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Copy file name to clipboardexpand all lines: articles/cognitive-services/QnAMaker/Tutorials/integrate-qnamaker-luis.md
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While it is straightforward to create multiple knowledge bases in QnA Maker, you will need some logic to route the incoming question to the appropriate knowledge base. You can do this by using LUIS.
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This article uses Bot Framework v3 SDK. Please see this [Bot Framework article](https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-tutorial-dispatch?view=azure-bot-service-4.0&tabs=csharp), if you are interested in Bot Framework v4 SDK version of this information.
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This article uses Bot Framework v3 SDK. Please see this [Bot Framework article](https://docs.microsoft.com/azure/bot-service/bot-builder-tutorial-dispatch?view=azure-bot-service-4.0&tabs=csharp), if you are interested in Bot Framework v4 SDK version of this information.
Copy file name to clipboardexpand all lines: articles/cognitive-services/Speech-Service/quickstart-csharp-dotnet-windows.md
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Use this guide to create a speech-to-text console application using the .NET framework for Windows and the Speech SDK. When finished, you can use your computer's microphone to transcribe speech to text in real time.
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This quickstart requires an [Azure Cognitive Services account](https://docs.microsoft.com/azure/cognitive-services/cognitive-services-apis-create-account) with Microsoft Speech enabled. If you don't have an account, you can use the [free trial](https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/get-started) to get a subscription key.
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This quickstart requires an [Azure Cognitive Services account](https://docs.microsoft.com/azure/cognitive-services/cognitive-services-apis-create-account) with Microsoft Speech enabled. If you don't have an account, you can use the [free trial](https://docs.microsoft.com/azure/cognitive-services/speech-service/get-started) to get a subscription key.
Copy file name to clipboardexpand all lines: articles/cognitive-services/Translator/custom-translator/overview.md
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Translation systems built with [Custom Translator](https://portal.customtranslator.azure.ai) are available through the same cloud-based, [secure](https://cognitive.uservoice.com/knowledgebase/articles/1147537-api-and-customization-confidentiality), high performance, highly scalable Microsoft Translator [Text API V3](https://docs.microsoft.com/azure/cognitive-services/translator/reference/v3-0-translate?tabs=curl), that powers billions of translations every day.
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Custom Translator supports more than three dozen languages, and maps directly to the languages available for NMT. For a complete list, see [Microsoft Translator Languages](https://docs.microsoft.com/en-us/azure/cognitive-services/translator/language-support).
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Custom Translator supports more than three dozen languages, and maps directly to the languages available for NMT. For a complete list, see [Microsoft Translator Languages](https://docs.microsoft.com/azure/cognitive-services/translator/language-support).
|`gatk4-promo` is not enabled for your account. For more information, see https://docs.microsoft.com/en-us/azure/genomics/frequently-asked-questions-genomics| You are trying to run GATK4 workflows with the Microsoft Genomics service without being activated. | Please visit [here](https://aka.ms/msgatk4) to activate your account. Note that the trial expires at the end of the calendar year 2018. You will not be able to activate your account for the promotional runs after this date. |
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| Thank you for trying `gatk4-promo`.Your trial period has ended. For more information, https://docs.microsoft.com/en-us/azure/genomics/frequently-asked-questions-genomics| The GATK4 trial has expired at the end of the calendar year, and you are trying to invoke the `gatk4-promo` process_name. | Switch the process_name parameter to, `gatk4`, instead of `gatk4-promo`. This is the official gatk4 version, and your workflow will be billed if you use this parameter. |
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| Thank you for trying `gatk4-promo` You have used all of your allocated runs. For more information, see https://docs.microsoft.com/en-us/azure/genomics/frequently-asked-questions-genomics| You have successfully submitted all of your 20 promotional runs for GATK4. | Submit any new gatk4 runs with process_name argument set to `gatk4` instead of `gatk4-promo`. Your workflow will be billed when you use this parameter. |
