Merge pull request #229303 from Blackmist/terminology

v-dirichards · web-flow · commit 814a6ae78838 · 2023-03-02T13:25:12.000-06:00
bulk update
diff --git a/articles/machine-learning/azure-machine-learning-glossary.md b/articles/machine-learning/azure-machine-learning-glossary.md
@@ -26,7 +26,7 @@ An Azure Machine Learning [component](concept-component.md) is a self-contained
 
 ## Compute
 
-A compute is a designated compute resource where you run your job or host your endpoint. Azure Machine learning supports the following types of compute:
+A compute is a designated compute resource where you run your job or host your endpoint. Azure Machine Learning supports the following types of compute:
 
 * **Compute cluster** - a managed-compute infrastructure that allows you to easily create a cluster of CPU or GPU compute nodes in the cloud.
 * **Compute instance** - a fully configured and managed development environment in the cloud. You can use the instance as a training or inference compute for development and testing. It's similar to a virtual machine on the cloud.
diff --git a/articles/machine-learning/concept-azure-machine-learning-v2.md b/articles/machine-learning/concept-azure-machine-learning-v2.md
@@ -74,7 +74,7 @@ This [Jupyter notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/
 
 ## Compute
 
-A compute is a designated compute resource where you run your job or host your endpoint. Azure Machine learning supports the following types of compute:
+A compute is a designated compute resource where you run your job or host your endpoint. Azure Machine Learning supports the following types of compute:
 
 * **Compute cluster** - a managed-compute infrastructure that allows you to easily create a cluster of CPU or GPU compute nodes in the cloud.
 * **Compute instance** - a fully configured and managed development environment in the cloud. You can use the instance as a training or inference compute for development and testing. It's similar to a virtual machine on the cloud.
diff --git a/articles/machine-learning/concept-data-encryption.md b/articles/machine-learning/concept-data-encryption.md
@@ -1,5 +1,5 @@
 ---
-title: Data encryption with Azure Machine learning
+title: Data encryption with Azure Machine Learning
 titleSuffix: Azure Machine Learning
 description: 'Learn how Azure Machine Learning computes and data stores provides data encryption at rest and in transit.'
 services: machine-learning
diff --git a/articles/machine-learning/concept-mlflow.md b/articles/machine-learning/concept-mlflow.md
@@ -107,7 +107,7 @@ The following table shows which operations are supported by each of the tools av
 | Track and log metrics, parameters, and models | **&check;** | | |
 | Retrieve metrics, parameters, and models | **&check;** | <sup>1</sup> | **&check;** |
 | Submit training jobs | **&check;** <sup>2</sup> | **&check;** | **&check;** |
-| Submit training jobs with Azure Machine learning data assets |  | **&check;** | **&check;** |
+| Submit training jobs with Azure Machine Learning data assets |  | **&check;** | **&check;** |
 | Submit training jobs with machine learning pipelines | | **&check;** | **&check;** |
 | Manage experiments and runs | **&check;** | **&check;** | **&check;** |
 | Manage MLflow models | **&check;**<sup>3</sup> | **&check;** | **&check;** |
diff --git a/articles/machine-learning/how-to-authenticate-batch-endpoint.md b/articles/machine-learning/how-to-authenticate-batch-endpoint.md
@@ -147,7 +147,7 @@ In this case, we want to execute a batch endpoint using a service principal alre
 
 1. Create a secret to use for authentication as explained at [Option 2: Create a new application secret](../active-directory/develop/howto-create-service-principal-portal.md#option-2-create-a-new-application-secret). 
 
-1. Use the login service from Azure to get an authorization token. Authorization tokens are issued to a particular scope. The resource type for Azure Machine learning is `https://ml.azure.com`. The request would look as follows:
+1. Use the login service from Azure to get an authorization token. Authorization tokens are issued to a particular scope. The resource type for Azure Machine Learning is `https://ml.azure.com`. The request would look as follows:
     
     __Request__:
     
diff --git a/articles/machine-learning/how-to-configure-auto-train.md b/articles/machine-learning/how-to-configure-auto-train.md
@@ -24,7 +24,7 @@ In this guide, learn how to set up an automated machine learning, AutoML, traini
 
 If you prefer a no-code experience, you can also [Set up no-code AutoML training in the Azure Machine Learning studio](how-to-use-automated-ml-for-ml-models.md).
 
-If you prefer to submit training jobs with the Azure Machine learning CLI v2 extension, see [Train models](how-to-train-model.md).
+If you prefer to submit training jobs with the Azure Machine Learning CLI v2 extension, see [Train models](how-to-train-model.md).
 
