Developing AI solutions on Azure

Module 1: Developing generative AI applications on Azure

Generative Artificial Intelligence (AI) is becoming more accessible through comprehensive development platforms like Microsoft Foundry. Learn how to create generative AI applications that use language models to converse with your users.

Lessons:

• Planning and preparing the development of AI solutions on Azure
• Choose and deploy models from the model catalog in the Microsoft Foundry portal
• Develop an AI application with the Microsoft Foundry SDK
• Get started with the prompt flow for developing language model applications in Microsoft Foundry
• Develop a RAC-based solution using your own data with Microsoft Foundry
• Refining a language model with Microsoft Foundry
• Implement a responsible generative AI solution in Microsoft Foundry
• Evaluate the performance of generative AI on the Microsoft Foundry portal

Module 2: Developing AI agents in 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 AI agents, including when to use them and how to build them, using the Microsoft Foundry Agent Service and the Microsoft Agent Framework. By the end of this learning path, you will have the skills needed to develop AI agents in Azure.

Lessons:

• Introduction to developing AI agents in Azure
• Develop an AI agent with the Microsoft Foundry Agent Service
• Develop AI agents using the Microsoft Foundry extension in Visual Studio Code
• Integrate customized tools into your agent
• Develop a multi-agent solution with the Microsoft Foundry Agent Service
• Integrate MCP tools with Azure AI agents
• Develop an AI agent using the Microsoft Agent Framework
• Orchestrate a multi-agent solution using the Microsoft Agent Framework
• Discover Azure AI Agents with A2A
• Building agent-driven workflows using Microsoft Foundry
• Build AI agents with enhanced knowledge using Foundry IQ

Module 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 Microsoft Foundry to use generative models for speech.

Lessons:

• Analyze text with Azure Language
• Create solutions to answer questions using the Azure Language
• Create a model for understanding conversational language
• Create customized text classification solutions
• Personalized named entity recognition
• Translate text using the Azure Translator service
• Create speech-enabled applications with Microsoft Foundry
• Translate speech using the Azure Speech service
• Develop an audio-enabled generative AI application
• Develop an Azure AI Voice Live agent

Module 4: Develop computer vision solutions on Azure

Digitized imaging is an area of ​​artificial intelligence that deals with visual perception. Azure AI includes several services that support common computer vision scenarios.

Lessons:

• Analyze images
• Read text in images
• Detect, analyze, and recognize faces
• Classify images
• Detecting objects in images
• Analyze the video
• Develop a generative AI application with vision capabilities
• Generate images with AI

Module 5: Develop AI information extraction solutions in Azure

Use Azure AI to extract insights from content to support scenarios such as: Data capture; Business process automation; Summary and analysis of the meeting; Digital asset management (DAM); Knowledge Extraction.

Lessons:

• Create a multimodal analytics solution with Azure Content Understanding
• Create an Azure Content Understanding client application
• Use pre-built document intelligence templates
• Extract data from forms with Azure Document Intelligence
• Create a knowledge mining solution with Azure AI Search