Fix merge conflicts

Author: ShawnKupfer

.openpublishing.redirection.json

@@ -5335,3 +5335,53 @@ achievements:
   title: Deploy model to NVIDIA Triton Inference Server
   summary: NVIDIA Triton Inference Server is a multi-framework, open-source software that is optimized for inference. It supports popular machine learning frameworks like TensorFlow, Open Neural Network Exchange (ONNX) Runtime, PyTorch, NVIDIA TensorRT, and more. It can be used for your CPU or GPU workloads. In this module, you deploy your production model to NVIDIA Triton server to perform inference on a cloud-hosted virtual machine.
   iconUrl: /training/achievements/introduction-nvidia-deepstream-graph-composer-azure.svg
+- uid: learn.nvidia.develop-custom-object-detection-models-with-nvidia-and-azure-machine-learning.trophy
+  type: trophy
+  title: Develop Custom Object Detection Models with NVIDIA and Azure Machine Learning
+  summary: Azure Machine Learning studio is a GUI-based integrated development environment for constructing and operationalizing Machine Learning workflow on Azure.  Learn how to develop custom object detection models using this service with NVIDIA GPU accelerated virtual machines.
+  iconUrl: /training/achievements/nvidia-deepstream-development-with-microsoft-azure-social.png
+- uid: learn.nvidia.create-workspace-resources-getting-started-azure-machine-learning.badge
+  type: badge
+  title: Create workspace resources for getting started with Azure Machine Learning
+  summary: In this module, you learn how to create resources for getting started with Azure Machine Learning.
+  iconUrl: /training/achievements/setup-configure-nvidia-deepstream-development.svg
+- uid: learn.nvidia.create-labeled-dataset-using-azure-machine-learning-data-labeling-tools.badge
+  type: badge
+  title: Create a labeled dataset using Azure Machine Learning data labeling tools
+  summary: Learn how to use Azure Machine Learning data labeling to create, manage, and monitor data labeling projects.
+  iconUrl: /training/achievements/setup-configure-nvidia-deepstream-development.svg
+- uid: learn.azure.communication-service-send-sms-console-app.badge
+  type: badge
+  title: Send an SMS message from a C# console application with Azure Communication Services
+  summary: In this module, you'll create a C# console application that sends SMS messages using a phone number provisioned via Azure Communication Services.
+  iconUrl: /training/achievements/communication-service-send-sms-console-app.svg
+- uid: learn.student-evangelism.build-ml-model-with-azure-stream-analytics.badge
+  type: badge
+  title: Track wild polar bears with AI
+  summary: Detect and track polar bears through photos using AI, and then use Power BI to show where cameras spot polar bears.
+  iconUrl: /training/achievements/student-evangelism/build-ml-model-with-azure-stream-analytics-badge.svg
+- uid: learn.reactors.blockchain-tokens.badge
+  type: badge
+  title: Create tokens using OpenZeppelin
+  summary: Learn about the significance of tokens and how they are used in blockchain.
+  iconUrl: /training/achievements/reactors/blockchain-tokens.svg
+- uid: learn.reactors.blockchain-solidity-ethereum-smart-contracts.badge
+  type: badge
+  title: Write Ethereum smart contracts by using Solidity
+  summary: Learn how to install and use tools that you can use to develop smart contracts.
+  iconUrl: /training/achievements/reactors/blockchain-solidity-ethereum-smart-contracts.svg
+- uid: learn.reactors.blockchain-learning-solidity.badge
+  type: badge
+  title: Learn how to use Solidity
+  summary: Discover how Solidity can make it easy to program smart contracts for the Ethereum blockchain platform.
+  iconUrl: /training/achievements/reactors/blockchain-learning-solidity.svg
+- uid: learn.reactors.blockchain-ethereum-networks.badge
+  type: badge
+  title: Connect and deploy to Ethereum networks
+  summary: Learn about and use Ethereum networks for development, testing, and production.
+  iconUrl: /training/achievements/reactors/blockchain-ethereum-networks.svg
+- uid: learn.ethereum-blockchain-development.trophy
+  type: trophy
+  title: Get started with blockchain development
+  summary: This learning path introduces you to blockchain and development on the Ethereum platform. Discover what skills are necessary to learn to begin building your own blockchain networks at scale.
+  iconUrl: /training/achievements/ethereum-blockchain-development.svg

learn-pr/achievements.yml

@@ -0,0 +1,13 @@
+### YamlMime:ModuleUnit
+uid: learn.introduction-azure-machine-learning-auth.introduction
+title: Introduction
+metadata:
+  title: Introduction
+  description: Introduction to Azure Machine Learning authentication and authorization.
+  ms.date: 03/06/2025
+  author: Orin-Thomas
+  ms.author: viniap
+  ms.topic: unit
+durationInMinutes: 2
+content: |
+  [!include[](includes/1-introduction.md)]

learn-pr/advocates/intro-azure-machine-learning-auth/1-introduction.yml

@@ -0,0 +1,13 @@
+### YamlMime:ModuleUnit
+uid: learn.introduction-azure-machine-learning-auth.authentication-azure-machine-learning-workspaces
+title: Authentication for Azure Machine Learning workspaces
+metadata:
+  title: Authentication for Azure Machine Learning workspaces
+  description: Understand authentication for Azure Machine Learning workspaces.
+  ms.date: 03/06/2025
+  author: Orin-Thomas
+  ms.author: viniap
+  ms.topic: unit
+durationInMinutes: 6
+content: |
+  [!include[](includes/2-authentication-azure-machine-learning-workspaces.md)]

learn-pr/advocates/intro-azure-machine-learning-auth/2-authentication-azure-machine-learning-workspaces.yml

