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articles/active-directory/saas-apps/jamfprosamlconnector-tutorial.md

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articles/blockchain/service/manage-cli.md

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@@ -182,7 +182,7 @@ az role assignment create --role <role> --assignee <assignee> --scope /subscript
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|---------|-------------|
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| **role** | Name of the Azure AD role. |
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| **assignee** | Azure AD user ID. For example, `[email protected]` |
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| **scope** | Scope of the role assignment. Can be either a blockchain member of transaction node. |
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| **scope** | Scope of the role assignment. Can be either a blockchain member or transaction node. |
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**Example:**
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|---------|-------------|
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| **role** | Name of the Azure AD role. |
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| **assignee-object-id** | Azure AD group ID or application ID. |
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| **scope** | Scope of the role assignment. Can be either a blockchain member of transaction node. |
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| **scope** | Scope of the role assignment. Can be either a blockchain member or transaction node. |
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**Example:**
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|---------|-------------|
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| **role** | Name of the Azure AD role. |
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| **assignee** | Azure AD user ID. For example, `[email protected]` |
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| **scope** | Scope of the role assignment. Can be either a blockchain member of transaction node. |
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| **scope** | Scope of the role assignment. Can be either a blockchain member or transaction node. |
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## Next steps
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articles/machine-learning/index.yml

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linkLists:
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- linkListType: tutorial
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links:
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- text: Experiment with notebooks
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- text: Experiment with Python notebooks
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url: service/tutorial-1st-experiment-sdk-setup.md
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- text: Experiment in visual interface
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- text: Experiment through drag & drop
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url: service/ui-tutorial-automobile-price-train-score.md
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- text: Automate your first experiment
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url: service/tutorial-first-experiment-automated-ml.md
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url: service/how-to-set-up-training-targets.md
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# PYTHON
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- title: Train with Python
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- title: Experiment with Python SDK
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linkLists:
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- linkListType: tutorial
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links:
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- text: Create your first ML experiment
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url: service/tutorial-1st-experiment-sdk-setup.md
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- text: Train image classification models
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- text: Classify images
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url: service/tutorial-train-models-with-aml.md
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- text: Predict taxi fares with automated ML
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url: service/tutorial-auto-train-models.md
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url: service/how-to-train-keras.md
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# VISUAL INTERFACE
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- title: Train in visual interface
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- title: Experiment in visual interface
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linkLists:
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- linkListType: get-started
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- linkListType: tutorial
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links:
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- text: Train automated ML experiment
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- text: Use automated ML to predict subscriptions
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url: service/tutorial-first-experiment-automated-ml.md
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- text: Train in the visual interface
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- text: Predict automobile prices with linear regression
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url: service/ui-tutorial-automobile-price-train-score.md
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- linkListType: how-to-guide
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links:
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- text: 'Visual interface: predict car prices'
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url: service/ui-sample-regression-predict-automobile-price-basic.md
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- text: 'Visual interface: predict credit risk'
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- text: 'Predict credit risk'
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url: service/ui-sample-classification-predict-credit-risk-cost-sensitive.md
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- text: 'Visual interface: predict delays'
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- text: 'Predict delays'
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url: service/ui-sample-classification-predict-flight-delay.md
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- text: 'Visual interface: predict churn'
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- text: 'Predict churn'
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url: service/ui-sample-classification-predict-churn.md
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# MLOps

articles/machine-learning/service/overview-what-happened-to-workbench.md

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## What about the old SDK and CLI?
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Yes, they'll continue to work until January. See the preceding [timeline](#timeline). We recommend that you start creating your new experiments and models with the latest SDK or CLI.
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See the preceding [timeline](#timeline). We recommend that you start creating your new experiments and models with the latest SDK or CLI.
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By using the new Python SDK in the latest release, you can interact with Azure Machine Learning service in any Python environment. Learn how to install the latest <a href="https://docs.microsoft.com/python/api/overview/azure/ml/intro?view=azure-ml-py" target="_blank">SDK</a>. You can also use the updated [Azure Machine Learning CLI extension](reference-azure-machine-learning-cli.md) with the rich set of `az ml` commands to interact with the service in any command-line environment, including Azure Cloud Shell.
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articles/managed-applications/create-uidefinition-overview.md

