Microsoft Azure AI Fundamentals Training is a beginner-friendly program designed to introduce participants to the core concepts of artificial intelligence, machine learning, generative AI, and Microsoft Azure AI services. The course provides a practical foundation for professionals who want to understand how AI technologies work and how cloud-based AI services can be used to build intelligent solutions.
Microsoft Azure AI Fundamentals Course Overview
This course introduces the fundamental concepts, technologies, and services used to develop artificial intelligence solutions on Microsoft Azure. Participants will explore responsible AI principles, generative and agentic AI, natural language processing, speech, computer vision, and information extraction.
The course also introduces Microsoft Foundry and provides foundational experience in selecting, deploying, configuring, and interacting with AI models and services. It is suitable for participants beginning their journey in AI solution development and cloud-based artificial intelligence.
Duration 2 Days – 14 hrs.
Learning Objectives
- Explain fundamental artificial intelligence concepts and common AI workloads.
- Describe the principles and importance of responsible AI.
- Identify appropriate AI models for different business and technical requirements.
- Explain model deployment options and configuration parameters.
- Recognize generative AI and agentic AI use cases.
- Create effective system and user prompts.
- Navigate Microsoft Foundry and explore its core capabilities.
- Deploy and interact with AI models in Microsoft Foundry.
- Identify text analysis and speech-processing capabilities.
- Describe computer vision and image-generation solutions.
- Explain how AI extracts information from documents, images, audio, and video.
- Recognize the basic steps involved in developing AI-enabled applications.
- Identify appropriate Azure AI services for common organizational requirements.
Target Audience
- Individuals beginning a career in artificial intelligence or cloud technology
- Business and technical professionals seeking foundational knowledge of Azure AI
- Software developers and application developers
- Data analysts and aspiring data professionals
- IT professionals and cloud administrators
- Solution architects and technical consultants
- Project managers and business decision-makers involved in AI initiatives
- Students and career changers interested in cloud-based AI
- Professionals preparing for Microsoft Azure AI Fundamentals certification
Prerequisites
- Basic computer and information technology knowledge
- General awareness of cloud computing concepts
- Familiarity with Microsoft Azure resources and services
- Basic understanding of client-server applications
- Foundational knowledge of programming concepts
- Basic familiarity with Python syntax, REST APIs, SDKs, and command-line tools
- An active Microsoft Azure account or access to an Azure training environment for practical activities
Course Outline
Day 1 – AI Concepts, Responsible AI, and Generative AI
Module 1: Introduction to Artificial Intelligence and Microsoft Azure
- Definition and evolution of artificial intelligence
- Artificial intelligence, machine learning, and deep learning
- Common business applications of AI
- Overview of Microsoft Azure AI capabilities
- Introduction to Microsoft Foundry
- Azure resources used in AI solutions
Module 2: Responsible Artificial Intelligence
- Importance of responsible AI
- Fairness in AI solutions
- Reliability and safety
- Privacy and security
- Inclusiveness and accessibility
- Transparency and explainability
- Accountability and governance
- Responsible AI considerations throughout the solution lifecycle
Module 3: AI Models and Model Configuration
- How generative AI models work
- Foundation models and large language models
- Tokens, context, and model outputs
- Selecting models according to capabilities
- Model deployment options
- Model configuration parameters
- Temperature and output control
- Performance, cost, security, and deployment considerations
Module 4: Generative and Agentic AI Workloads
- Generative AI concepts and use cases
- Text, image, audio, and multimodal generation
- Introduction to AI agents
- Agentic AI scenarios and capabilities
- Components of an AI agent
- Tools, instructions, knowledge, and actions
- Generative AI risks and safeguards
Module 5: Prompt Engineering Fundamentals
- System prompts and user prompts
- Writing clear and effective instructions
- Providing context and examples
- Zero-shot and few-shot prompting
- Grounding model responses
- Prompt testing and refinement
- Reducing inaccurate or irrelevant responses
- Applying content-safety considerations
Day 2 – Building AI Solutions with Microsoft Foundry
Module 6: Getting Started with Microsoft Foundry
- Microsoft Foundry portal overview
- Projects, hubs, and supporting Azure resources
- Exploring the model catalog
- Selecting and deploying a model
- Configuring model deployments
- Testing models in the playground
- Introduction to Microsoft Foundry SDK
- Interacting with a deployed model
- Overview of lightweight AI client applications
Module 7: Creating AI Agents
- Agent architecture and core components
- Creating an agent in Microsoft Foundry
- Defining agent instructions
- Connecting tools and knowledge sources
- Testing an agent
- Managing agent conversations
- Introduction to client applications for agents
- Responsible agent design considerations
Module 8: Text Analysis and Speech Solutions
- Natural language processing concepts
- Keyword and key-phrase extraction
- Named-entity recognition
- Sentiment analysis
- Text summarization
- Language detection and translation
- Speech recognition and speech synthesis
- Azure Speech capabilities in Foundry Tools
- Processing spoken prompts with multimodal models
- Common text and speech solution scenarios
Module 9: Computer Vision and Image Generation
- Computer vision concepts and workloads
- Image classification and object detection
- Optical character recognition
- Image analysis and visual understanding
- Interpreting visual inputs with multimodal models
- Image-generation concepts
- Creating visual outputs with generative models
- Common computer vision solution scenarios
- Responsible use of facial and visual AI technologies
Module 10: Information Extraction and Content Understanding
- Introduction to information extraction
- Overview of Azure Content Understanding
- Extracting information from documents and forms
- Extracting information from images
- Extracting information from audio
- Extracting information from video
- Structuring extracted information
- Common content-processing business scenarios
- Introduction to applications using information-extraction capabilities
Module 11: Planning an Azure AI Solution
- Identifying the appropriate AI workload
- Selecting suitable Azure AI capabilities
- Matching models and services to requirements
- Planning Azure resources and access
- Considering security, privacy, cost, and scalability
- Applying responsible AI principles
- Identifying next steps for continued Azure AI learning