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|`gatk4-promo` is not enabled for your account. For more information, see https://docs.microsoft.com/azure/genomics/frequently-asked-questions-genomics| You are trying to run GATK4 workflows with the Microsoft Genomics service without being activated. | Please visit [here](https://aka.ms/msgatk4) to activate your account. Note that the trial expires at the end of the calendar year 2018. You will not be able to activate your account for the promotional runs after this date. |
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| Thank you for trying `gatk4-promo`.Your trial period has ended. For more information, https://docs.microsoft.com/azure/genomics/frequently-asked-questions-genomics| The GATK4 trial has expired at the end of the calendar year, and you are trying to invoke the `gatk4-promo` process_name. | Switch the process_name parameter to, `gatk4`, instead of `gatk4-promo`. This is the official gatk4 version, and your workflow will be billed if you use this parameter. |
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| Thank you for trying `gatk4-promo` You have used all of your allocated runs. For more information, see https://docs.microsoft.com/azure/genomics/frequently-asked-questions-genomics| You have successfully submitted all of your 20 promotional runs for GATK4. | Submit any new gatk4 runs with process_name argument set to `gatk4` instead of `gatk4-promo`. Your workflow will be billed when you use this parameter. |
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## Can I run GATK4 workflows on Microsoft Genomics without signing up for the GATK4 promotion?
Copy file name to clipboardexpand all lines: articles/hdinsight/domain-joined/apache-domain-joined-configure-using-azure-adds.md
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## Networking considerations
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> [!NOTE]
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> Azure AD-DS must be deployed in an Azure Resource Manager (ARM) based vNET. Classic virtual networks are not supported for Azure AD-DS. Please refer to [Enable Azure Active Directory Domain Services using the Azure portal](https://docs.microsoft.com/en-us/azure/active-directory-domain-services/active-directory-ds-getting-started-network) for more details.
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> Azure AD-DS must be deployed in an Azure Resource Manager (ARM) based vNET. Classic virtual networks are not supported for Azure AD-DS. Please refer to [Enable Azure Active Directory Domain Services using the Azure portal](https://docs.microsoft.com/azure/active-directory-domain-services/active-directory-ds-getting-started-network) for more details.
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After you enable Azure AD-DS, a local Domain Name Service (DNS) server runs on the AD Virtual Machines (VMs). Configure your Azure AD-DS Virtual Network (VNET) to use these custom DNS servers. To locate the right IP addresses, select **Properties** under the **Manage** category and look at the IP Addresses listed beneath **IP Address on Virtual Network**.
Copy file name to clipboardexpand all lines: articles/hdinsight/hdinsight-hadoop-create-linux-clusters-portal.md
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* Click **Next**.
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4. In **Security and Networking**, you can connect your cluster to a virtual network using the provided dropdown. Select an Azure virtual network and the subnet if you want to place the cluster into a virtual network. For information on using HDInsight with a Virtual Network, including specific configuration requirements for the Virtual Network, see [Extend HDInsight capabilities by using an Azure Virtual Network](hdinsight-extend-hadoop-virtual-network.md). If you want to use the **Enterprise Security Package**, you can also follow instructions here: [Configure a HDInsight cluster with Enterprise Security Package by using Azure Active Directory Domain Services](https://docs.microsoft.com/en-us/azure/hdinsight/domain-joined/apache-domain-joined-configure-using-azure-adds).
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4. In **Security and Networking**, you can connect your cluster to a virtual network using the provided dropdown. Select an Azure virtual network and the subnet if you want to place the cluster into a virtual network. For information on using HDInsight with a Virtual Network, including specific configuration requirements for the Virtual Network, see [Extend HDInsight capabilities by using an Azure Virtual Network](hdinsight-extend-hadoop-virtual-network.md). If you want to use the **Enterprise Security Package**, you can also follow instructions here: [Configure a HDInsight cluster with Enterprise Security Package by using Azure Active Directory Domain Services](https://docs.microsoft.com/azure/hdinsight/domain-joined/apache-domain-joined-configure-using-azure-adds).
