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articles/app-service/environment/management-addresses.md

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ms.workload: na
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ms.tgt_pltfrm: na
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ms.topic: article
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ms.date: 07/25/2019
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ms.date: 11/13/2019
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ms.author: ccompy
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ms.custom: seodec18
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---
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| Region | Addresses |
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|--------|-----------|
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| All public regions | 13.64.115.203, 13.66.140.0, 13.67.8.128, 13.69.64.128, 13.69.227.128, 13.70.73.128, 13.71.170.64, 13.71.194.129, 13.75.127.117, 13.77.50.128, 13.89.171.0, 13.94.141.115, 13.94.143.126, 13.94.149.179, 20.36.106.128, 20.36.114.64, 23.100.226.236, 23.102.135.246, 23.102.188.65, 40.69.106.128, 40.70.146.128, 40.71.13.64, 40.74.100.64, 40.78.194.128, 40.79.130.64, 40.83.120.64, 40.83.121.56, 40.83.125.161, 40.90.240.166, 40.91.126.196, 40.112.242.192, 40.119.4.111, 40.124.47.188, 51.140.146.64, 51.140.210.128, 52.151.25.45, 52.162.80.89, 52.162.106.192, 52.165.152.214, 52.165.153.122, 52.165.154.193, 52.165.158.140, 52.174.22.21, 52.178.177.147, 52.178.184.149, 52.178.190.65, 52.178.195.197, 52.187.56.50, 52.187.59.251, 52.187.63.19, 52.187.63.37, 52.224.105.172, 52.225.177.153, 52.231.18.64, 52.231.146.128, 65.52.14.230, 65.52.172.237, 65.52.193.203, 70.37.57.58, 70.37.89.222, 104.43.242.137, 104.44.129.141, 104.44.129.243, 104.44.129.255, 104.44.134.255, 104.208.54.11, 104.211.81.64, 104.211.146.128, 104.214.49.0, 157.55.176.93, 157.55.208.185, 191.233.203.64, 191.236.154.88 |
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| All public regions | 13.64.115.203, 13.66.140.0, 13.67.8.128, 13.69.64.128, 13.69.227.128, 13.70.73.128, 13.71.170.64, 13.71.194.129, 13.75.34.192, 13.75.127.117, 13.77.50.128, 13.78.109.0, 13.89.171.0, 13.94.141.115, 13.94.143.126, 13.94.149.179, 20.36.106.128, 20.36.114.64, 23.100.226.236, 23.102.135.246, 23.102.188.65, 40.69.106.128, 40.70.146.128, 40.71.13.64, 40.74.100.64, 40.78.194.128, 40.79.130.64, 40.79.178.128, 40.83.120.64, 40.83.121.56, 40.83.125.161, 40.90.240.166, 40.91.126.196, 40.112.242.192, 40.119.4.111, 40.124.47.188, 51.140.146.64, 51.140.210.128, 52.151.25.45, 52.162.80.89, 52.162.106.192, 52.165.152.214, 52.165.153.122, 52.165.154.193, 52.165.158.140, 52.174.22.21, 52.178.177.147, 52.178.184.149, 52.178.190.65, 52.178.195.197, 52.187.56.50, 52.187.59.251, 52.187.63.19, 52.187.63.37, 52.224.105.172, 52.225.177.153, 52.231.18.64, 52.231.146.128, 65.52.14.230, 65.52.172.237, 65.52.193.203, 70.37.57.58, 70.37.89.222, 104.43.242.137, 104.44.129.141, 104.44.129.243, 104.44.129.255, 104.44.134.255, 104.208.54.11, 104.211.81.64, 104.211.146.128, 104.214.49.0, 157.55.176.93, 157.55.208.185, 191.233.203.64, 191.236.154.88 |
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| Microsoft Azure Government | 23.97.29.209, 13.72.53.37, 13.72.180.105, 23.97.0.17, 23.97.16.184 |
