From fabf6579dfe2234cd1ce6d1bb4e588b7da3f6658 Mon Sep 17 00:00:00 2001 From: j-martens Date: Thu, 17 Oct 2019 14:59:02 -0500 Subject: [PATCH] rebranding some more... --- articles/azure-subscription-service-limits.md | 4 ++-- articles/cdn/cdn-billing.md | 2 +- articles/data-factory/compute-linked-services.md | 2 +- .../transform-data-machine-learning-service.md | 12 ++++++------ .../apache-spark-run-machine-learning-automl.md | 2 +- .../iot-edge/tutorial-deploy-machine-learning.md | 6 +++--- .../cpp-consulting-service-define-offer-settings.md | 2 +- articles/notebooks/azure-notebooks-overview.md | 2 +- articles/notebooks/azure-notebooks-samples.md | 4 ++-- articles/notebooks/create-clone-jupyter-notebooks.md | 2 +- articles/notebooks/toc.yml | 2 +- .../tutorial-create-run-jupyter-notebook.md | 2 +- ...se-machine-learning-services-jupyter-notebooks.md | 12 ++++++------ .../open-datasets/tutorial-opendatasets-automl.md | 6 +++--- .../security/blueprints/ffiec-analytics-overview.md | 4 ++-- .../blueprints/nist171-analytics-overview.md | 4 ++-- .../security/blueprints/pcidss-analytics-overview.md | 4 ++-- articles/security/fundamentals/encryption-atrest.md | 2 +- articles/storage/common/storage-network-security.md | 2 +- ...nalytics-scale-with-machine-learning-functions.md | 2 +- includes/aml-delete-resource-group.md | 2 +- includes/aml-ui-cleanup.md | 2 +- .../aml-templates/template-concepts.md | 2 +- .../aml-templates/template-quickstart.md | 2 +- 24 files changed, 43 insertions(+), 43 deletions(-) diff --git a/articles/azure-subscription-service-limits.md b/articles/azure-subscription-service-limits.md index e93ccb6449f85..b9641c559d07f 100644 --- a/articles/azure-subscription-service-limits.md +++ b/articles/azure-subscription-service-limits.md @@ -51,7 +51,7 @@ In the following list of limits, a new table reflects any differences in limits * [Azure Firewall](#azure-firewall-limits) * [Azure Functions](#functions-limits) * [Azure Kubernetes Service](#azure-kubernetes-service-limits) -* [Azure Machine Learning Service](#azure-machine-learning-service-limits) +* [Azure Machine Learning](#azure-machine-learning-service-limits) * [Azure Maps](#azure-maps-limits) * [Azure Monitor](#azure-monitor-limits) * [Azure Policy](#azure-policy-limits) @@ -149,7 +149,7 @@ The following table details the features and limits of the Basic, Standard, and ### Azure Kubernetes Service limits [!INCLUDE [container-service-limits](../includes/container-service-limits.md)] -### Azure Machine Learning Service limits +### Azure Machine Learning limits The latest values for Azure Machine Learning Compute quotas can be found in the [Azure Machine Learning quota page](../articles/machine-learning/service/how-to-manage-quotas.md) ### Networking limits diff --git a/articles/cdn/cdn-billing.md b/articles/cdn/cdn-billing.md index 6f8c15dc55e5b..a98a98ac9fedd 100644 --- a/articles/cdn/cdn-billing.md +++ b/articles/cdn/cdn-billing.md @@ -109,7 +109,7 @@ If you use one of the following Azure services as your CDN origin, you will not - HDInsight - Azure Cosmos DB - Azure Data Lake Store -- Azure Machine Learning service +- Azure Machine Learning - Azure SQL database - Azure Cache for Redis diff --git a/articles/data-factory/compute-linked-services.md b/articles/data-factory/compute-linked-services.md index 38e8fbb155e72..5ef9e738ec98e 100644 --- a/articles/data-factory/compute-linked-services.md +++ b/articles/data-factory/compute-linked-services.md @@ -22,7 +22,7 @@ The following table provides a list of compute environments supported by Data Fa | [On-demand HDInsight cluster](#azure-hdinsight-on-demand-linked-service) or [your own HDInsight cluster](#azure-hdinsight-linked-service) | [Hive](transform-data-using-hadoop-hive.md), [Pig](transform-data-using-hadoop-pig.md), [Spark](transform-data-using-spark.md), [MapReduce](transform-data-using-hadoop-map-reduce.md), [Hadoop Streaming](transform-data-using-hadoop-streaming.md) | | [Azure Batch](#azure-batch-linked-service) | [Custom](transform-data-using-dotnet-custom-activity.md) | | [Azure Machine Learning Studio](#azure-machine-learning-studio-linked-service) | [Machine Learning activities: Batch Execution and Update Resource](transform-data-using-machine-learning.md) | -| [Azure Machine Learning Service](#azure-machine-learning-service-linked-service) | [Azure Machine Learning Execute Pipeline](transform-data-machine-learning-service.md) | +| [Azure Machine Learning](#azure-machine-learning-service-linked-service) | [Azure Machine Learning Execute Pipeline](transform-data-machine-learning-service.md) | | [Azure Data Lake Analytics](#azure-data-lake-analytics-linked-service) | [Data Lake Analytics U-SQL](transform-data-using-data-lake-analytics.md) | | [Azure SQL](#azure-sql-database-linked-service), [Azure SQL Data Warehouse](#azure-sql-data-warehouse-linked-service), [SQL Server](#sql-server-linked-service) | [Stored Procedure](transform-data-using-stored-procedure.md) | | [Azure Databricks](#azure-databricks-linked-service) | [Notebook](transform-data-databricks-notebook.md), [Jar](transform-data-databricks-jar.md), [Python](transform-data-databricks-python.md) | diff --git a/articles/data-factory/transform-data-machine-learning-service.md b/articles/data-factory/transform-data-machine-learning-service.md