AI-102: Designing and Implementing an Azure AI Solution Training

AI-102: Develop AI solutions in Azure

Microsoft Certified: Azure AI Engineer Associate

Dynamics Edge courses and labs are enhanced Instructor-Led Training (ILT) materials, purpose-built for live, guided instruction, structured learning and practical, work-ready skills development.

Unlike Microsoft Learn paths—which are designed for self-paced study—our ILT content follows a carefully crafted curriculum tailored for real-time engagement, interactive Q&A, The structure and flow of our materials are intentionally different to support deeper learning and immediate application.

Overview

Audience Profile

Software engineers concerned with building, managing and deploying AI solutions that leverage Azure AI Services, Azure AI Search, and Azure OpenAI. They are familiar with C# or Python and have knowledge on using REST-based APIs to build computer vision, language analysis, knowledge mining, intelligent search, and generative AI solutions on Azure.

Course outline

Module 1: Prepare to develop AI solutions on Azure

• Introduction
• Define artificial intelligence
• Understand AI-related terms
• Understand considerations for AI Engineers
• Understand considerations for responsible AI
• Understand capabilities of Azure Machine Learning
• Understand capabilities of Azure AI Services
• Understand capabilities of the Azure OpenAI Service
• Understand capabilities of Azure Cognitive Search

Module 2: Create and consume Azure AI services

• Introduction
• Provision an Azure AI services resource
• Identify endpoints and keys
• Use a REST API
• Use an SDK
• Exercise – Use Azure AI services

Module 3: Secure Azure AI services

• Introduction
• Consider authentication
• Implement network security
• Exercise – Manage Azure AI Services Security

Module 4: Monitor Azure AI services

• Introduction
• Monitor cost
• Create alerts
• View metrics
• Manage diagnostic logging
• Exercise – Monitor Azure AI services

Module 5: Deploy Azure AI services in containers

• Introduction
• Understand containers
• Use Azure AI services containers
• Exercise – Use a container

Module 6: Analyze images

• Introduction
• Provision an Azure AI Vision resource
• Analyze an image
• Generate a smart-cropped thumbnail and remove background
• Exercise – Analyze images with Azure AI Vision

Module 7: Classify images

• Introduction
• Provision Azure resources for Azure AI Custom Vision
• Understand image classification
• Train an image classifier
• Exercise – Classify images with Azure AI Custom Vision

Module 8: Detect, analyze, and recognize faces

• Introduction
• Identify options for face detection analysis and identification
• Understand considerations for face analysis
• Detect faces with the Azure AI Vision service
• Understand capabilities of the face service
• Compare and match detected faces
• Implement facial recognition
• Exercise – Detect, analyze, and identify faces

Module 9: Read Text in images and documents with the Azure AI Vision Service

• Introduction
• Explore Azure AI Vision options for reading text
• Use the Read API
• Exercise – Read text in images

Module 10: Analyze video

• Introduction
• Understand Azure Video Indexer capabilities
• Extract custom insights
• Use Video Analyzer widgets and APIs
• Exercise – Analyze video

Module 11: Analyze text with Azure AI Language

• Introduction
• Provision an Azure AI Language resource
• Detect language
• Extract key phrases
• Analyze sentiment
• Extract entities
• Extract linked entities
• Exercise – Analyze text

Module 12: Create question answering solutions with Azure AI Language

• Introduction
• Understand question answering
• Compare question answering to Azure AI Language understanding
• Create a knowledge base
• Implement multi-turn conversation
• Test and publish a knowledge base
• Use a knowledge base
• Improve question answering performance
• Exercise – Create a question answering solution

Module 13: Build a conversational language understanding model

• Introduction
• Understand prebuilt capabilities of the Azure AI Language service
• Understand resources for building a conversational language understanding model
• Define intents, utterances, and entities
• Use patterns to differentiate similar utterances
• Use pre-built entity components
• Train, test, publish, and review a conversational language understanding model
• Exercise – Build an Azure AI services conversational language understanding model

