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Merge pull request #53425 from sherzyang/NEW-get-started-with-ai-in-azure
Add new module.
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### YamlMime:ModuleUnit
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uid: learn.wwl.get-started-with-ai-in-azure.introduction
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title: Introduction
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metadata:
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title: Introduction
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description: "This unit is an introduction to AI applications."
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ms.date: 02/03/2026
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author: sherzyang
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ms.author: sheryang
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ms.topic: unit
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zone_pivot_groups: video-or-text
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durationInMinutes: 3
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content: |
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[!include[](includes/1-introduction.md)]
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### YamlMime:ModuleUnit
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uid: learn.wwl.get-started-with-ai-in-azure.what-is-azure
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title: Understand Azure
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metadata:
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title: Understand Azure
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description: "This unit provides an overview of Azure, its capabilities, and its role in building AI solutions. Learn about the key components and services that make up the platform."
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ms.date: 02/03/2026
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author: sherzyang
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ms.author: sheryang
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ms.topic: unit
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zone_pivot_groups: video-or-text
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durationInMinutes: 4
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content: |
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[!include[](includes/2-what-is-azure.md)]
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### YamlMime:ModuleUnit
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uid: learn.wwl.get-started-with-ai-in-azure.develop-ai-apps
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title: Developing AI apps on Azure
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metadata:
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title: Developing AI apps on Azure
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description: "This unit covers the essential components that make up an AI application on Azure."
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ms.date: 02/03/2026
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author: sherzyang
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ms.author: sheryang
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ms.topic: unit
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zone_pivot_groups: video-or-text
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durationInMinutes: 4
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content: |
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[!include[](includes/3-develop-ai-apps.md)]
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### YamlMime:ModuleUnit
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uid: learn.wwl.get-started-with-ai-in-azure.microsoft-foundry
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title: Microsoft Foundry for AI
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metadata:
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title: Microsoft Foundry for AI
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description: "This unit explores how Microsoft Foundry provides a robust platform for building AI applications. Learn about the specific services and tools available on Microsoft Foundry that support AI development. Understand how to use Foundry to build a client application."
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ms.date: 02/03/2026
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author: sherzyang
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ms.author: sheryang
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ms.topic: unit
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zone_pivot_groups: video-or-text
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durationInMinutes: 5
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content: |
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[!include[](includes/4-microsoft-foundry.md)]
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### YamlMime:ModuleUnit
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uid: learn.wwl.get-started-with-ai-in-azure.endpoints
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title: Using Microsoft Foundry endpoints
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metadata:
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title: Using Microsoft Foundry endpoints
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description: "Understand how to use Foundry to build a client application and use its endpoints."
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ms.date: 02/03/2026
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author: sherzyang
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ms.author: sheryang
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ms.topic: unit
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zone_pivot_groups: video-or-text
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durationInMinutes: 4
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content: |
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[!include[](includes/5-endpoints.md)]
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### YamlMime:ModuleUnit
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uid: learn.wwl.get-started-with-ai-in-azure.exercise
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title: Exercise - Get started with Microsoft Foundry
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metadata:
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title: Exercise - Get started with Microsoft Foundry
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description: "This unit provides hands-on experience with Microsoft Foundry. Complete the exercises to reinforce your understanding of the concepts covered in the previous units."
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ms.date: 02/03/2026
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author: graememalcolm
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ms.author: gmalc
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ms.topic: unit
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durationInMinutes: 35
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content: |
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[!include[](includes/6-exercise.md)]
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### YamlMime:ModuleUnit
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uid: learn.wwl.get-started-with-ai-in-azure.knowledge-check
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title: Knowledge check
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metadata:
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title: Knowledge check
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description: "This unit provides a knowledge check to assess your understanding of the concepts covered in the previous units. Test your knowledge of Azure and its capabilities for AI development."
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ms.date: 02/03/2026
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author: sherzyang
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ms.author: sheryang
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ms.topic: unit
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durationInMinutes: 4
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content: |
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quiz:
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questions:
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- content: "Which statement best explains the relationship between AI and ML?"
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choices:
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- content: "AI and ML are interchangeable terms; both refer to systems that mimic human intelligence without distinction."
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isCorrect: false
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explanation: "AI and ML aren't interchangeable. AI is the broader concept, while ML is one of the primary techniques used to achieve AI."
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- content: "ML focuses exclusively on generative tasks like creating text and images, whereas AI is limited to decision-making and planning."
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isCorrect: false
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explanation: "ML isn't limited to generative tasks. AI also encompasses more than decision-making—it includes perception, reasoning, and language understanding."
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- content: "AI is the overarching goal of creating systems that exhibit human-like intelligence, while ML is a data-driven method used to achieve AI by learning patterns from data."
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isCorrect: true
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explanation: "AI aims to mimic human intelligence, and ML enables AI by using algorithms that learn from data without explicit programming."
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- content: "How does Microsoft Foundry relate to Azure?"