index b0f62a8d0155e..4a8d164c297d7 100644 --- a/articles/data-factory/transform-data-machine-learning-service.md +++ b/articles/data-factory/transform-data-machine-learning-service.md @@ -1,6 +1,6 @@ --- -title: Execute Azure Machine Learning service pipelines in your Azure Data Factory pipelines | Microsoft Docs -description: Learn how to run your Azure Machine Learning service pipelines in your Azure Data Factory pipelines. +title: Execute Azure Machine Learning pipelines in your Azure Data Factory pipelines | Microsoft Docs +description: Learn how to run your Azure Machine Learning pipelines in your Azure Data Factory pipelines. services: data-factory documentationcenter: '' ms.service: data-factory @@ -11,9 +11,9 @@ ms.date: 10/10/2019 author: djpmsft ms.author: daperlov --- -# Execute Azure Machine Learning service pipelines in Azure Data Factory +# Execute Azure Machine Learning pipelines in Azure Data Factory -Run your Azure Machine Learning service pipelines as a step in your Azure Data Factory pipelines. The Machine Learning Execute Pipeline activity enables batch prediction scenarios such as identifying possible loan defaults, determining sentiment, and analyzing customer behavior patterns. +Run your Azure Machine Learning pipelines as a step in your Azure Data Factory pipelines. The Machine Learning Execute Pipeline activity enables batch prediction scenarios such as identifying possible loan defaults, determining sentiment, and analyzing customer behavior patterns. ## Syntax @@ -42,11 +42,11 @@ Property | Description | Allowed values | Required -------- | ----------- | -------------- | -------- name | Name of the activity in the pipeline | String | Yes type | Type of activity is ‘AzureMLExecutePipeline’ | String | Yes -linkedServiceName | Linked Service to Azure Machine Learning Service | Linked service reference | Yes +linkedServiceName | Linked Service to Azure Machine Learning | Linked service reference | Yes mlPipelineId | ID of the published Azure Machine Learning pipeline | String (or expression with resultType of string) | Yes experimentName | Run history experiment name of the Machine Learning pipeline run | String (or expression with resultType of string) | No mlPipelineParameters | Key, Value pairs to be passed to the published Azure Machine Learning pipeline endpoint. Keys must match the names of pipeline parameters defined in the published Machine Learning pipeline | Object with key value pairs (or Expression with resultType object) | No -mlParentRunId | The parent Azure Machine Learning Service pipeline run ID | String (or expression with resultType of string) | No +mlParentRunId | The parent Azure Machine Learning pipeline run ID | String (or expression with resultType of string) | No continueOnStepFailure | Whether to continue execution of other steps in the Machine Learning pipeline run if a step fails | boolean | No ## Next steps diff --git a/articles/hdinsight/spark/apache-spark-run-machine-learning-automl.md b/articles/hdinsight/spark/apache-spark-run-machine-learning-automl.md index 5aa9033394d29..7d4e1451b5363 100644 --- a/articles/hdinsight/spark/apache-spark-run-machine-learning-automl.md +++ b/articles/hdinsight/spark/apache-spark-run-machine-learning-automl.md @@ -70,5 +70,5 @@ In the [automated machine learning configuration](https://docs.microsoft.com/pyt ## Next steps * For more information on the motivation behind automated machine learning, see [Release models at pace using Microsoft’s automated machine learning!](https://azure.microsoft.com/blog/release-models-at-pace-using-microsoft-s-automl/) -* For more details on using Azure ML Automated ML capabilities, see [New automated machine learning capabilities in Azure Machine Learning service](https://azure.microsoft.com/blog/new-automated-machine-learning-capabilities-in-azure-machine-learning-service/) +* For more details on using Azure ML Automated ML capabilities, see [New automated machine learning capabilities in Azure Machine Learning](https://azure.microsoft.com/blog/new-automated-machine-learning-capabilities-in-azure-machine-learning-service/) * [AutoML project from Microsoft Research](https://www.microsoft.com/research/project/automl/) diff --git a/articles/iot-edge/tutorial-deploy-machine-learning.md b/articles/iot-edge/tutorial-deploy-machine-learning.md index 5dd774653c189..ec3298c7fedfe 100644 --- a/articles/iot-edge/tutorial-deploy-machine-learning.md +++ b/articles/iot-edge/tutorial-deploy-machine-learning.md @@ -15,7 +15,7 @@ ms.custom: "mvc, seodec18" Use Azure Notebooks to develop a machine learning module and deploy it to a Linux device running Azure IoT Edge. -You can use IoT Edge modules to deploy code that implements your business logic directly to your IoT Edge devices. This tutorial walks you through deploying an Azure Machine Learning module that predicts when a device fails based on simulated machine temperature data. For more information about Azure Machine Learning service on IoT Edge, see [Azure Machine Learning documentation](../machine-learning/service/how-to-deploy-to-iot.md). +You can use IoT Edge modules to deploy code that implements your business logic directly to your IoT Edge devices. This tutorial walks you through deploying an Azure Machine Learning module