 ## Prerequisites
 
diff --git a/articles/machine-learning/how-to-custom-dns.md b/articles/machine-learning/how-to-custom-dns.md
@@ -599,7 +599,7 @@ For more information on the services and DNS resolution, see [Azure Private Endp
 
 ## Troubleshooting
 
-If after running through the above steps you are unable to access the workspace from a virtual machine or jobs fail on compute resources in the Virtual Network containing the Private Endpoint to the Azure Machine learning workspace, follow the below steps to try to identify the cause.
+If after running through the above steps you are unable to access the workspace from a virtual machine or jobs fail on compute resources in the Virtual Network containing the Private Endpoint to the Azure Machine Learning workspace, follow the below steps to try to identify the cause.
 
 1. **Locate the workspace FQDNs on the Private Endpoint**:
 
diff --git a/articles/machine-learning/how-to-log-view-metrics.md b/articles/machine-learning/how-to-log-view-metrics.md
@@ -34,7 +34,7 @@ Logs can help you diagnose errors and warnings, or track performance metrics lik
 > * Viewing diagnostic information about training.
 
 > [!TIP]
-> This article shows you how to monitor the model training process. If you're interested in monitoring resource usage and events from Azure Machine learning, such as quotas, completed training jobs, or completed model deployments, see [Monitoring Azure Machine Learning](monitor-azure-machine-learning.md).
+> This article shows you how to monitor the model training process. If you're interested in monitoring resource usage and events from Azure Machine Learning, such as quotas, completed training jobs, or completed model deployments, see [Monitoring Azure Machine Learning](monitor-azure-machine-learning.md).
 
 > [!TIP]
 > For information on logging metrics in Azure Machine Learning designer, see [How to log metrics in the designer](how-to-track-designer-experiments.md).
diff --git a/articles/machine-learning/how-to-prevent-data-loss-exfiltration.md b/articles/machine-learning/how-to-prevent-data-loss-exfiltration.md
@@ -42,7 +42,7 @@ Azure Machine Learning has several inbound and outbound dependencies. Some of th
 
 ## Why do I need to use the service endpoint policy
 
-Service endpoint policies allow you to filter egress virtual network traffic to Azure Storage accounts over service endpoint and allow data exfiltration to only specific Azure Storage accounts. Azure Machine Learning compute instance and compute cluster requires access to Microsoft-managed storage accounts for its provisioning. The Azure Machine learning alias in service endpoint policies includes Microsoft-managed storage accounts. We use service endpoint policies with the Azure Machine Learning alias to prevent data exfiltration or control the destination storage accounts. You can learn more in [Service Endpoint policy documentation](../virtual-network/virtual-network-service-endpoint-policies-overview.md).
+Service endpoint policies allow you to filter egress virtual network traffic to Azure Storage accounts over service endpoint and allow data exfiltration to only specific Azure Storage accounts. Azure Machine Learning compute instance and compute cluster requires access to Microsoft-managed storage accounts for its provisioning. The Azure Machine Learning alias in service endpoint policies includes Microsoft-managed storage accounts. We use service endpoint policies with the Azure Machine Learning alias to prevent data exfiltration or control the destination storage accounts. You can learn more in [Service Endpoint policy documentation](../virtual-network/virtual-network-service-endpoint-policies-overview.md).
 
 ## 1. Create the service endpoint policy
 
diff --git a/articles/machine-learning/how-to-secure-kubernetes-inferencing-environment.md b/articles/machine-learning/how-to-secure-kubernetes-inferencing-environment.md
@@ -23,7 +23,7 @@ If you have an Azure Kubernetes (AKS) cluster behind of VNet, you would need to
 
 * If your AKS cluster is behind of a VNet, your workspace and its associated resources (storage, key vault, Azure Container Registry) must have private endpoints or service endpoints in the same or peered VNet as AKS cluster's VNet. For more information on securing the workspace and associated resources, see [create a secure workspace](tutorial-create-secure-workspace.md).
 * If your workspace has a __private endpoint__, the Azure Kubernetes Service cluster must be in the same Azure region as the workspace.
-* Using a [public fully qualified domain name (FQDN) with a private AKS cluster](../aks/private-clusters.md) is __not supported__ with Azure Machine learning.
+* Using a [public fully qualified domain name (FQDN) with a private AKS cluster](../aks/private-clusters.md) is __not supported__ with Azure Machine Learning.
 