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5. For **Storage**, specify whether you want Azure Storage (WASB) or Data Lake Storage as your default storage. Look at the table below for more information.
Copy file name to clipboardexpand all lines: articles/hdinsight/hdinsight-hadoop-install-presto.md
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In this document, you learn how to install Presto on HDInsight Hadoop clusters by using Script Action. You also learn how to install Airpal on an existing Presto HDInsight cluster.
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HDInsight also offers the Starburst Presto application for Apache Hadoop clusters. For more information, see [Install third-party applications on Azure HDInsight](https://docs.microsoft.com/en-us/azure/hdinsight/hdinsight-apps-install-applications)
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HDInsight also offers the Starburst Presto application for Apache Hadoop clusters. For more information, see [Install third-party applications on Azure HDInsight](https://docs.microsoft.com/azure/hdinsight/hdinsight-apps-install-applications)
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> [!IMPORTANT]
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> The steps in this document require an **HDInsight 3.5 Hadoop cluster** that uses Linux. Linux is the only operating system used on HDInsight version 3.4 or greater. For more information, see [HDInsight versions](hdinsight-component-versioning.md).
Copy file name to clipboardexpand all lines: articles/hdinsight/spark/apache-spark-python-package-installation.md
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There are two types of open-source components that are available in the HDInsight service:
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***Built-in components** - These components are pre-installed on HDInsight clusters and provide core functionality of the cluster. For example, YARN ResourceManager, the Hive query language (HiveQL), and the Mahout library belong to this category. A full list of cluster components is available in [What's new in the Hadoop cluster versions provided by HDInsight](https://docs.microsoft.com/en-us/azure/hdinsight/hdinsight-component-versioning).
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***Built-in components** - These components are pre-installed on HDInsight clusters and provide core functionality of the cluster. For example, YARN ResourceManager, the Hive query language (HiveQL), and the Mahout library belong to this category. A full list of cluster components is available in [What's new in the Hadoop cluster versions provided by HDInsight](https://docs.microsoft.com/azure/hdinsight/hdinsight-component-versioning).
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***Custom components** - You, as a user of the cluster, can install or use in your workload any component available in the community or created by you.
Copy file name to clipboardexpand all lines: articles/hdinsight/spark/apache-spark-settings.md
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Use the best Spark version for your cluster. The HDInsight service includes several versions of both Spark and HDInsight itself. Each version of Spark includes a set of default cluster settings.
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When you create a new cluster, there are multiple Spark versions to choose from. To see the full list, [HDInsight Components and Versions](https://docs.microsoft.com/en-us/azure/hdinsight/hdinsight-component-versioning)
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When you create a new cluster, there are multiple Spark versions to choose from. To see the full list, [HDInsight Components and Versions](https://docs.microsoft.com/azure/hdinsight/hdinsight-component-versioning)
For more information about the ModuleClient class and its communication methods, see the API reference for your preferred SDK language: [C#](https://docs.microsoft.com/dotnet/api/microsoft.azure.devices.client.moduleclient?view=azure-dotnet), [C and Python](https://docs.microsoft.com/en-us/azure/iot-hub/iot-c-sdk-ref/iothub-module-client-h), [Java](https://docs.microsoft.com/java/api/com.microsoft.azure.sdk.iot.device._module_client?view=azure-java-stable), or [Node.js](https://docs.microsoft.com/javascript/api/azure-iot-device/moduleclient?view=azure-node-latest).
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For more information about the ModuleClient class and its communication methods, see the API reference for your preferred SDK language: [C#](https://docs.microsoft.com/dotnet/api/microsoft.azure.devices.client.moduleclient?view=azure-dotnet), [C and Python](https://docs.microsoft.com/azure/iot-hub/iot-c-sdk-ref/iothub-module-client-h), [Java](https://docs.microsoft.com/java/api/com.microsoft.azure.sdk.iot.device._module_client?view=azure-java-stable), or [Node.js](https://docs.microsoft.com/javascript/api/azure-iot-device/moduleclient?view=azure-node-latest).