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A current JSON schema for createUiDefinition is available here: https://schema.management.azure.com/schemas/0.1.2-preview/CreateUIDefinition.MultiVm.json.
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For an example user interface file, see [createUiDefinition.json](https://github.com/Azure/azure-managedapp-samples/blob/master/samples/201-managed-app-using-existing-vnet/createUiDefinition.json).
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For an example user interface file, see [createUiDefinition.json](https://github.com/Azure/azure-managedapp-samples/blob/master/Managed%20Application%20Sample%20Packages/201-managed-app-using-existing-vnet/createUiDefinition.json).

articles/managed-applications/sample-projects.md

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ms.topic: sample
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ms.tgt_pltfrm: na
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ms.workload: na
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ms.date: 01/02/2019
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ms.date: 09/04/2019
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ms.author: tomfitz
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---
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# Sample projects for Azure managed applications
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| --- | --- |
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| **Examples** | |
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| [Managed Application (Trial or Production) into a new or existing virtual network](https://github.com/Azure/azure-managedapp-samples/tree/master/samples/201-managed-app-using-existing-vnet) | Demonstrates how you can create flexible deployment options for customers. This managed application can be deployed to a new virtual network or an existing virtual network. Customers can specify either trial or production version of the managed applications. |
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| [Managed Azure Storage Account](https://github.com/Azure/azure-managedapp-samples/tree/master/samples/201-managed-storage-account) | Deploys a single storage account. Use this sample project as an introduction to creating managed applications. |
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| [Managed Service Fabric with Azure management services](https://github.com/Azure/azure-managedapp-samples/tree/master/samples/201-managed-service-fabric) | Deploys a service fabric cluster and virtual machine scale sets. Includes storage accounts for logging and diagnostics. |
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| [Managed Web Application (IaaS) with Azure management services](https://github.com/Azure/azure-managedapp-samples/tree/master/samples/201-managed-web-app) | Deploys a virtual machine that hosts a web application. |
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| [Managed SQL 2017 IaaS with automated patching and backup](https://github.com/Azure/azure-managedapp-samples/tree/master/samples/201-managed-sql-iaas) | Deploys a virtual machine that hosts SQL 2017. |
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| [Managed Application (Trial or Production) into a new or existing virtual network](https://github.com/Azure/azure-managedapp-samples/tree/master/Managed%20Application%20Sample%20Packages/201-managed-app-using-existing-vnet) | Demonstrates how you can create flexible deployment options for customers. This managed application can be deployed to a new virtual network or an existing virtual network. Customers can specify either trial or production version of the managed applications. |
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| [Managed Azure Storage Account](https://github.com/Azure/azure-managedapp-samples/tree/master/Managed%20Application%20Sample%20Packages/201-managed-storage-account) | Deploys a single storage account. Use this sample project as an introduction to creating managed applications. |
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| [Managed Service Fabric with Azure management services](https://github.com/Azure/azure-managedapp-samples/tree/master/Managed%20Application%20Sample%20Packages/201-managed-service-fabric) | Deploys a service fabric cluster and virtual machine scale sets. Includes storage accounts for logging and diagnostics. |
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| [Managed Web Application (IaaS) with Azure management services](https://github.com/Azure/azure-managedapp-samples/tree/master/Managed%20Application%20Sample%20Packages/201-managed-web-app) | Deploys a virtual machine that hosts a web application. |
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| [Managed SQL 2017 IaaS with automated patching and backup](https://github.com/Azure/azure-managedapp-samples/tree/master/Managed%20Application%20Sample%20Packages/201-managed-sql-iaas) | Deploys a virtual machine that hosts SQL 2017. |

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