 ## What is a secure AKS inferencing environment
 
diff --git a/articles/machine-learning/how-to-view-online-endpoints-costs.md b/articles/machine-learning/how-to-view-online-endpoints-costs.md
@@ -33,7 +33,7 @@ Navigate to the **Cost Analysis** page for your subscription:
 
     [![Managed online endpoint cost analysis: screenshot of a subscription in the Azure portal showing red box around "Cost Analysis" button on the left hand side.](./media/how-to-view-online-endpoints-costs/online-endpoints-cost-analysis.png)](./media/how-to-view-online-endpoints-costs/online-endpoints-cost-analysis.png#lightbox)
 
-Create a filter to scope data to your Azure Machine learning workspace resource:
+Create a filter to scope data to your Azure Machine Learning workspace resource:
 
 1. At the top navigation bar, select **Add filter**.
 
diff --git a/articles/machine-learning/reference-machine-learning-cloud-parity.md b/articles/machine-learning/reference-machine-learning-cloud-parity.md
@@ -237,4 +237,4 @@ The information in the rest of this document provides information on what featur
 
 ## Next steps
 
-To learn more about the regions that Azure Machine learning is available in, see [Products by region](https://azure.microsoft.com/global-infrastructure/services/).
+To learn more about the regions that Azure Machine Learning is available in, see [Products by region](https://azure.microsoft.com/global-infrastructure/services/).
diff --git a/articles/machine-learning/v1/concept-mlflow-v1.md b/articles/machine-learning/v1/concept-mlflow-v1.md
@@ -22,7 +22,7 @@ ms.custom: how-to, devx-track-python, sdkv1, event-tier1-build-2022
 
 [MLflow](https://www.mlflow.org) is an open-source library for managing the life cycle of your machine learning experiments. MLflow's tracking URI and logging API are collectively known as [MLflow Tracking](https://mlflow.org/docs/latest/quickstart.html#using-the-tracking-api). This component of MLflow logs and tracks your training run metrics and model artifacts, no matter where your experiment's environment is--on your computer, on a remote compute target, on a virtual machine, or in an Azure Databricks cluster. 
 
-Together, MLflow Tracking and Azure Machine learning allow you to track an experiment's run metrics and store model artifacts in your Azure Machine Learning workspace.
+Together, MLflow Tracking and Azure Machine Learning allow you to track an experiment's run metrics and store model artifacts in your Azure Machine Learning workspace.
 
 ## Compare MLflow and Azure Machine Learning clients
 
diff --git a/articles/machine-learning/v1/how-to-create-attach-kubernetes.md b/articles/machine-learning/v1/how-to-create-attach-kubernetes.md
@@ -54,7 +54,7 @@ Azure Machine Learning can deploy trained machine learning models to Azure Kuber
 
 - If you want to use a private AKS cluster (using Azure Private Link), you must create the cluster first, and then **attach** it to the workspace. For more information, see [Create a private Azure Kubernetes Service cluster](../../aks/private-clusters.md).
 
-- Using a [public fully qualified domain name (FQDN) with a private AKS cluster](../../aks/private-clusters.md) is __not supported__ with Azure Machine learning. 
+- Using a [public fully qualified domain name (FQDN) with a private AKS cluster](../../aks/private-clusters.md) is __not supported__ with Azure Machine Learning. 
 
 - The compute name for the AKS cluster MUST be unique within your Azure Machine Learning workspace. It can include letters, digits and dashes. It must start with a letter, end with a letter or digit, and be between 3 and 24 characters in length.
  
diff --git a/articles/machine-learning/v1/how-to-log-view-metrics.md b/articles/machine-learning/v1/how-to-log-view-metrics.md
@@ -34,7 +34,7 @@ Logs can help you diagnose errors and warnings, or track performance metrics lik
 
 
 > [!TIP]
-> This article shows you how to monitor the model training process. If you're interested in monitoring resource usage and events from Azure Machine learning, such as quotas, completed training runs, or completed model deployments, see [Monitoring Azure Machine Learning](../monitor-azure-machine-learning.md).
+> This article shows you how to monitor the model training process. If you're interested in monitoring resource usage and events from Azure Machine Learning, such as quotas, completed training runs, or completed model deployments, see [Monitoring Azure Machine Learning](../monitor-azure-machine-learning.md).
 
 ## Data types
 
diff --git a/articles/machine-learning/v1/how-to-monitor-datasets.md b/articles/machine-learning/v1/how-to-monitor-datasets.md
@@ -28,7 +28,7 @@ With Azure Machine Learning dataset monitors (preview), you can:
 * **Set up alerts on data drift** for early warnings to potential issues. 
 * **[Create a new dataset version](how-to-version-track-datasets.md)** when you determine the data has drifted too much.
 
-An [Azure Machine learning dataset](how-to-create-register-datasets.md) is used to create the monitor. The dataset must include a timestamp column.
+An [Azure Machine Learning dataset](how-to-create-register-datasets.md) is used to create the monitor. The dataset must include a timestamp column.
 
 You can view data drift metrics with the Python SDK or in Azure Machine Learning studio.  Other metrics and insights are available through the [Azure Application Insights](../../azure-monitor/app/app-insights-overview.md) resource associated with the Azure Machine Learning workspace.
 
diff --git a/articles/machine-learning/v1/how-to-monitor-tensorboard.md b/articles/machine-learning/v1/how-to-monitor-tensorboard.md
@@ -29,7 +29,7 @@ How you launch TensorBoard with Azure Machine Learning experiments depends on th
 + For experiments that don't natively output TensorBoard consumable files, such as like Scikit-learn or Azure Machine Learning experiments, use [the `export_to_tensorboard()` method](#option-2-export-history-as-log-to-view-in-tensorboard) to export the job histories as TensorBoard logs and launch TensorBoard from there. 
 