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## Configuring a Network Security Group
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$rg = "resource group name"
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$rt = "route table name"
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$location = "azure location"
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$managementAddresses = "13.64.115.203", "13.66.140.0", "13.67.8.128", "13.69.64.128", "13.69.227.128", "13.70.73.128", "13.71.170.64", "13.71.194.129", "13.75.127.117", "13.77.50.128", "13.89.171.0", "13.94.141.115", "13.94.143.126", "13.94.149.179", "20.36.106.128", "20.36.114.64", "23.100.226.236", "23.102.135.246", "23.102.188.65", "40.69.106.128", "40.70.146.128", "40.71.13.64", "40.74.100.64", "40.78.194.128", "40.79.130.64", "40.83.120.64", "40.83.121.56", "40.83.125.161", "40.90.240.166", "40.91.126.196", "40.112.242.192", "40.119.4.111", "40.124.47.188", "51.140.146.64", "51.140.210.128", "52.151.25.45", "52.162.80.89", "52.162.106.192", "52.165.152.214", "52.165.153.122", "52.165.154.193", "52.165.158.140", "52.174.22.21", "52.178.177.147", "52.178.184.149", "52.178.190.65", "52.178.195.197", "52.187.56.50", "52.187.59.251", "52.187.63.19", "52.187.63.37", "52.224.105.172", "52.225.177.153", "52.231.18.64", "52.231.146.128", "65.52.14.230", "65.52.172.237", "65.52.193.203", "70.37.57.58", "70.37.89.222", "104.43.242.137", "104.44.129.141", "104.44.129.243", "104.44.129.255", "104.44.134.255", "104.208.54.11", "104.211.81.64", "104.211.146.128", "104.214.49.0", "157.55.176.93", "157.55.208.185", "191.233.203.64", "191.236.154.88"
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$managementAddresses = "13.64.115.203", "13.66.140.0", "13.67.8.128", "13.69.64.128", "13.69.227.128", "13.70.73.128", "13.71.170.64", "13.71.194.129", "13.75.34.192", "13.75.127.117", "13.77.50.128", "13.78.109.0", "13.89.171.0", "13.94.141.115", "13.94.143.126", "13.94.149.179", "20.36.106.128", "20.36.114.64", "23.100.226.236", "23.102.135.246", "23.102.188.65", "40.69.106.128", "40.70.146.128", "40.71.13.64", "40.74.100.64", "40.78.194.128", "40.79.130.64", "40.79.178.128", "40.83.120.64", "40.83.121.56", "40.83.125.161", "40.90.240.166", "40.91.126.196", "40.112.242.192", "40.119.4.111", "40.124.47.188", "51.140.146.64", "51.140.210.128", "52.151.25.45", "52.162.80.89", "52.162.106.192", "52.165.152.214", "52.165.153.122", "52.165.154.193", "52.165.158.140", "52.174.22.21", "52.178.177.147", "52.178.184.149", "52.178.190.65", "52.178.195.197", "52.187.56.50", "52.187.59.251", "52.187.63.19", "52.187.63.37", "52.224.105.172", "52.225.177.153", "52.231.18.64", "52.231.146.128", "65.52.14.230", "65.52.172.237", "65.52.193.203", "70.37.57.58", "70.37.89.222", "104.43.242.137", "104.44.129.141", "104.44.129.243", "104.44.129.255", "104.44.134.255", "104.208.54.11", "104.211.81.64", "104.211.146.128", "104.214.49.0", "157.55.176.93", "157.55.208.185", "191.233.203.64", "191.236.154.88"
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az network route-table create --name $rt --resource-group $rg --location $location
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foreach ($ip in $managementAddresses) {