@@ -0,0 +1,13 @@
+### YamlMime:ModuleUnit
+uid: learn.introduction-azure-machine-learning-auth.manage-access-azure-machine-learning
+title: Manage access to Azure Machine Learning
+metadata:
+  title: Manage access to Azure Machine Learning
+  description: Control access to Azure Machine Learning resources.
+  ms.date: 03/06/2025
+  author: Orin-Thomas
+  ms.author: viniap
+  ms.topic: unit
+durationInMinutes: 7
+content: |
+  [!include[](includes/3-manage-access-azure-machine-learning.md)]

learn-pr/advocates/intro-azure-machine-learning-auth/3-manage-access-azure-machine-learning.yml

@@ -0,0 +1,13 @@
+### YamlMime:ModuleUnit
+uid: learn.introduction-azure-machine-learning-auth.authentication-between-azure-machine-learning-other-azure-services
+title: Authentication between Azure Machine Learning and other Azure services
+metadata:
+  title: Authentication between Azure Machine Learning and other Azure services
+  description: Configure access for workspaces to other Azure services.
+  ms.date: 03/06/2025
+  author: Orin-Thomas
+  ms.author: viniap
+  ms.topic: unit
+durationInMinutes: 10
+content: |
+  [!include[](includes/4-authentication-between-azure-machine-learning-other-azure-services.md)]

learn-pr/advocates/intro-azure-machine-learning-auth/4-authentication-between-azure-machine-learning-other-azure-services.yml

@@ -0,0 +1,51 @@
+### YamlMime:ModuleUnit
+uid: learn.introduction-azure-machine-learning-auth.knowledge-check
+title: Knowledge check
+metadata:
+  title: Knowledge Check
+  description: Knowledge Check
+  ms.date: 03/06/2025
+  author: Orin-Thomas
+  ms.author: viniap
+  ms.topic: unit
+durationInMinutes: 3
+content: Choose the best response for each question. 
+quiz:
+  questions:
+    - content: "When are account keys used for authentication purposes rather than Microsoft Entra ID?"
+      choices:
+        - content: "When accessing other Azure resources."
+          isCorrect: false
+          explanation: "Access between Azure resources (including Azure Machine Learning) use managed-identities which is a feature of Microsoft Entra ID"
+        - content: "When Azure Machine Learning compute clusters or Kubernetes clusters access other Azure services."
+          isCorrect: false
+          explanation: "Azure Machine Learning compute clusters or Kubernetes clusters use managed-identities which are a feature of Entra ID"
+        - content: "When account keys or tokens are used for access to external data sources."
+          isCorrect: true
+          explanation: "In cases on which the data source only accepts credential-based authentication, Azure Machine Learning can use Azure Key Vault to store these secrets"
+    - content: "Which Azure Machine Learning default role should you assign to someone who is responsible for the compute resources in a workspace?"
+      choices:
+        - content: "Contributor."
+          isCorrect: false
+          explanation: "Contributors can create and delete compute resources in a workspace and also have additional permissions. Granting Contributor access to someone who is responsible to for the compute resources might pose a security risk"
+        - content: "AzureML Compute Operator."
+          isCorrect: true
+          explanation: "Users assigned the AzureML Compute Operator role can only create, manage, delete, and access compute resources within a workspace"
+        - content: "AzureML Data Scientist."
+          isCorrect: false
+          explanation: "Users assigned the AzureML Data Scientist role can perform all actions within an Azure Machine Learning workspace, except for creating or deleting compute resources and modifying the workspace itself."
+    - content: "Which of the following statements is true regarding system-assigned managed identity for Azure Machine Learning?"
+      choices:
+        - content: "When that workspace is deleted, its associated system-assigned identity is also deleted."
+          isCorrect: true
+          explanation: "The lifecycle of system-assigned managed identities is tied to their associated resource. When the resource is deleted, the identity is also deleted."
+        - content: "You must manually enable a system-assigned identity after creating a machine learning workspace."
+          isCorrect: false
+          explanation: "By default, Azure Machine Learning has a system-assigned managed identity and that is a supported scenario"
+        - content: "Azure Machine Learning workspaces are assigned a user-managed identity by default."
+          isCorrect: false
+          explanation: "Creating an Azure Machine Learning workspace automatically creates a system-assigned managed identity. User-managed identities must be manually configured."
+  
+  
+        
+   