that predicts when a device fails based on simulated machine temperature data. For more information about Azure Machine Learning on IoT Edge, see [Azure Machine Learning documentation](../machine-learning/service/how-to-deploy-to-iot.md). The Azure Machine Learning module that you create in this tutorial reads the environmental data generated by your device and labels the messages as anomalous or not. @@ -50,7 +50,7 @@ Cloud resources: ## Create and deploy Azure Machine Learning module -In this section, you convert trained machine learning model files and into an Azure Machine Learning service container. All the components required for the Docker image are in the [AI Toolkit for Azure IoT Edge Git repo](https://github.com/Azure/ai-toolkit-iot-edge/tree/master/IoT%20Edge%20anomaly%20detection%20tutorial). Follow these steps to upload that repository into Microsoft Azure Notebooks to create the container and push it to Azure Container Registry. +In this section, you convert trained machine learning model files and into an Azure Machine Learning container. All the components required for the Docker image are in the [AI Toolkit for Azure IoT Edge Git repo](https://github.com/Azure/ai-toolkit-iot-edge/tree/master/IoT%20Edge%20anomaly%20detection%20tutorial). Follow these steps to upload that repository into Microsoft Azure Notebooks to create the container and push it to Azure Container Registry. 1. Navigate to your Azure Notebooks projects. You can get there from your Azure Machine Learning workspace in the [Azure portal](https://portal.azure.com) or by signing in to [Microsoft Azure Notebooks](https://notebooks.azure.com/home/projects) with your Azure account. @@ -144,7 +144,7 @@ The following steps show you how to set up Visual Studio Code to monitor device- 5. Observe the messages coming from tempSensor every five seconds. The message body contains a property called **anomaly**, which the machinelearningmodule provides with a true or false value. The **AzureMLResponse** property contains the value "OK" if the model ran successfully. - ![Azure Machine Learning service response in message body](./media/tutorial-deploy-machine-learning/ml-output.png) + ![Azure Machine Learning response in message body](./media/tutorial-deploy-machine-learning/ml-output.png) ## Clean up resources diff --git a/articles/marketplace/cloud-partner-portal/consulting-services/cpp-consulting-service-define-offer-settings.md b/articles/marketplace/cloud-partner-portal/consulting-services/cpp-consulting-service-define-offer-settings.md index 5a8221fe24e3d..02a71c87f400e 100644 --- a/articles/marketplace/cloud-partner-portal/consulting-services/cpp-consulting-service-define-offer-settings.md +++ b/articles/marketplace/cloud-partner-portal/consulting-services/cpp-consulting-service-define-offer-settings.md @@ -54,7 +54,7 @@ The following list provides several well-named offer names: - Essentials for Professional Services: 1-Hr Briefing - Cloud Migration Platform: 1-Hr Briefing - PowerApps and Microsoft Flow: 1-Day Workshop -- Azure Machine Learning Services: 3-Wk PoC +- Azure Machine Learnings: 3-Wk PoC - Brick and Click Retail Solution: 1-Hr Briefing - Bring Your Own Data: 1-Wk Workshop - Cloud Analytics: 3-Day Workshop diff --git a/articles/notebooks/azure-notebooks-overview.md b/articles/notebooks/azure-notebooks-overview.md index 96cd08144f4c6..615af3fdf616a 100644 --- a/articles/notebooks/azure-notebooks-overview.md +++ b/articles/notebooks/azure-notebooks-overview.md @@ -113,4 +113,4 @@ To discuss your questions about Azure Notebooks, file an issue on the [GitHub re - [Present a slide show](present-jupyter-notebooks-slideshow.md) - [Work with data files](work-with-project-data-files.md) - [Access data resources](access-data-resources-jupyter-notebooks.md) - - [Use Azure Machine Learning Services](use-machine-learning-services-jupyter-notebooks.md) + - [Use Azure Machine Learning](use-machine-learning-services-jupyter-notebooks.md) diff --git a/articles/notebooks/azure-notebooks-samples.md b/articles/notebooks/azure-notebooks-samples.md index 7f8e1c534709d..3f57232185b8e 100644 --- a/articles/notebooks/azure-notebooks-samples.md +++ b/articles/notebooks/azure-notebooks-samples.md @@ -25,7 +25,7 @@ Once you've identified a notebook you'd like to explore, here are a few details ## Great sample notebooks -- [Azure Notebooks starter set](https://notebooks.azure.com/#sample-redirect) includes introductions to Python, R, and F#, along with notebooks demonstrating data access, Azure Machine Learning Services, and a few data science exercises. +- [Azure Notebooks starter set](https://notebooks.azure.com/#sample-redirect) includes introductions to Python, R, and F#, along with notebooks demonstrating data access, Azure Machine Learning, and a few data science exercises. - [Introduction to Jupyter notebooks](https://nbviewer.jupyter.org/github/jupyter/notebook/blob/master/docs/source/examples/Notebook/Notebook%20Basics.ipynb) (jupyter.org) - [Introduction to Data Science](https://github.com/jakevdp/PythonDataScienceHandbook/tree/master/notebooks) by Jake Vanderplas. - [Gallery of interesting notebooks](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks) (Jupyter project on GitHub) @@ -38,4 +38,4 @@ Once you've identified a notebook you'd