Cloud Private

Introduction to AI in Azure (AI-901T00)

1 day
AzureAI

Learn core AI concepts and how to get started building AI solutions in Microsoft Azure, including generative AI, language, speech, vision, and extraction.

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  • Private class for your team
  • Live expert instructor
  • Online or on‑location
  • Customizable agenda
  • Proposal turnaround within 1–2 business days
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Course Overview

This course introduces fundamental concepts related to artificial intelligence (AI) and the services in Microsoft Azure that can be used to create AI solutions. You will learn foundational AI concepts and practical starting points for implementing AI workloads on Azure, including generative AI, language, speech, computer vision, and information extraction.

Course Benefits

  • Understand foundational AI concepts, including responsible AI considerations.
  • Describe generative AI concepts such as large language models (LLMs), prompting, and AI agents.
  • Explain core natural language processing (NLP) concepts, including tokenization and semantic language models.
  • Identify key speech AI concepts such as speech recognition and speech synthesis.
  • Recognize common computer vision tasks and modern approaches, including multimodal models.
  • Describe information extraction concepts, including OCR and field extraction.
  • Get started using Azure services and tooling to build AI apps, including Microsoft Foundry for AI and Azure Language and Speech capabilities.

Delivery Methods

Private Class
Delivered for your team at your site or online.

Microsoft Certified Partner

Webucator is a Microsoft Certified Partner. This class uses official Microsoft courseware and will be delivered by a Microsoft Certified Trainer (MCT).

Microsoft Certified Partner

Course Outline

  1. Introduction to AI concepts
    1. Generative AI and agents
    2. Text and natural language
    3. Speech
    4. Computer vision
    5. Information extraction
    6. Responsible AI
    7. Module assessment
  2. Introduction to generative AI and agents
    1. Large language models (LLMs)
    2. Prompts
    3. AI agents
    4. Module assessment
  3. Introduction to natural language processing concepts
    1. Tokenization
    2. Statistical text analysis
    3. Semantic language models
    4. Module assessment
  4. Introduction to AI speech concepts
    1. Speech-enabled solutions
    2. Speech recognition
    3. Speech synthesis
    4. Module assessment
  5. Introduction to computer vision concepts
    1. Computer vision tasks and techniques
    2. Images and image processing
    3. Convolutional neural networks
    4. Vision transformers and multimodal models
    5. Image generation
    6. Module assessment
  6. Introduction to AI-powered information extraction concepts
    1. Overview of information extraction
    2. Optical character recognition (OCR)
    3. Field extraction and mapping
    4. Module assessment
  7. Get started with AI in Azure
    1. Understand Azure
    2. Developing AI apps on Azure
    3. Microsoft Foundry for AI
    4. Using Microsoft Foundry endpoints
  8. Get started with generative AI and agents in Azure
    1. Generative AI models
    2. Using a generative AI model
    3. Creating an agent
  9. Get started with text analysis in Azure
    1. Azure Language
    2. Azure Language SDK
    3. Azure Language MCP
    4. Module assessment
  10. Get started with speech in Azure
    1. Speech recognition
    2. Speech synthesis
    3. Creating a speech-capable agent
    4. Module assessment
  11. Get started with computer vision in Azure
    1. Multimodal models for image analysis
    2. Image generation models
    3. Video generation models
    4. Module assessment
  12. Get started with AI-powered information extraction in Azure
    1. Extract information from documents
    2. Extract information from audio and video
    3. Module assessment

Class Materials

Each student receives a comprehensive set of materials, including course notes and all class examples.

Class Prerequisites

Experience in the following would be useful for this Azure class:

  • Experience using computers and the internet
  • Interest in AI workloads and applications
  • A basic understanding of math (interpreting charts, simple probability)
  • Familiarity with fundamental programming concepts (helpful but not required)

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