Visão Geral

O Curso Microsoft AI-102T00 – Develop AI Solutions in Azure capacita profissionais a projetar, desenvolver e implementar soluções de Inteligência Artificial utilizando os serviços de AI do Microsoft Azure. O treinamento cobre desde fundamentos de IA, desenvolvimento com modelos generativos, criação de agentes inteligentes, até soluções completas envolvendo linguagem natural, visão computacional, fala e aplicações multimodais.

Ao longo do curso, os participantes irão trabalhar com o Azure AI Foundry, serviços de linguagem, visão, fala e ferramentas modernas de desenvolvimento de aplicações baseadas em Large Language Models (LLMs), incluindo RAG, fine-tuning e agentes multiagentes.

Objetivo

Após realizar este curso Microsoft AI-102T00, você será capaz de:

• Desenvolver aplicações de IA utilizando Azure
• Trabalhar com modelos generativos e LLMs
• Implementar soluções com RAG e fine-tuning
• Criar agentes inteligentes e multiagentes
• Desenvolver aplicações com linguagem, visão e fala
• Construir soluções multimodais completas

Publico Alvo

• Desenvolvedores
• Engenheiros de IA
• Engenheiros de Machine Learning
• Arquitetos de soluções
• Profissionais de cloud
• Profissionais de dados

Pre-Requisitos

• Conhecimentos básicos de programação
• Noções de cloud computing
• Familiaridade com APIs e desenvolvimento de aplicações

Materiais

Conteúdo Programatico

Module 1: Plan and prepare to develop AI solutions on Azure

1. Introduction
2. What is AI?
3. Azure AI Services
4. Azure AI Foundry
5. Developer Tools and SDKs
6. Responsible AI
7. Exercise: Prepare for an AI Development Project
8. Module Assessment
9. Summary

Module 2: Choose and deploy models from the model catalog in Azure AI Foundry portal

1. Introduction
2. Explore the Model Catalog
3. Deploy a Model to an Endpoint
4. Optimize Model Performance
5. Exercise: Explore, Deploy, and Chat with Language Models
6. Module Assessment
7. Summary

Module 3: Develop an AI app with the Azure AI Foundry SDK

1. Introduction
2. What is the Azure AI Foundry SDK?
3. Work with Project Connections
4. Create a Chat Client
5. Exercise: Create a Generative AI Chat App
6. Module Assessment
7. Summary

Module 4: Get started with prompt flow to develop language model apps in the Azure AI Foundry

1. Introduction
2. Understand the Development Lifecycle of a Large Language Model (LLM) App
3. Understand Core Components and Explore Flow Types
4. Explore Connections and Runtimes
5. Explore Variants and Monitoring Options
6. Exercise: Get Started with Prompt Flow
7. Module Assessment
8. Summary

Module 5: Develop a RAG-based solution with your own data using Azure AI Foundry

1. Introduction
2. Understand How to Ground Your Language Model
3. Make Your Data Searchable
4. Create a RAG-Based Client Application
5. Implement RAG in a Prompt Flow
6. Exercise: Create a Generative AI App That Uses Your Own Data
7. Module Assessment
8. Summary

Module 6: Fine-tune a language model with Azure AI Foundry

1. Introduction
2. Understand When to Fine-Tune a Language Model
3. Prepare Your Data to Fine-Tune a Chat Completion Model
4. Explore Fine-Tuning Language Models in Azure AI Foundry Portal
5. Exercise: Fine-Tune a Language Model
6. Module Assessment
7. Summary

Module 7: Implement a responsible generative AI solution in Azure AI Foundry

1. Introduction
2. Plan a Responsible Generative AI Solution
3. Map Potential Harms
4. Measure Potential Harms
5. Mitigate Potential Harms
6. Manage a Responsible Generative AI Solution
7. Exercise: Apply Content Filters to Prevent the Output of Harmful Content
8. Module Assessment
9. Summary

Module 8: Evaluate generative AI performance in Azure AI Foundry portal

1. Introduction
2. Assess the Model Performance
3. Manually Evaluate the Performance of a Model
4. Automated Evaluations
5. Exercise: Evaluate Generative AI Model Performance
6. Module Assessment
7. Summary

Module 9: Get started with AI agent development on Azure

1. Introduction
2. What Are AI Agents?
3. Options for Agent Development
4. Azure AI Foundry Agent Service
5. Exercise: Explore AI Agent Development
6. Module Assessment
7. Summary

Module 10: Develop an AI agent with Azure AI Foundry Agent Service

1. Introduction
2. What Is an AI Agent?
3. How to Use Azure AI Foundry Agent Service
4. Develop Agents with the Azure AI Foundry Agent Service
5. Exercise: Build an AI Agent
6. Module Assessment
7. Summary

Module 11: Integrate custom tools into your agent

1. Introduction
2. Why Use Custom Tools
3. Options for Implementing Custom Tools
4. How to Integrate Custom Tools
5. Exercise: Build an Agent with Custom Tools
6. Module Assessment
7. Summary

Module 12: Develop a multi-agent solution with Azure AI Foundry Agent Service

1. Introduction
2. Understand connected agents
3. Design a multi-agent solution with connected agents
4. Exercise – Develop a multi-agent app with Azure AI Foundry
5. Module assessment
6. Summary

Module 13: Integrate MCP Tools with Azure AI Agents

1. Introduction
2. Understand MCP Tool Discovery
3. Integrate Agent Tools Using an MCP Server and Client
4. Use Azure AI Agents with MCP Servers
5. Exercise: Connect MCP Tools to Azure AI Agents
6. Module Assessment
7. Summary