like to explore, here are a few details - [How to: Configure and manage projects](configure-manage-azure-notebooks-projects.md) - [How to: Work with data files](work-with-project-data-files.md) - [How to: Access data resources](access-data-resources-jupyter-notebooks.md) -- [How to: Use Azure Machine Learning Services](use-machine-learning-services-jupyter-notebooks.md) +- [How to: Use Azure Machine Learning](use-machine-learning-services-jupyter-notebooks.md) diff --git a/articles/notebooks/create-clone-jupyter-notebooks.md b/articles/notebooks/create-clone-jupyter-notebooks.md index 94f16df125f62..bb93a025af0b3 100644 --- a/articles/notebooks/create-clone-jupyter-notebooks.md +++ b/articles/notebooks/create-clone-jupyter-notebooks.md @@ -120,4 +120,4 @@ To clone a project: - [How to: Present a slide show](present-jupyter-notebooks-slideshow.md) - [How to: Work with data files](work-with-project-data-files.md) - [How to: Access data resources](access-data-resources-jupyter-notebooks.md) -- [How to: Use Azure Machine Learning Services](use-machine-learning-services-jupyter-notebooks.md) +- [How to: Use Azure Machine Learning](use-machine-learning-services-jupyter-notebooks.md) diff --git a/articles/notebooks/toc.yml b/articles/notebooks/toc.yml index d1ab63f7d21ee..8a64630f16a34 100644 --- a/articles/notebooks/toc.yml +++ b/articles/notebooks/toc.yml @@ -46,5 +46,5 @@ href: access-data-resources-jupyter-notebooks.md - name: Use Data Science Virtual Machines href: use-data-science-virtual-machine.md - - name: Use Azure Machine Learning Services + - name: Use Azure Machine Learning href: use-machine-learning-services-jupyter-notebooks.md diff --git a/articles/notebooks/tutorial-create-run-jupyter-notebook.md b/articles/notebooks/tutorial-create-run-jupyter-notebook.md index 54d424cdc43f7..8f717424e0b1e 100644 --- a/articles/notebooks/tutorial-create-run-jupyter-notebook.md +++ b/articles/notebooks/tutorial-create-run-jupyter-notebook.md @@ -425,4 +425,4 @@ How-to articles: - [Present a slide show](present-jupyter-notebooks-slideshow.md) - [Work with data files](work-with-project-data-files.md) - [Access data resources](access-data-resources-jupyter-notebooks.md) -- [Use Azure Machine Learning Services](use-machine-learning-services-jupyter-notebooks.md) +- [Use Azure Machine Learning](use-machine-learning-services-jupyter-notebooks.md) diff --git a/articles/notebooks/use-machine-learning-services-jupyter-notebooks.md b/articles/notebooks/use-machine-learning-services-jupyter-notebooks.md index 7d4e86ca9b099..173ed170cdaa1 100644 --- a/articles/notebooks/use-machine-learning-services-jupyter-notebooks.md +++ b/articles/notebooks/use-machine-learning-services-jupyter-notebooks.md @@ -1,6 +1,6 @@ --- -title: Use Azure Machine Learning Services in Azure Notebooks -description: An overview of the sample notebooks for Azure Machine Learnings that you can use with Azure Notebooks. +title: Use Azure Machine Learning in Azure Notebooks +description: An overview of the sample notebooks for Azure Machine Learning that you can use with Azure Notebooks. services: app-service documentationcenter: '' author: kraigb @@ -14,16 +14,16 @@ ms.date: 12/04/2018 ms.author: kraigb --- -# Use Azure Machine Learning service in a notebook +# Use Azure Machine Learning in a notebook -Azure Notebooks comes pre-configured with the necessary environment to work with [Azure Machine Learning service](/azure/machine-learning/service/). You can easily clone a sample project into your Notebooks account to explore a variety of Machine Learning scenarios. +Azure Notebooks comes pre-configured with the necessary environment to work with [Azure Machine Learning](/azure/machine-learning/service/). You can easily clone a sample project into your Notebooks account to explore a variety of Machine Learning scenarios. ## Clone the sample into your account 1. Sign into [Azure Notebooks](https://notebooks.azure.com/). 1. Select **My Projects** to go to the projects dashboard. 1. Select the **Upload GitHub Repo** (the up arrow) button to open the **Upload GitHub Repository** popup. -1. In the popup, enter `Azure/MachineLearningNotebooks` in **GitHub repository**, provide a name for the project in **Project Name** like "Azure Machine Learning service", provide an identifier in **Project ID**, clear **Public** if you want, then select **Import**. +1. In the popup, enter `Azure/MachineLearningNotebooks` in **GitHub repository**, provide a name for the project in **Project Name** like "Azure Machine Learning", provide an identifier in **Project ID**, clear **Public** if you want, then select **Import**. ![Import Azure Machine Learning Notebook sample into your Notebooks account](media/azureml-import-project.png) @@ -44,6 +44,6 @@ The Azure Machine Learning documentation contains a variety of other resources t - [Quickstart: Use Python to get started with Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/service/quickstart-create-workspace-with-python) - [Tutorial #1: Train an image classification model with Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/service/tutorial-train-models-with-aml) - [Tutorial #2: Deploy an image classification model in Azure Container Instance (ACI)](https://docs.microsoft.com/azure/machine-learning/service/tutorial-deploy-models-with-aml) -- [Tutorial: