run locally, The SDK functions will not contact any Azure services. You can run explanation remotely on Azure Machine Learning Compute and log the explanation info into Azure Machine Learning Run History Services. Once this information is logged, reports and visualizations from the explanation are readily available on Azure Machine Learning Workspace portal for user analysis.
+The `explain` package is designed to work with both local and remote compute targets. If run locally, The SDK functions will not contact any Azure services. You can run explanation remotely on Azure Machine Learning Compute and log the explanation info into Azure Machine Learning Run History Services. Once this information is logged, reports and visualizations from the explanation are readily available on Azure Machine Learning workspace for user analysis.
 
 
 ## Next steps
diff --git a/articles/machine-learning/service/how-to-monitor-datasets.md b/articles/machine-learning/service/how-to-monitor-datasets.md
@@ -26,7 +26,7 @@ With Azure Machine Learning dataset monitors, you can:
 * **Profile features in data** to track how statistical properties change over time.
 * **Set up alerts on data drift** for early warnings to potential issues. 
 
-Metrics and insights are available through the [Azure Application Insights](https://docs.microsoft.com/azure/azure-monitor/app/app-insights-overview) resource associated with the Azure Machine Learning service workspace.
+Metrics and insights are available through the [Azure Application Insights](https://docs.microsoft.com/azure/azure-monitor/app/app-insights-overview) resource associated with the Azure Machine Learning workspace.
 
 > [!Important]
 > Please note that monitoring data drift with the SDK is available in all editions, while monitoring data drift through the studio on the web is Enterprise edition only.
diff --git a/articles/machine-learning/service/how-to-use-event-grid.md b/articles/machine-learning/service/how-to-use-event-grid.md
@@ -15,7 +15,7 @@ ms.date: 11/04/2019
 
 # Create event driven machine learning workflows (Preview)
 
-[Azure Event Grid](https://docs.microsoft.com/azure/event-grid/) supports Azure Machine Learning service events. For example, you can use events from run completion, model registration, model deployment, and data drift detection scoped to a workspace.
+[Azure Event Grid](https://docs.microsoft.com/azure/event-grid/) supports Azure Machine Learning events. For example, you can use events from run completion, model registration, model deployment, and data drift detection scoped to a workspace.
 
 For more information, see [Azure Machine Learning integration with Event Grid](concept-event-grid-integration.md) and the [Azure Machine Learning event grid schema](/azure/event-grid/event-schema-machine-learning).
 
@@ -26,7 +26,7 @@ Use Event Grid to enable common scenarios such as:
 
 ## Prerequisites
 
-* Contributor or owner access to the Azure Machine learning service workspace you will create events for.
+* Contributor or owner access to the Azure Machine Learning workspace you will create events for.
 * Select an event handler endpoint such as a webhook or Event Hub. For more information, see [event handlers](https://docs.microsoft.com/azure/event-grid/event-handlers). 
 
 ## Register resource providers
diff --git a/articles/machine-learning/service/how-to-version-track-datasets.md b/articles/machine-learning/service/how-to-version-track-datasets.md
diff --git a/articles/machine-learning/service/monitor-azure-machine-learning.md b/articles/machine-learning/service/monitor-azure-machine-learning.md
diff --git a/articles/machine-learning/service/monitor-resource-reference.md b/articles/machine-learning/service/monitor-resource-reference.md
diff --git a/articles/machine-learning/service/resource-known-issues.md b/articles/machine-learning/service/resource-known-issues.md
diff --git a/articles/storage/common/storage-network-security.md b/articles/storage/common/storage-network-security.md
diff --git a/articles/virtual-network/service-tags-overview.md b/articles/virtual-network/service-tags-overview.md