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choices:
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- content: "Foundry is built on top of Azure and uses Azure resources such as compute, networking, identity, and security to host and operate AI applications."
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isCorrect: true
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explanation: "Foundry is an enterprise-grade platform built on Azure, using Azure’s compute, networking, identity (Entra ID), security, and resource management. You can't run Foundry without Azure."
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- content: "Foundry runs independently from Azure and doesn't require Azure resources to deploy models or agents."
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isCorrect: false
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explanation: "Foundry doesn't operate independently—its deployments, security, and hosting all rely on Azure resource infrastructure."
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- content: "Foundry replaces Azure services entirely, serving as a standalone cloud platform for running AI workloads."
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isCorrect: false
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explanation: "Foundry doesn't replace Azure; it extends Azure specifically for AI agents and applications."
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- content: "Which statement best describes how keys, secrets, and endpoints work together in an Azure‑based AI application?"
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choices:
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- content: "The key is the location where model responses are stored, and the endpoint retrieves those responses from Azure Key Vault."
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isCorrect: false
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explanation: "Keys don't store model responses, and endpoints don't retrieve data from Key Vault. Key Vault stores secrets, not model outputs."
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- content: "The endpoint stores sensitive values for an AI application, and the key determines how much data the endpoint can return."
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isCorrect: false
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explanation: "Endpoints don't store secrets. They expose model deployments. Keys don't determine “how much data” is returned—they authenticate access."
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- content: "The endpoint is a URL for calling a deployed model, and the key (stored as a secret in Azure Key Vault) authenticates the request made to that endpoint."
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isCorrect: true
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explanation: "An endpoint is the URL used to call a deployed model. A key is a type of secret that authenticates the request. Keys and secrets should be stored in Azure Key Vault, not in code. The application retrieves the secret at runtime and uses it to securely call the endpoint."
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- content: "Which statement best describes a client application in the context of an AI solution built with Foundry?"
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choices:
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- content: "A client application is the environment that hosts the model, runs inference, and returns results."
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isCorrect: false
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explanation: "This describes the server or model deployment, not the client."
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- content: "A client application is a program the user interacts with—such as a web app or mobile app—that sends requests to a model endpoint and displays the response."
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isCorrect: true
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explanation: "A client application is the user-facing program (web, mobile, desktop, CLI) that collects input, sends it to the model endpoint, and shows the result."
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- content: "A client application is a standalone Azure service that automatically generates API keys for model deployments."
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isCorrect: false
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explanation: "Client apps don't generate keys; keys come from Azure/Foundry resources."
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### YamlMime:ModuleUnit
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uid: learn.wwl.get-started-with-ai-in-azure.summary
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title: Summary
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metadata:
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title: Summary
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description: "This unit summarizes the key concepts covered in the previous units. Review the main points related to Azure and its capabilities for AI development."
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ms.date: 02/03/2026
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author: sherzyang
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ms.author: sheryang
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ms.topic: unit
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zone_pivot_groups: video-or-text
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durationInMinutes: 2
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content: |
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[!include[](includes/8-summary.md)]
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::: zone pivot="video"
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>[!VIDEO https://learn-video.azurefd.net/vod/player?id=0bfdd2dd-4d8c-47ce-b632-33b2f2e69430]
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::: zone-end
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::: zone pivot="text"
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**Artificial Intelligence (AI)** refers to systems designed to perform tasks that typically require human intelligence—such as reasoning, problem-solving, perception, and language understanding.
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An **AI application** is a software solution that uses AI techniques—such as computer vision, speech, and information extraction—to perform tasks that typically require human-like intelligence. These applications can understand, reason, learn, and respond to inputs in a way that feels more adaptive than traditional software.
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AI applications are powered by *machine learning* (ML) models, which are mathematical systems trained to recognize patterns in data and make predictions or generate outputs. ML models are the engines inside an AI application. When you interact with an AI application, the model performs *inference*, meaning it applies what it learned during training to new input.
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AI applications are:
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- *Model-powered*: They use trained models to process inputs and generate outputs, such as text, images, or decisions.
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- *Dynamic*: Unlike static programs, AI apps can improve over time through retraining or fine-tuning.
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Some examples of AI applications for different industries include:
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- **Healthcare**: AI-powered diagnostic tools that analyze medical images (such as X-rays or MRIs) and help doctors detect diseases more accurately and quickly.
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- **Finance**: Fraud detection systems that use AI to monitor transactions in real time and identify suspicious activity, helping prevent financial crimes.
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- **Retail**: Personalized recommendation engines that analyze customer behavior and preferences to suggest products, improving the shopping experience.
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- **Manufacturing**: Predictive maintenance solutions that use AI to monitor equipment and forecast when machines are likely to fail, reducing downtime and maintenance costs.
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- **Education**: Intelligent tutoring systems that adapt to each student’s learning style and pace, providing customized feedback and support to enhance learning outcomes.