Module 14: Develop an AI agent with Semantic Kernel

1. Introduction
2. Understand Semantic Kernel AI Agents
3. Create an Azure AI Agent with Semantic Kernel
4. Add Plugins to Azure AI Agent
5. Exercise: Develop an Azure AI Agent with the Semantic Kernel SDK
6. Knowledge Check
7. Summary

Module 15: Orchestrate a multi-agent solution using Semantic Kernel

1. Introduction
2. Understand the Semantic Kernel Agent Framework
3. Understand agent orchestration
4. Use concurrent orchestration
5. Use sequential orchestration
6. Use group chat orchestration
7. Use handoff orchestration
8. Use Magentic orchestration
9. Manage orchestration runtime lifecycles
10. Exercise – Develop a multi-agent solution
11. Knowledge check
12. Summary

Module 16: Analyze text with Azure AI Language

1. Introduction
2. Provision an Azure AI Language Resource
3. Detect Language
4. Extract Key Phrases
5. Analyze Sentiment
6. Extract Entities
7. Extract Linked Entities
8. Exercise: Analyze Text
9. Module Assessment
10. Summary

Module 17: Create question answering solutions with Azure AI Language

1. Introduction
2. Understand question answering
3. Compare question answering to Azure AI Language understanding
4. Create a knowledge base
5. Implement multi-turn conversation
6. Test and publish a knowledge base
7. Use a knowledge base
8. Improve question answering performance
9. Exercise – Create a question answering solution
10. Module assessment
11. Summary

Module 18: Build a conversational language understanding model

1. Introduction
2. Understand prebuilt capabilities of the Azure AI Language service
3. Understand resources for building a conversational language understanding model
4. Define intents, utterances, and entities
5. Use patterns to differentiate similar utterances
6. Use pre-built entity components
7. Train, test, publish, and review a conversational language understanding model
8. Exercise – Build an Azure AI services conversational language understanding model
9. Module assessment
10. Summary

Module 19: Create a custom text classification solution

1. Introduction
2. Understand types of classification projects
3. Understand how to build text classification projects
4. Exercise – Classify text
5. Module assessment
6. Summary

Module 20: Custom named entity recognition

1. Introduction
2. Understand custom named entity recognition
3. Label your data
4. Train and evaluate your model
5. Exercise – Extract custom entities
6. Module assessment
7. Summary

Module 21: Translate text with Azure AI Translator service

1. Introduction
2. Provision an Azure AI Translator resource
3. Understand language detection, translation, and transliteration
4. Specify translation options
5. Define custom translations
6. Exercise – Translate text with the Azure AI Translator service
7. Module assessment
8. Summary

Module 22: Create speech-enabled apps with Azure AI services

1. Introduction
2. Provision an Azure resource for speech
3. Use the Azure AI Speech to Text API
4. Use the Text to Speech API
5. Configure audio format and voices
6. Use Speech Synthesis Markup Language (SSML)
7. Exercise – Create a speech-enabled app
8. Module assessment
9. Summary

Module 23: Translate speech with the Azure AI Speech service

1. Introduction
2. Provision an Azure resource for speech translation
3. Translate speech to text
4. Synthesize translations
5. Exercise – Translate speech
6. Module assessment
7. Summary

Module 24: Develop an audio-enabled generative AI application

1. Introduction
2. Deploy a multimodal model
3. Develop an audio-based chat app
4. Exercise – Develop an audio-enabled chat app
5. Module assessment
6. Summary

Module 25: Analyze images

1. Introduction
2. Provision an Azure AI Vision resource
3. Analyze an images
4. Exercise – Analyze images
5. Module assessment
6. Summary

Module 26: Read text in images

1. Introduction
2. Explore Azure AI options for reading text
3. Read text with Azure AI Vision Image Analysis
4. Exercise – Read text in images
5. Module assessment
6. Summary

Module 27: Detect, analyze, and recognize faces

1. Introduction
2. Plan a face detection, analysis, or recognition solution
3. Detect and analyze faces
4. Verify and identify faces
5. Responsible AI considerations for face-based solutions
6. Exercise – Detect and analyze faces
7. Module assessment
8. Summary

Module 28: Classify images

1. Introduction
2. Azure AI Custom Vision
3. Train an image classification model
4. Create an image classification client application
5. Exercise – Classify images
6. Module assessment
7. Summary

Module 29: Detect objects in images

1. Introduction
2. Use Azure AI Custom Vision for object detection
3. Train an object detector
4. Develop an object detection client application
5. Exercise – Detect objects in images
6. Module assessment
7. Summary

Module 30: Analyze video

1. Introduction
2. Understand Azure Video Indexer (Video Analyzer) capabilities
3. Extract custom insights
4. Use Video Analyzer widgets and APIs
5. Exercise – Analyze video
6. Module assessment
7. Summary

Module 31: Develop a vision-enabled generative AI application

1. Introduction
2. Deploy a multimodal model
3. Develop a vision-based chat app
4. Exercise – Develop a vision-enabled chat app
5. Module assessment
6. Summary

Module 32: Generate images with AI

1. Introduction
2. What are image-generation models?
3. Explore image-generation models in Azure AI Foundry portal
4. Create a client application that uses an image generation model
5. Exercise – Generate images with AI
6. Module assessment
7. Summary

Module 33: Create a multimodal analysis solution with Azure AI Content Understanding

1. Introduction
2. What is Azure AI Content Understanding?
3. Create a Content Understanding analyzer
4. Use the Content Understanding REST API
5. Exercise – Extract information from multimodal content
6. Module assessment
7. Summary

Module 34: Create an Azure AI Content Understanding client application

1. Introduction
2. Prepare to use the AI Content Understanding REST API
3. Create a Content Understanding analyzer
4. Analyze content
5. Exercise – Develop a Content Understanding client application
6. Module assessment
7. Summary