Module 14: Create a custom text classification solution

Module 15: Custom named entity recognition

Module 16: Translate text with Azure AI Translator service

• Introduction
• Provision an Azure AI Translator resource
• Understand language detection, translation, and transliteration
• Specify translation options
• Define custom translations
• Exercise – Translate text with the Azure AI Translator service

Module 17: Create speech-enabled apps with Azure AI services

• Introduction
• Provision an Azure resource for speech
• Use the Azure AI Speech to Text API
• Use the text to speech API
• Configure audio format and voices
• Use Speech Synthesis Markup Language
• Exercise – Create a speech-enabled app

Module 18: Translate speech with the Azure AI Speech service

• Introduction
• Provision an Azure resource for speech translation
• Translate speech to text
• Synthesize translations
• Exercise – Translate speech

Module 19: Create an Azure AI Search solution

• Introduction
• Manage capacity
• Understand search components
• Understand the indexing process
• Search an index
• Apply filtering and sorting
• Enhance the index
• Exercise – Create a search solution

Module 20: Create a custom skill for Azure AI Search

• Introduction
• Create a custom skill
• Add a custom skill to a skillset
• Exercise – Implement a custom skill

Module 21: Create a knowledge store with Azure AI Search

• Introduction
• Define projections
• Define a knowledge store
• Exercise – Create a knowledge store

Module 22: Plan an Azure AI Document Intelligence solution

• Introduction
• Understand AI Document Intelligence
• Plan Azure AI Document Intelligence resources
• Choose a model type

Module 23: Use prebuilt Document intelligence models

• Introduction
• Understand prebuilt models
• Use the General Document, Read, and Layout models
• Use financial, ID, and tax models
• Exercise – Analyze a document using Azure AI Document Intelligence

Module 24: Extract data from forms with Azure Document intelligence

• Introduction
• What is Azure Document Intelligence?
• Get started with Azure Document Intelligence
• Train custom models
• Use Azure Document Intelligence models
• Use the Azure Document Intelligence Studio
• Exercise – Extract data from custom forms

Module 25: Get started with Azure OpenAI Service

• Introduction
• Access Azure OpenAI Service
• Use Azure OpenAI Studio
• Explore types of generative AI models
• Deploy generative AI models
• Use prompts to get completions from models
• Test models in Azure OpenAI Studio’s playgrounds
• Exercise – Get started with Azure OpenAI Service

Module 26: Build natural language solutions with Azure OpenAI Service

• Introduction
• Integrate Azure OpenAI into your app
• Use Azure OpenAI REST API
• Use Azure OpenAI SDK
• Exercise – Integrate Azure OpenAI into your app

Module 27: Apply prompt engineering with Azure OpenAI Service

• Introduction
• Understand prompt engineering
• Write more effective prompts
• Provide context to improve accuracy
• Exercise – Utilize prompt engineering in your application

Module 28: Generate code with Azure OpenAI Service

• Introduction
• Construct code from natural language
• Complete code and assist the development process
• Fix bugs and improve your code
• Exercise – Generate and improve code with Azure OpenAI Service

Module 29: Generate images with Azure OpenAI Service

• Introduction
• What is DALL-E?
• Explore DALL-E in Azure OpenAI Studio
• Use the Azure OpenAI REST API to consume DALL-E models
• Exercise – Generate images with a DALL-E model

Module 30: Implement Retrieval Augmented Generation (RAG) with Azure OpenAI Service

• Introduction
• Understand Retrieval Augmented Generation (RAG) with Azure OpenAI Service
• Add your own data source
• Chat with your model using your own data
• Exercise – Add your data for RAG with Azure OpenAI Service

Module 31: Fundamentals of Responsible Generative AI

• Introduction
• Plan a responsible generative AI solution
• Identify potential harms
• Measure potential harms
• Mitigate potential harms
• Operate a responsible generative AI solution
• Exercise – Explore content filters in Azure OpenAI