learn-pr/advocates/intro-azure-machine-learning-auth/5-knowledge-check.yml

@@ -0,0 +1,13 @@
+### YamlMime:ModuleUnit
+uid: learn.introduction-azure-machine-learning-auth.summary
+title: Summary
+metadata:
+  title: Summary
+  description: Module summary
+  ms.date: 03/06/2025
+  author: Orin-Thomas
+  ms.author: viniap
+  ms.topic: unit
+durationInMinutes: 1
+content: |
+  [!include[](includes/6-summary.md)]

learn-pr/advocates/intro-azure-machine-learning-auth/6-summary.yml

@@ -0,0 +1,11 @@
+Azure Machine Learning (ML) is a cloud service for managing machine learning project lifecycles. ML professionals, data scientists, and engineers can use Azure Machine Learning to train and deploy models and manage machine learning operations (MLOps).
+
+An Azure Machine Learning workspace is a centralized environment for managing machine learning projects, allowing collaboration and organization of experiments, datasets, models, and deployments. The workspace provides tools for creating, training, and deploying models, along with managing compute resources and data assets. As a cloud operations professional, you need to manage Azure Machine Learning workspace authentication and authorization.
+
+## Learning objectives ##
+
+After completing this module, you'll be able to:
+
+- Set up authentication for Azure Machine Learning resources and workflows
+- Manage access to Azure Machine Learning workspaces
+- Set up authentication between Azure Machine Learning and other services

learn-pr/advocates/intro-azure-machine-learning-auth/includes/1-introduction.md

@@ -0,0 +1,30 @@
+Azure Machine Learning provides workspaces to create and manage machine learning artifacts. Workspaces serve as containers for access management, cost management, and data isolation. As the administrator of an Azure Machine Learning workspace, you'll manage two aspects of authentication and authorization:
+
+- Manage access to Azure Machine Learning workspaces giving users the ability to create new resources or use existing ones.
+- Manage the authentication between Azure Machine Learning and the services it relies on.
+
+Authentication in Azure Machine Learning workspaces can use Microsoft Entra ID or account keys or tokens. Keys and tokens are most only often used for access to external data sources that might not support Entra ID. In those scenarios, you can use Azure Key Vault to securely manage secrets. You should never include account keys or tokens directly in code.
+
+Users authenticate to an Azure Machine Learning workspace using one of the following methods:
+
+- **Interactive**: Users can leverage their Microsoft Entra ID to either directly authenticate, or to get a token that is used for authentication. Interactive authentication is used during experimentation and iterative development. Interactive authentication enables you to control access to resources (such as a web service) on a per-user basis.
+
+- **Service principal**: Service principal accounts in Microsoft Entra ID can be used by services to authenticate or get a token. A service principal is used to authenticate an automated process to the service without requiring user interaction. For example, a continuous integration and deployment script that trains and tests a model every time the training code changes.
+
+- **Azure CLI session**: The Azure CLI extension for Machine Learning (the ml extension or CLI v2) is a command line tool for working with Azure Machine Learning. Users can sign in to Azure via the Azure CLI on their local workstation, without storing credentials in Python code or prompting them to authenticate. Similarly, users can reuse the same scripts as part of continuous integration and deployment pipelines, while authenticating the Azure CLI with a service principal identity.
+
+- **Managed identity**: When using the Azure Machine Learning SDK v2 on a compute instance or on an Azure Virtual Machine, users can use a managed identity for Azure. This workflow allows the VM to connect to the workspace using the managed identity, without storing credentials in Python code or prompting the user to authenticate. Azure Machine Learning compute clusters can also be configured to use a managed identity to access the workspace when training models. Whenever possible, using a managed identity is the preferred method and best practice.
+
+You can use Microsoft Entra Conditional Access to further control or restrict access to the workspace for each authentication workflow. For example, you can configure conditional access so that an administrator is only able to access an Azure Machine Learning workspace from a managed device.
+
+Azure Machine Learning can authenticate with other services using the following methods:
+
+- Data access can happen along multiple paths depending on the data storage service and your configuration. For example, authentication to the datastore can use an account key, token, security principal, managed identity, or user identity.
+
+- Azure Machine Learning workspaces use a managed identity to communicate with other Azure services. By default, this is a system-assigned managed identity, but you can also configure an Azure Machine Learning workspace with a user-assigned managed identity.
+
+- Azure Machine Learning uses Azure Container Registry (ACR) to store container images used to train and deploy models. If you allow Azure Machine Learning to automatically create an ACR registry, it enables the **admin account** for that registry.
+
+- The Azure Machine Learning compute cluster uses a **managed identity** to retrieve connection information for datastores from Azure Key Vault and to pull container images from ACR. You can also configure identity-based access to datastores, which will instead use the managed identity of the compute cluster.
+
+- Managed online endpoints can use a managed identity to access Azure resources when performing inference.