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The solution developer is responsible for specifying the rules that determine how Edge hub passes messages between modules. Routing rules are defined in the cloud and pushed down to Edge hub in its device twin. The same syntax for IoT Hub routes is used to define routes between modules in Azure IoT Edge.
Copy file name to clipboardexpand all lines: articles/machine-learning/service/concept-ml-pipelines.md
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With Azure Machine Learning, you can use various toolkits and frameworks such as Microsoft Cognitive Toolkit or TensorFlow for each step in your pipeline. Azure coordinates between the various [compute targets](concept-azure-machine-learning-architecture.md) you use so that your intermediate data can be shared with the downstream compute targets easily.
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You can [track the metrics for your pipeline experiments](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-track-experiments) directly in the Azure portal.
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You can [track the metrics for your pipeline experiments](https://docs.microsoft.com/azure/machine-learning/service/how-to-track-experiments) directly in the Azure portal.
Copy file name to clipboardexpand all lines: articles/machine-learning/service/how-to-consume-web-service.md
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Deploying an Azure Machine Learning model as a web service creates a REST API. You can send data to this API and receive the prediction returned by the model. In this document, learn how to create clients for the web service using C#, Go, Java, and Python.
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A web service is created when you deploy an image to an Azure Container Instance, Azure Kubernetes Service, or Project Brainwave (field programmable gate arrays). Images are created from registered models and scoring files. The URI used to access a web service can be retrieved using the [Azure Machine Learning SDK](https://docs.microsoft.com/en-us/python/api/overview/azure/ml/intro?view=azure-ml-py). If authentication is enabled, you can also use the SDK to get the authentication keys.
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A web service is created when you deploy an image to an Azure Container Instance, Azure Kubernetes Service, or Project Brainwave (field programmable gate arrays). Images are created from registered models and scoring files. The URI used to access a web service can be retrieved using the [Azure Machine Learning SDK](https://docs.microsoft.com/python/api/overview/azure/ml/intro?view=azure-ml-py). If authentication is enabled, you can also use the SDK to get the authentication keys.
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The general workflow when creating a client that uses an ML web service is:
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> [!NOTE]
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> The Azure Machine Learning SDK is used to get the web service information. This is a Python SDK. While it is used to retrieve information about the web services, you can use any language to create a client for the service.
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The web service connection information can be retrieved using the Azure Machine Learning SDK. The [azureml.core.Webservice](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice(class)?view=azure-ml-py) class provides the information needed to create a client. The following `Webservice` properties that are useful when creating a client application:
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The web service connection information can be retrieved using the Azure Machine Learning SDK. The [azureml.core.Webservice](https://docs.microsoft.com/python/api/azureml-core/azureml.core.webservice(class)?view=azure-ml-py) class provides the information needed to create a client. The following `Webservice` properties that are useful when creating a client application:
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*`auth_enabled` - If authentication is enabled, `True`; otherwise, `False`.
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*`scoring_uri` - The REST API address.
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print(service.scoring_uri)
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```
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* You can use `Webservice.list` to retrieve a list of deployed web services for models in your workspace. You can add filters to narrow the list of information returned. For more information on what can be filtered on, see the [Webservice.list](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice.webservice.webservice?view=azure-ml-py#list) reference documentation.
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* You can use `Webservice.list` to retrieve a list of deployed web services for models in your workspace. You can add filters to narrow the list of information returned. For more information on what can be filtered on, see the [Webservice.list](https://docs.microsoft.com/python/api/azureml-core/azureml.core.webservice.webservice.webservice?view=azure-ml-py#list) reference documentation.
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```python
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services= Webservice.list(ws)
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```
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> [!IMPORTANT]
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> If you need to regenerate a key, use [`service.regen_key`](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice(class)?view=azure-ml-py#regen-key).
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> If you need to regenerate a key, use [`service.regen_key`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.webservice(class)?view=azure-ml-py#regen-key).
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