Train a classification model with automated machine learning in Azure Machine Learning service](https://docs.microsoft.com/azure/machine-learning/service/tutorial-auto-train-models) +- [Tutorial: Train a classification model with automated machine learning in Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/service/tutorial-auto-train-models) Also see the documentation for the [Azure Machine Learning SDK for Python](https://docs.microsoft.com/python/api/overview/azure/ml/intro?view=azure-ml-py). diff --git a/articles/open-datasets/tutorial-opendatasets-automl.md b/articles/open-datasets/tutorial-opendatasets-automl.md index 916c0a6ca0c43..fa36105184413 100644 --- a/articles/open-datasets/tutorial-opendatasets-automl.md +++ b/articles/open-datasets/tutorial-opendatasets-automl.md @@ -1,7 +1,7 @@ --- title: 'Tutorial: Enrich an automated machine learning model' titleSuffix: Azure Open Datasets -description: Learn how to leverage the convenience of Azure Open Datasets along with the power of Azure Machine Learning service to create a regression model to predict NYC taxi fare prices. +description: Learn how to leverage the convenience of Azure Open Datasets along with the power of Azure Machine Learning to create a regression model to predict NYC taxi fare prices. services: open-datasets ms.service: open-datasets ms.topic: tutorial @@ -13,7 +13,7 @@ ms.date: 05/02/2019 # Tutorial: Build a regression model with automated machine learning and Open Datasets -In this tutorial, you leverage the convenience of Azure Open Datasets along with the power of Azure Machine Learning service to create a regression model to predict NYC taxi fare prices. Easily download publicly available taxi, holiday and weather data, and configure an automated machine learning experiment using Azure Machine Learning service. This process accepts training data and configuration settings, and automatically iterates through combinations of different feature normalization/standardization methods, models, and hyperparameter settings to arrive at the best model. +In this tutorial, you leverage the convenience of Azure Open Datasets along with the power of Azure Machine Learning to create a regression model to predict NYC taxi fare prices. Easily download publicly available taxi, holiday and weather data, and configure an automated machine learning experiment using Azure Machine Learning. This process accepts training data and configuration settings, and automatically iterates through combinations of different feature normalization/standardization methods, models, and hyperparameter settings to arrive at the best model. In this tutorial you learn the following tasks: @@ -1969,4 +1969,4 @@ If you don't plan to use the resources you created, delete them, so you don't in ## Next steps * See the Azure Open Datasets [notebooks](https://github.com/Azure/OpenDatasetsNotebooks) for more code examples. -* Follow the [how-to](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-auto-train) for more information on automated machine learning in Azure Machine Learning service. +* Follow the [how-to](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-auto-train) for more information on automated machine learning in Azure Machine Learning. diff --git a/articles/security/blueprints/ffiec-analytics-overview.md b/articles/security/blueprints/ffiec-analytics-overview.md index 18e69314c159f..bc5c1fa0d3c69 100644 --- a/articles/security/blueprints/ffiec-analytics-overview.md +++ b/articles/security/blueprints/ffiec-analytics-overview.md @@ -24,7 +24,7 @@ Achieving FFIEC-compliance requires that qualified auditors certify a production This Azure Security and Compliance Blueprint provides an analytics platform upon which customers can build their own analytics tools. The reference architecture outlines a generic use case where customers input data either through bulk data imports by the SQL/Data Administrator or through operational data updates via an Operational User. Both work streams incorporate Azure Functions for importing data into Azure SQL Database. Azure Functions must be configured by the customer through the Azure portal to handle the import tasks unique to each customer's own analytics requirements. -Azure offers a variety of reporting and analytics services for the customers. This solution incorporates Azure Machine Learning services in conjunction with Azure SQL Database to rapidly browse through data and deliver faster results through smarter modeling. Azure Machine Learning increases query speeds by discovering new relationships between datasets. Once the data has been trained through several statistical functions, up to 7 additional query pools (8 total including the customer server) can be synchronized with the same tabular models to spread query workloads and reduce response times. +Azure offers a variety of reporting and analytics services for the customers. This solution incorporates Azure Machine Learning in conjunction with Azure SQL Database to rapidly browse through data and deliver faster results through smarter modeling. Azure Machine Learning increases query speeds by discovering new relationships between datasets. Once the data has been trained through several statistical functions, up to 7 additional