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This course replaces the following:
AI-100: Designing and Implementing an Azure AI Solution
Learning Path 1: Develop generative AI apps in Azure
Generative Artificial Intelligence (AI) is becoming more accessible through comprehensive development platforms like Azure AI Foundry. Learn how to build generative AI applications that use language models to chat with your users.
Lessons:
- Plan and prepare to develop AI solutions on Azure
- Choose and deploy models from the model catalog in Azure AI Foundry portal
- Develop an AI app with the Azure AI Foundry SDK
- Get started with prompt flow to develop language model apps in the Azure AI Foundry
- Develop a RAG-based solution with your own data using Azure AI Foundry
- Fine-tune a language model with Azure AI Foundry
- Implement a responsible generative AI solution in Azure AI Foundry
- Evaluate generative AI performance in Azure AI Foundry portal
Exercises:
- Prepare for an AI development project
- Explore, deploy, and chat with language models
- Create a generative AI chat app
- Get started with prompt flow
- Create a generative AI app that uses your own data
- Fine-tune a language model
- Apply content filters to prevent the output of harmful content
- Evaluate generative AI model performance
Learning Path 2: Develop AI agents on Azure
Generative Artificial Intelligence (AI) is becoming more functional and accessible, and AI agents are a key component of this evolution. This learning path will help you understand the AI agents, including when to use them and how to build them, using Azure AI Foundry Agent Service and Semantic Kernel Agent Framework. By the end of this learning path, you will have the skills needed to develop AI agents on Azure.
Lessons:
- Get started with AI agent development on Azure
- Develop an AI agent with Azure AI Foundry Agent Service
- Integrate custom tools into your agent
- Develop a multi-agent solution with Azure AI Foundry Agent Service
- Integrate MCP Tools with Azure AI Agents
- Develop an AI agent with Microsoft Agent Framework
- Orchestrate a multi-agent solution using the Microsoft Agent Framework
- Discover Azure AI Agents with A2A
Exercises:
- Explore AI Agent development
- Build an AI agent
- Build an agent with custom tools
- Develop a multi-agent app with Azure AI Foundry
- Connect MCP tools to Azure AI Agents
- Develop an Azure AI agent with the Microsoft Agent Framework SDK
- Develop a multi-agent solution
- Connect to remote Azure AI Agents with the A2A protocol
Learning Path 3: Develop natural language solutions in Azure
Natural language solutions use language models to interpret the semantic meaning of written or spoken language, and in some cases respond based on that meaning. You can use the Language service to build language models for your applications and explore Azure AI Foundry to use generative models for speech.
Lessons:
- Analyze text with Azure AI Language
- Create question answering solutions with Azure AI Language
- Build a conversational language understanding model
- Create custom text classification solutions
- Custom named entity recognition
- Translate text with Azure AI Translator service
- Create speech-enabled apps with Azure AI services
- Translate speech with the Azure AI Speech service
- Develop an audio-enabled generative AI application
- Develop an Azure AI Voice Live agent
Exercises:
- Analyze text
- Create a question answering solution
- Build an Azure AI services conversational language understanding model
- Classify text
- Extract custom entities
- Translate text with the Azure AI Translator service
- Create a speech-enabled app
- Translate speech
- Develop an audio-enabled chat app
- Develop an Azure AI Voice Live agent
Learning Path 4: Develop computer vision solutions in Azure
Computer vision is an area of artificial intelligence that deals with visual perception. Azure AI includes multiple services that support common computer vision scenarios.
Lessons:
- Analyze images
- Read text in images
- Detect, analyze, and recognize faces
- Classify images
- Detect objects in images
- Analyze video
- Develop a vision-enabled generative AI application
- Generate images with AI
Exercises:
- Analyze images
- Read text in images
- Detect and analyze faces
- Classify images
- Detect objects in images
- Analyze video
- Develop a vision-enabled chat app
- Generate images with AI
Learning Path 5: Develop AI information extraction solutions in Azure
Use Azure AI to extract information from content to support scenarios like: Data capture, Business process automation, Meeting summarization and analysis, Digital asset management (DAM) and Knowledge Mining
Lessons:
- Create a multimodal analysis solution with Azure AI Content Understanding
- Create an Azure AI Content Understanding client application
- Use prebuilt Document intelligence models
- Extract data from forms with Azure Document intelligence
- Create a knowledge mining solution with Azure AI Search
Exercises:
- Extract information from multimodal content
- Develop a Content Understanding client application
- Analyze a document using Azure AI Document Intelligence
- Extract data from custom forms
- Create a knowledge mining solution
Microsoft Azure AI engineers build, manage, and deploy AI solutions that make the most of services. Their responsibilities include participating in all phases of AI solutions development—from requirements definition and design to development, deployment, integration, maintenance, performance tuning, and monitoring.
These professionals work with solution architects to translate their vision and with data scientists, data engineers, IoT specialists, infrastructure administrators, and other software developers to build complete end-to-end AI solutions.
Azure AI engineers have experience developing solutions that use languages such as Python or C# and should be able to use REST-based APIs and software development kits (SDKs) to build secure image processing, video processing, natural language processing, and generative AI solutions on Azure. They should be familiar with all methods of implementing AI solutions. Plus, they understand the components that make up the Azure AI portfolio and the available backend options. Azure AI engineers also need to understand and be able to apply responsible AI principles.
Before attending this course, students must have:
- Knowledge of Microsoft Azure and ability to navigate the Azure portal
- Knowledge of either C# or Python
- Familiarity with JSON and REST programming semantics
If you are new to artificial intelligence, and want an overview of AI capabilities on Azure, consider completing the Azure AI Fundamentals course before taking this one.