articles/app-service/manage-scale-up.md

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like dedicated virtual machines (VMs), custom domains and certificates, staging slots, autoscaling, and more. You scale up by changing the pricing tier of the
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App Service plan that your app belongs to.
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* [Scale out](https://en.wikipedia.org/wiki/Scalability#Horizontal_and_vertical_scaling): Increase the number of VM instances that run your app.
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You can scale out to as many as 20 instances, depending on your pricing tier. [App Service Environments](environment/intro.md)
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You can scale out to as many as 30 instances, depending on your pricing tier. [App Service Environments](environment/intro.md)
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in **Isolated** tier further increases your scale-out count to 100 instances. For more information about scaling out, see
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[Scale instance count manually or automatically](../monitoring-and-diagnostics/insights-how-to-scale.md). There, you find out how
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to use autoscaling, which is to scale instance count automatically based on predefined rules and schedules.

articles/azure-app-configuration/quickstart-feature-flag-aspnet-core.md

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webBuilder.ConfigureAppConfiguration((hostingContext, config) =>
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{
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var settings = config.Build();
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config.AddAzureAppConfiguration(settings["ConnectionStrings:AppConfig"])
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.UseFeatureFlags();
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config.AddAzureAppConfiguration(options => {
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options.Connect(settings["ConnectionStrings:AppConfig"])
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.UseFeatureFlags();
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});
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})
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.UseStartup<Startup>());
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```

articles/azure-monitor/insights/vminsights-enable-at-scale-powershell.md

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- The scoped resource group that's specified by *ResourceGroup*.
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- A single VM or virtual machine scale set that's specified by *Name*.
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For each VM or virtual machine scale set, the script verifies whether the VM extension is already installed. If the VM extension isn't installed, the script tries to reinstall it. If the VM extension is installed, the script installs the Log Analytics and Dependency agent VM extensions.
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For each VM or virtual machine scale set, the script verifies whether the VM extension is already installed. If the VM extension is installed, the script tries to reinstall it. If the VM extension isn't installed, the script installs the Log Analytics and Dependency agent VM extensions.
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Verify you are using Azure PowerShell module Az version 1.0.0 or later with `Enable-AzureRM` compatibility aliases enabled. Run `Get-Module -ListAvailable Az` to find the version. If you need to upgrade, see [Install Azure PowerShell module](https://docs.microsoft.com/powershell/azure/install-az-ps). If you're running PowerShell locally, you also need to run `Connect-AzAccount` to create a connection with Azure.
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articles/data-factory/transform-data.md

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> * [.NET custom](transform-data-using-dotnet-custom-activity.md)
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## Overview
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This article explains data transformation activities in Azure Data Factory that you can use to transform and processes your raw data into predictions and insights at scale. A transformation activity executes in a computing environment such as Azure Databricks or Azure HDInsight. It provides links to articles with detailed information on each transformation activity.
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This article explains data transformation activities in Azure Data Factory that you can use to transform and process your raw data into predictions and insights at scale. A transformation activity executes in a computing environment such as Azure Databricks or Azure HDInsight. It provides links to articles with detailed information on each transformation activity.
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Data Factory supports the following data transformation activities that can be added to [pipelines](concepts-pipelines-activities.md) either individually or chained with another activity.
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articles/databox/data-box-disk-deploy-picked-up.md

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### [In Singapore](#tab/in-singapore)
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1. Print the shipping label and attach onto the box. If the label is damaged or lost:
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1. Print the shipping label and attach it to the box. If the label is damaged or lost:
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- Go to **Overview > Download shipping label** and get a return shipping label.
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- Affix the label on the device. Make sure that the label is visible.
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2. To request pickup:
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- Call **SingPost** hotline at **6845 6485** during office hours (9am to 5pm, Monday to Friday).
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- Quote *Microsoft Azure pickup* and the service request number (tracking number on the return shipping label) to arrange for a collection.
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- You will get a verbal confirmation for the pickup schedule.
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- If the courier does not arrive for collection, call **SingPost** at **6845 6485** for alternate arrangements.
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2. To request pickup, email SingPost Customer Service by using the following template with the tracking number (the tracking number can be found on the return label provided in the delivered package).
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```
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Subject: Microsoft Azure Pick-up - XZ00001234567
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Body:
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a. Requestor name
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b. Requestor contact number
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c. Requestor collection address
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d. Preferred collection date
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```
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> [!NOTE]
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> For booking requests received on a business day:
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> - Before 3 PM, pickup will be the next business day between 9 AM and 1 PM.
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> - After 3 PM, pickup will be the next business day between 2 PM to 6 PM.
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If you encounter any issues, kindly reach out to Data Box Operations Asia at [email protected]. Provide the job name in the subject header and the issue encountered.
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articles/machine-learning/service/how-to-deploy-and-where.md