query pools (8 total including the customer server) can be synchronized with the same tabular models to spread query workloads and reduce response times. For enhanced analytics and reporting, Azure SQL databases can be configured with columnstore indexes. Both Azure Machine Learning and Azure SQL databases can be scaled up or down or shut off completely in response to customer usage. All SQL traffic is encrypted with SSL through the inclusion of self-signed certificates. As a best practice, Azure recommends the use of a trusted certificate authority for enhanced security. @@ -68,7 +68,7 @@ The following section details the deployment and implementation elements. **Azure Functions**: [Azure Functions](https://docs.microsoft.com/azure/azure-functions/functions-overview) is a server-less compute service that enables users to run code on-demand without having to explicitly provision or manage infrastructure. Use Azure Functions to run a script or piece of code in response to a variety of events. -**Azure Machine Learning service**: +**Azure Machine Learning**: [Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/service/) is a data science technique that allows computers to use existing data to forecast future behaviors, outcomes, and trends. **Azure Data Catalog**: diff --git a/articles/security/blueprints/nist171-analytics-overview.md b/articles/security/blueprints/nist171-analytics-overview.md index 40e7edec2c84c..fd2095c717b2c 100644 --- a/articles/security/blueprints/nist171-analytics-overview.md +++ b/articles/security/blueprints/nist171-analytics-overview.md @@ -22,7 +22,7 @@ This reference architecture, associated implementation guide, and threat model a ## Architecture diagram and components This solution provides an analytics platform upon which customers can build their own analytics tools. The reference architecture outlines a generic use case. Customers can use it to input data through bulk data imports by the SQL/Data administrator. They also can use it to input data through operational data updates via an operational user. Both workstreams incorporate Azure Functions for importing data into Azure SQL Database. Azure Functions must be configured by the customer through the Azure portal to handle the import tasks unique to the customer's analytics requirements. -Azure offers a variety of reporting and analytics services for the customer. This solution uses Azure Machine Learning services and SQL Database to rapidly browse through data and deliver quicker results through smarter modeling of data. Machine Learning is intended to increase query speeds by discovering new relationships between datasets. Initially, data is trained through several statistical functions. Afterward, up to seven additional query pools can be synchronized with the same tabular models to spread query workload and reduce response times. The customer server brings the total of query pools to eight. +Azure offers a variety of reporting and analytics services for the customer. This solution uses Azure Machine Learnings and SQL Database to rapidly browse through data and deliver quicker results through smarter modeling of data. Machine Learning is intended to increase query speeds by discovering new relationships between datasets. Initially, data is trained through several statistical functions. Afterward, up to seven additional query pools can be synchronized with the same tabular models to spread query workload and reduce response times. The customer server brings the total of query pools to eight. For enhanced analytics and reporting, SQL Database can be configured with column store indexes. Machine Learning and SQL Database can be scaled up or down or shut off completely in response to customer usage. All SQL traffic is encrypted with SSL through the inclusion of self-signed certificates. As a best practice, we recommend the use of a trusted certificate authority for enhanced security. @@ -65,7 +65,7 @@ With [Event Grid](https://docs.microsoft.com/azure/event-grid/overview), custome **Azure Functions**: [Azure Functions](https://docs.microsoft.com/azure/azure-functions/functions-overview) is a serverless compute service that runs code on-demand. You don't have to explicitly provision or manage infrastructure. Use Azure Functions to run a script or piece of code in response to a variety of events. -**Azure Machine Learning service**: +**Azure Machine Learning**: [Machine Learning](https://docs.microsoft.com/azure/machine-learning/service/) is a data science technique that allows computers to use existing data to forecast future behaviors, outcomes, and trends. **Azure Data Catalog**: diff --git a/articles/security/blueprints/pcidss-analytics-overview.md b/articles/security/blueprints/pcidss-analytics-overview.md index 6b7fd90e98193..da8a516f4d471 100644 --- a/articles/security/blueprints/pcidss-analytics-overview.md +++ b/articles/security/blueprints/pcidss-analytics-overview.md @@ -24,7 +24,7 @@ Achieving PCI DSS-compliance requires that an accredited Qualified Security Asse This Azure Security and Compliance Blueprint provides an analytics platform upon which customers can build their own analytics tools. The reference architecture outlines a generic use case where customers input data either through bulk data imports by