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In this module, you learn how Microsoft enables you to build AI applications with the latest technology, securely, and at scale. While the model is the engine, AI applications also need security, networking, hosting, data storage, application logic, and user interfaces. Microsoft provides all the infrastructure and services needed to support enterprise-scale AI development. The module gives you a foundation in how Azure streamlines AI application development, integrates with Microsoft Foundry, and enables rapid innovation.
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::: zone-end
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> [!NOTE]
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> We recognize that different people like to learn in different ways. You can choose to complete this module in video-based format or you can read the content as text and images. The text contains greater detail than the videos, so in some cases you might want to refer to it as supplemental material to the video presentation.
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::: zone pivot="video"
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>[!VIDEO https://learn-video.azurefd.net/vod/player?id=e7684170-b332-4cfd-bc95-f908653b39c4]
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::: zone-end
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::: zone pivot="text"
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**Microsoft Azure** is one of the world's leading cloud platforms. A **cloud platform** is a collection of services you can use over the internet instead of running everything on your own computer or server. Today, most modern applications run in the cloud, in internet-based data centers that let you store data, run code, and scale without worrying about physical hardware. Instead of buying and maintaining your own infrastructure, you use services provided by trusted platforms.
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Azure's trusted by organizations globally to build secure, reliable applications. With Azure, you can develop AI applications and agents that take advantage of advanced AI services and deploy them at global scale.
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Azure provides four core categories of services:
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- **Compute**: The ability to run applications, programs, and workloads in the cloud. You can think of compute as renting computers in the cloud that you can scale up or down whenever you need.
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- **Storage**: Services that let you save and manage data in the cloud. Storage can include files, databases, images, backups—anything you want safely stored and accessible from anywhere.
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- **Networking**: Tools that connect your cloud resources to each other, to the internet, or to your organization.
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Networking makes sure your apps can talk to each other securely and efficiently.
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- **App Services**: Ready‑made platforms for building, hosting, and running applications without managing the underlying servers.
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## Azure's organizational structure
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When a user gets started with Azure, their access needs to be organized and managed. Azure organizes access and management by **tenants**, **subscriptions**, **resource groups**, and **resources**.
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![Screenshot of diagram of Azure's organizational structure.](../media/azure-tenant-structure.png)
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An **Azure tenant** is an organization's home base and identity in Microsoft's cloud. Having a tenant is like having an apartment unit in a large building, where the building is Microsoft cloud. Each tenant is separate and secured from others—your organization has its own locks, rooms, and controls.
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When a company signs up for Azure or Microsoft 365, Microsoft creates a *tenant* for them. This tenant is a dedicated and secure space where all the organization’s cloud resources live. The tenant includes users, groups, identities, and policies for managing secure access.
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An **Azure subscription** is a billing container for your cloud resources. One tenant can contain one or many Azure subscriptions. A subscription ties your Azure usage to a payment method (or credits for free/trial accounts) and sets boundaries for cost, quotas, and access control.
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**Resource groups (RGs)** are folders that hold related Azure resources so you can manage them together. One subscription can have multiple resource groups. Each resource group can have custom permissions and policies at the resource-group level.
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An **Azure resource** is *any individual service or object you create in Azure* such as a storage account, a database, or a Foundry resource. Each resource has a resource type, configuration settings, unique resource name and ID, and optional access controls. The resource type (for example: `Microsoft.Storage/storageAccounts`) defines the resource's behavior, capabilities, and settings.
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When you *configure* a resource, you choose its settings, such as:
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- Region (where the resource is deployed)
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- Performance tier (associated with cost)
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- Permissions and security
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Azure's organizational structure helps ensure clarity, security, and scalability in cloud environments. Tenants and subscriptions allow for clear separation of concerns across departments or projects. Resource groups simplify management by grouping related assets, making it easier to apply policies, monitor usage, and automate deployments. Understanding this hierarchy is essential for efficient cloud governance and cost control in Azure.
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## Azure portal
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**Azure portal**, accessible at [https://portal.azure.com](https://portal.azure.com?portal=true), is a centralized, web-based management user interface (UI) for all Azure services. It can be used to:
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- Create and manage cloud resources
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- Deploy and configure services
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- Monitor usage, performance, and health
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- Manage identities, roles, and access policies
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- View billing, costs, and spending patterns
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- Access specialized services like Microsoft Foundry
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![Screenshot of the Azure portal experience.](../media/azure-portal-example.png)
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You can create and manage individual resources in Azure portal. Each resource has information which is accessible through the *All resources* pane.
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![Screenshot of a resource in the Azure portal.](../media/example-foundry-resource.png)
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> [!NOTE]
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> You can also create Azure resources **programmatically**, which means using code or scripts *instead* of clicking through the Azure portal interface. This approach is useful when you want to repeat the same setup across environments, automate deployments, or reduce manual errors. For example, instead of going into the Azure portal to create a storage account, you could run a single command in the Azure CLI or use a script that creates it the same way every time. This makes your deployments faster, more reliable, and easier to maintain—especially for larger AI applications that require many resources.
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Next, learn how Azure gives you everything you need to build scalable, secure AI applications.
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::: zone-end

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