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## (Preview) No-code model deployment
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No-code model deployment is currently in preview and supports the following machine learning frameworks:
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### Tensorflow SavedModel format
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```python
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from azureml.core import Model
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model = Model.register(workspace=ws,
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model_name='flowers', # Name of the registered model in your workspace.
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model_path='./flowers_model', # Local Tensorflow SavedModel folder to upload and register as a model.
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model_framework=Model.Framework.TENSORFLOW, # Framework used to create the model.
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model_framework_version='1.14.0', # Version of Tensorflow used to create the model.
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description='Flowers model')
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service_name = 'tensorflow-flower-service'
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```
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ONNX model registration and deployment is supported for any ONNX inference graph. Preprocess and postprocess steps are not currently supported.
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model = Model.register(workspace=ws,
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model_name='mnist-sample', # Name of the registered model in your workspace.
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model_path='mnist-model.onnx', # Local ONNX model to upload and register as a model.
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model_framework=Model.Framework.ONNX , # Framework used to create the model.
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model_framework_version='1.3', # Version of ONNX used to create the model.
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description='Onnx MNIST model')
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### Scikit-learn models
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No code model deployment is supported for all built-in scikit-learn model types.
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from azureml.core import Model
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from azureml.core.resource_configuration import ResourceConfiguration
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model = Model.register(workspace=ws,
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model_name='my-sklearn-model', # Name of the registered model in your workspace.
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model_path='./sklearn_regression_model.pkl', # Local file to upload and register as a model.
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model_framework=Model.Framework.SCIKITLEARN, # Framework used to create the model.
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model_framework_version='0.19.1', # Version of scikit-learn used to create the model.
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resource_configuration=ResourceConfiguration(cpu=1, memory_in_gb=0.5),
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description='Ridge regression model to predict diabetes progression.',
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tags={'area': 'diabetes', 'type': 'regression'})
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service = Model.deploy(ws, service_name, [model])
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- inference-schema[numpy-support]
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- numpy
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articles/machine-learning/service/how-to-deploy-azure-kubernetes-service.md

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> [!IMPORTANT]
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> Cluter scaling is not provided through the Azure Machine Learning SDK. For more information on scaling the nodes in an AKS cluster, see [Scale the node count in an AKS cluster](../../aks/scale-cluster.md).
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> Cluster scaling is not provided through the Azure Machine Learning SDK. For more information on scaling the nodes in an AKS cluster, see [Scale the node count in an AKS cluster](../../aks/scale-cluster.md).
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When deploying to Azure Kubernetes Service, you deploy to an AKS cluster that is __connected to your workspace__. There are two ways to connect an AKS cluster to your workspace:
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articles/migrate/tutorial-prepare-physical.md

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Continue to the next tutorial to create an Azure Migrate project, and assess physical servers for migration to Azure
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> [!div class="nextstepaction"]
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> [Assess physical servers](./tutorial-assess-physical.md)
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> [Assess physical servers](./tutorial-assess-physical.md)

articles/storage/files/storage-how-to-use-files-linux.md

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```
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On older versions of **Red Hat Enterprise Linux** and **CentOS**, use the `dnf` package manager:
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```bash
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sudo yum install cifs-utils
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* [Planning for an Azure Files deployment](storage-files-planning.md)
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* [FAQ](../storage-files-faq.md)
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* [Troubleshooting](storage-troubleshoot-linux-file-connection-problems.md)
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* [Troubleshooting](storage-troubleshoot-linux-file-connection-problems.md)

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