the SQL/Data Administrator or through operational data updates via an Operational User. Both work streams incorporate Azure Functions for importing data into Azure SQL Database. Azure Functions must be configured by the customer through the Azure portal to handle the import tasks unique to each customer's own analytics requirements. -Azure offers a variety of reporting and analytics services for the customers. This solution incorporates Azure Machine Learning services in conjunction with Azure SQL Database to rapidly browse through data and deliver faster results through smarter modeling. Azure Machine Learning increases query speeds by discovering new relationships between datasets. Once the data has been trained through several statistical functions, up to 7 additional query pools (8 total including the customer server) can be synchronized with the same tabular models to spread query workloads and reduce response times. +Azure offers a variety of reporting and analytics services for the customers. This solution incorporates Azure Machine Learning in conjunction with Azure SQL Database to rapidly browse through data and deliver faster results through smarter modeling. Azure Machine Learning increases query speeds by discovering new relationships between datasets. Once the data has been trained through several statistical functions, up to 7 additional query pools (8 total including the customer server) can be synchronized with the same tabular models to spread query workloads and reduce response times. For enhanced analytics and reporting, Azure SQL databases can be configured with columnstore indexes. Both Azure Machine Learning and Azure SQL databases can be scaled up or down or shut off completely in response to customer usage. All SQL traffic is encrypted with SSL through the inclusion of self-signed certificates. As a best practice, Azure recommends the use of a trusted certificate authority for enhanced security. @@ -68,7 +68,7 @@ The following section details the deployment and implementation elements. **Azure Functions**: [Azure Functions](https://docs.microsoft.com/azure/azure-functions/functions-overview) is a server-less compute service that enables users to run code on-demand without having to explicitly provision or manage infrastructure. Use Azure Functions to run a script or piece of code in response to a variety of events. -**Azure Machine Learning service**: +**Azure Machine Learning**: [Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/service/) is a data science technique that allows computers to use existing data to forecast future behaviors, outcomes, and trends. **Azure Data Catalog**: diff --git a/articles/security/fundamentals/encryption-atrest.md b/articles/security/fundamentals/encryption-atrest.md index 6533a915aa96b..c42415040df3f 100644 --- a/articles/security/fundamentals/encryption-atrest.md +++ b/articles/security/fundamentals/encryption-atrest.md @@ -259,7 +259,7 @@ Client-side encryption of Azure SQL Database data is supported through the [Alwa | | **Server-Side Using Service-Managed Key** | **Server-Side Using Customer-Managed Key** | **Client-Side Using Client-Managed** | | **AI and Machine Learning** | | | | | Azure Search | Yes | Preview | - | -| Azure Machine Learning Service | Yes | - | - | +| Azure Machine Learning | Yes | - | - | | Azure Machine Learning Studio | Yes | Preview, RSA 2048-bit | - | | Power BI | Yes | Preview, RSA 2048-bit | - | | **Analytics** | | | | diff --git a/articles/storage/common/storage-network-security.md b/articles/storage/common/storage-network-security.md index 75aad0ae25d7c..e34515af35893 100644 --- a/articles/storage/common/storage-network-security.md +++ b/articles/storage/common/storage-network-security.md @@ -366,7 +366,7 @@ If you enable the **Allow trusted Microsoft services...** exception, the followi | Azure Event Hubs | Microsoft.EventHub | Archive data with Event Hubs Capture. [Learn More](/azure/event-hubs/event-hubs-capture-overview). | | Azure File Sync | Microsoft.StorageSync | Enables you to transform your on-prem file server to a cache for Azure File shares. Allowing for multi-site sync, fast disaster-recovery, and cloud-side backup. [Learn more](../files/storage-sync-files-planning.md) | | Azure HDInsight | Microsoft.HDInsight | Provision the initial contents of the default file system for a new HDInsight cluster. [Learn more](https://azure.microsoft.com/blog/enhance-hdinsight-security-with-service-endpoints/). | -| Azure Machine Learning Service | Microsoft.MachineLearningServices | Authorized Azure Machine Learning workspaces write experiment output, models, and logs to Blob storage. [Learn more](/azure/machine-learning/service/how-to-enable-virtual-network#use-a-storage-account-for-your-workspace). +| Azure Machine Learning | Microsoft.MachineLearningServices | Authorized Azure Machine Learning workspaces write experiment output, models, and logs to Blob storage. [Learn more](/azure/machine-learning/service/how-to-enable-virtual-network#use-a-storage-account-for-your-workspace). | Azure Monitor | Microsoft.Insights | Allows writing of monitoring data to a secured storage account [Learn more](/azure/monitoring-and-diagnostics/monitoring-roles-permissions-security). | | Azure Networking | Microsoft.Network | Store