 > [!TIP]
-> The information in this document is primarily for data scientists and developers who want to monitor the model training process. If you are an administrator interested in monitoring resource usage and events from Azure Machine learning, such as quotas, completed training jobs, or completed model deployments, see [Monitoring Azure Machine Learning](../monitor-azure-machine-learning.md).
+> The information in this document is primarily for data scientists and developers who want to monitor the model training process. If you are an administrator interested in monitoring resource usage and events from Azure Machine Learning, such as quotas, completed training jobs, or completed model deployments, see [Monitoring Azure Machine Learning](../monitor-azure-machine-learning.md).
 
 ## Prerequisites
 
diff --git a/articles/machine-learning/v1/how-to-secure-inferencing-vnet.md b/articles/machine-learning/v1/how-to-secure-inferencing-vnet.md
@@ -67,7 +67,7 @@ When your Azure Machine Learning workspace is configured with a private endpoint
 
 * If your AKS cluster is behind of a VNET, your workspace and its associated resources (storage, key vault, Azure Container Registry) must have private endpoints or service endpoints in the same VNET as AKS cluster's VNET. Please read tutorial [create a secure workspace](../tutorial-create-secure-workspace.md) to add those private endpoints or service endpoints to your VNET.
 * If your workspace has a __private endpoint__, the Azure Kubernetes Service cluster must be in the same Azure region as the workspace.
-* Using a [public fully qualified domain name (FQDN) with a private AKS cluster](../../aks/private-clusters.md) is __not supported__ with Azure Machine learning.
+* Using a [public fully qualified domain name (FQDN) with a private AKS cluster](../../aks/private-clusters.md) is __not supported__ with Azure Machine Learning.
 
 <a id="aksvnet"></a>
 
diff --git a/articles/machine-learning/v1/how-to-troubleshoot-deployment-local.md b/articles/machine-learning/v1/how-to-troubleshoot-deployment-local.md
@@ -32,7 +32,7 @@ Try a local model deployment as a first step in troubleshooting deployment to Az
     * The Azure Machine Learning inference HTTP server [(preview)](https://azure.microsoft.com/support/legal/preview-supplemental-terms/) is a Python package that allows you to easily validate your entry script (`score.py`) in a local development environment. If there's a problem with the scoring script, the server will return an error. It will also return the location where the error occurred.
     * The server can also be used when creating validation gates in a continuous integration and deployment pipeline. For example, start the server with thee candidate script and run the test suite against the local endpoint.
 
-## Azure Machine learning inference HTTP server
+## Azure Machine Learning inference HTTP server
 
 The local inference server allows you to quickly debug your entry script (`score.py`). In case the underlying score script has a bug, the server will fail to initialize or serve the model. Instead, it will throw an exception & the location where the issues occurred. [Learn more about Azure Machine Learning inference HTTP Server](../how-to-inference-server-http.md)
 
diff --git a/articles/machine-learning/v1/how-to-troubleshoot-deployment.md b/articles/machine-learning/v1/how-to-troubleshoot-deployment.md
@@ -87,7 +87,7 @@ print(service.get_logs())
 
 If you have problems when deploying a model to ACI or AKS, deploy it as a local web service. Using a local web service makes it easier to troubleshoot problems. To troubleshoot a deployment locally, see the [local troubleshooting article](how-to-troubleshoot-deployment-local.md).
 
-## Azure Machine learning inference HTTP server
+## Azure Machine Learning inference HTTP server
 
 The local inference server allows you to quickly debug your entry script (`score.py`). In case the underlying score script has a bug, the server will fail to initialize or serve the model. Instead, it will throw an exception & the location where the issues occurred. [Learn more about Azure Machine Learning inference HTTP Server](../how-to-inference-server-http.md)
 
diff --git a/articles/machine-learning/v1/how-to-workspace-diagnostic-api.md b/articles/machine-learning/v1/how-to-workspace-diagnostic-api.md
@@ -26,7 +26,7 @@ In this article, learn how to use the workspace diagnostics from the Azure Machi
 
 ## Prerequisites
 
-* An Azure Machine learning workspace. If you don't have one, see [Create a workspace](../quickstart-create-resources.md).
+* An Azure Machine Learning workspace. If you don't have one, see [Create a workspace](../quickstart-create-resources.md).
 * The [Azure Machine Learning SDK for Python](/python/api/overview/azure/ml).
 
 ## Diagnostics from Python