and analyze network traffic logs. [Learn more](/azure/network-watcher/network-watcher-packet-capture-overview). | | Azure Site Recovery | Microsoft.SiteRecovery | Configure disaster recovery by enabling replication for Azure IaaS virtual machines. This is required if you are using firewall enabled cache storage account or source storage account or target storage account. [Learn more](https://docs.microsoft.com/azure/site-recovery/azure-to-azure-tutorial-enable-replication). | diff --git a/articles/stream-analytics/stream-analytics-scale-with-machine-learning-functions.md b/articles/stream-analytics/stream-analytics-scale-with-machine-learning-functions.md index 845ed157a1096..467ec6a5cc0f2 100644 --- a/articles/stream-analytics/stream-analytics-scale-with-machine-learning-functions.md +++ b/articles/stream-analytics/stream-analytics-scale-with-machine-learning-functions.md @@ -29,7 +29,7 @@ There are two parameters to configure the Machine Learning function used by your To determine the appropriate values for SUs, decide whether you would like to optimize latency of the Stream Analytics job or the throughput of each SU. SUs may always be added to a job to increase the throughput of a well-partitioned Stream Analytics query. Additional SUs do increase the cost of running the job. Determine the latency *tolerance* for your Stream Analytics job. -Increasing the batch size will increase the latency of your Azure Machine Learning service requests and the latency of the Stream Analytics job. +Increasing the batch size will increase the latency of your Azure Machine Learning requests and the latency of the Stream Analytics job. Increasing the batch size allows the Stream Analytics job to process **more events** with the **same number** of Machine Learning web service requests. The increase of Machine Learning web service latency is usually sublinear to the increase of batch size. diff --git a/includes/aml-delete-resource-group.md b/includes/aml-delete-resource-group.md index 59911e229d8f7..b769d0a81bead 100644 --- a/includes/aml-delete-resource-group.md +++ b/includes/aml-delete-resource-group.md @@ -11,7 +11,7 @@ ms.date: 12/04/2018 --- >[!IMPORTANT] ->The resources you created can be used as prerequisites to other Azure Machine Learning service tutorials and how-to articles. +>The resources you created can be used as prerequisites to other Azure Machine Learning tutorials and how-to articles. If you don't plan to use the resources you created, delete them, so you don't incur any charges: diff --git a/includes/aml-ui-cleanup.md b/includes/aml-ui-cleanup.md index 60feabaa9713a..34327949b356c 100644 --- a/includes/aml-ui-cleanup.md +++ b/includes/aml-ui-cleanup.md @@ -11,7 +11,7 @@ ms.date: 05/06/2019 --- >[!IMPORTANT] ->You can use the resources that you created as prerequisites for other Azure Machine Learning service tutorials and how-to articles. +>You can use the resources that you created as prerequisites for other Azure Machine Learning tutorials and how-to articles. ### Delete everything diff --git a/markdown templates/aml-templates/template-concepts.md b/markdown templates/aml-templates/template-concepts.md index c306af2e1bcc5..79d426b310f8f 100644 --- a/markdown templates/aml-templates/template-concepts.md +++ b/markdown templates/aml-templates/template-concepts.md @@ -1,5 +1,5 @@ --- -title: CONCEPT SUCH AS Web Services in Azure Machine Learning service in 59 chars or less. Include the name Azure Machine Learning. Test title here https://moz.com/learn/seo/title-tag +title: CONCEPT SUCH AS Web Services in Azure Machine Learning in 59 chars or less. Include the name Azure Machine Learning. Test title here https://moz.com/learn/seo/title-tag description: This string describes the article in 115 to 145 characters. Use SEO kind of action verbs here. such as - Learn how to do this and that using customer words. This info is displayed on the search page inline with the article date stamp. If your intro para describes your article's intent, you can use it here edited for length. services: machine-learning ms.service: machine-learning diff --git a/markdown templates/aml-templates/template-quickstart.md b/markdown templates/aml-templates/template-quickstart.md index 3c39242f2c349..4d0c41784170a 100644 --- a/markdown templates/aml-templates/template-quickstart.md +++ b/markdown templates/aml-templates/template-quickstart.md @@ -1,5 +1,5 @@ --- -title: Verb action quickstart for Azure Machine Learning in 59 chars or less. Include the name Azure Machine Learning. Test title here https://moz.com/learn/seo/title-tag [Example - Create an Azure Machine Learning service account and get started] +title: Verb action quickstart for Azure Machine Learning in 59 chars or less. Include the name Azure Machine Learning. Test title here https://moz.com/learn/seo/title-tag [Example - Create an Azure Machine Learning account and get started] description: This string describes the article in 115 to 145 characters. Use SEO kind of action verbs here. such as - Learn how to do this and that using customer words. This info is displayed on the search page inline with the article date stamp. If your intro para describes your article's intent, you can use it here edited for length. services: machine-learning ms.service: machine-learning