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Designing and Implementing a Microsoft Azure AI Solution

AI-102 Designing and Implementing an Azure AI Solution is intended for software developers wanting to build AI infused applications that leverage Azure AI Services, Azure AI Search, and Azure OpenAI....

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$2,380 USD GSA  $1,764.48
Duration 4 days
Course Code AI-102T00
Available Formats Classroom, Virtual

Upcoming Course Dates

  • Guaranteed to Run
    Jun 10, 2025 - Jun 13, 2025 (4 days)
    Live Virtual | 9:00AM – 5:00PM EDT
    Language English
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    Live Virtual |9:00AM – 5:00PM EDT
    Live Virtual | 9:00AM – 5:00PM EDT
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  • Aug 12, 2025 - Aug 15, 2025 (4 days)
    Live Virtual | 9:00AM – 5:00PM EDT
    Language English
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    Live Virtual |9:00AM – 5:00PM EDT
    Live Virtual | 9:00AM – 5:00PM EDT
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  • Oct 14, 2025 - Oct 17, 2025 (4 days)
    Live Virtual | 9:00AM – 5:00PM EDT
    Language English
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    Live Virtual |9:00AM – 5:00PM EDT
    Live Virtual | 9:00AM – 5:00PM EDT
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Overview

AI-102 Designing and Implementing an Azure AI Solution is intended for software developers wanting to build AI infused applications that leverage Azure AI Services, Azure AI Search, and Azure OpenAI. The course will use C# or Python as the programming language.

Audience Profile

This course was designed for 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.

Skills Gained

After completing this course, students will be able to:

  • Describe considerations for AI-enabled application development
  • Create, configure, deploy, and secure Azure Cognitive Services
  • Develop applications that analyze text
  • Develop speech-enabled applications
  • Create applications with natural language understanding capabilities
  • Create QnA applications
  • Create conversational solutions with bots
  • Use computer vision services to analyze images and videos
  • Create custom computer vision models
  • Develop applications that detect, analyze, and recognize faces
  • Develop applications that read and process text in images and documents
  • Create intelligent search solutions for knowledge mining

Prerequisites

Before attending this course, students must have:

  • Knowledge of Microsoft Azure and ability to navigate the Azure portal
  • Knowledge of either C# orPython
  • Familiarity with JSON and REST programming semantics
To gain C# or Python skills, complete the freeTake your first steps with C# or Take your first steps with Python learning path before attending the course.
If you are new to artificial intelligence, and want an overview of AI capabilities on Azure, consider completing the Azure AI Fundamentals certification before taking this one.

Course Details

Outline

  • Plan and prepare to develop AI solutions on Azure
    • What is AI?
    • Azure AI services
    • Azure AI Foundry
    • Developer tools and SDKs
    • Responsible AI
    • Exercise - Prepare for an AI development project
    • Module assessment
  • Create and consume Azure AI services
    • Provision an Azure AI services resource
    • Identify endpoints and keys
    • Use a REST API
    • Use an SDK
    • Exercise - Use Azure AI services
    • Module assessment
  • Secure Azure AI services
    • Consider authentication
    • Implement network security
    • Exercise - Manage Azure AI Services Security
    • Module assessment
  • Monitor Azure AI services
    • Monitor cost
    • Create alerts
    • View metrics
    • Manage diagnostic logging
    • Exercise - Monitor Azure AI services
    • Module assessment
  • Deploy Azure AI services in containers
    • Understand containers
    • Use Azure AI services containers
    • Exercise - Use a container
    • Module assessment
  • Use AI responsibly with Azure AI Content Safety
    • What is Content Safety
    • How does Azure AI Content Safety work?
    • When to use Azure AI Content Safety
    • Exercise - Implementing Azure AI Content Safety
    • Module assessment
  • Plan and prepare to develop AI solutions on Azure
    • What is AI?
    • Azure AI services
    • Azure AI Foundry
    • Developer tools and SDKs
    • Responsible AI
    • Exercise - Prepare for an AI development project
    • Module assessment
  • Choose and deploy models from the model catalog in Azure AI Foundry portal
    • Explore the model catalog
    • Deploy a model to an endpoint
    • Optimize model performance
    • Exercise - Explore, deploy, and chat with language models
    • Module assessment
  • Develop an AI app with the Azure AI Foundry SDK
    • What is the Azure AI Foundry SDK?
    • Work with project connections
    • Create a chat client
    • Exercise - Create a generative AI chat app
    • Module assessment
  • Get started with prompt flow to develop language model apps in the Azure AI Foundry
    • Understand the development lifecycle of a large language model (LLM) app
    • Understand core components and explore flow types
    • Explore connections and runtimes
    • Explore variants and monitoring options
    • Exercise - Get started with prompt flow
    • Module assessment
  • Develop a RAG-based solution with your own data using Azure AI Foundry
    • Understand how to ground your language model
    • Make your data searchable
    • Create a RAG-based client application
    • Implement RAG in a prompt flow
    • Exercise - Create a generative AI app that uses your own data
    • Module assessment
  • Fine-tune a language model with Azure AI Foundry
    • Understand when to fine-tune a language model
    • Prepare your data to fine-tune a chat completion model
    • Explore fine-tuning language models in Azure AI Studio
    • Exercise - Fine-tune a language model
    • Module assessment
  • Implement a responsible generative AI solution in Azure AI Foundry
    • Plan a responsible generative AI solution
    • Map potential harms
    • Measure potential harms
    • Mitigate potential harms
    • Manage a responsible generative AI solution
    • Exercise - Apply content filters to prevent the output of harmful content
    • Module assessment
  • Evaluate generative AI performance in Azure AI Foundry portal
    • Assess the model performance
    • Manually evaluate the performance of a model
    • Automated evaluations
    • Assess the performance of your generative AI apps
    • Exercise - Evaluate generative AI model performance
    • Module assessment
  • Develop applications with Azure OpenAI Service
    • Access Azure OpenAI Service
    • Integrate OpenAI into an app
    • OpenAI prompt engineering
    • Provide context with prompt engineering
    • Construct code from natural language
    • Exercise - Get started with Azure OpenAI Service
    • Module assessment
  • Implement Retrieval Augmented Generation (RAG) with Azure OpenAI Service
    • 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 assessment
  • Generate images with AI
    • What is DALL-E?
    • Explore DALL-E in Azure AI Foundry portal
    • Use the Azure OpenAI REST API to consume DALL-E models
    • Exercise - Generate images with a DALL-E model
    • Module assessment
  • Get started with AI agent development on Azure
    • What are AI agents?
    • Options for agent development
    • Azure AI Agent Service
    • Exercise - Explore AI Agent development
    • Module assessment
  • Develop an AI agent with Azure AI Agent Service
    • What is an AI agent
    • How to use Azure AI Agent Service
    • Develop agents with the Azure AI Agent Service
    • Exercise - Build an AI agent
    • Module assessment
  • Integrate custom tools into your agent
    • Why use custom tools
    • Options for implementing custom tools
    • How to integrate custom tools
    • Exercise - Build an agent with custom tools
    • Module assessment
  • Develop an AI agent with Semantic Kernel
    • Understand Semantic Kernel AI agents
    • Create an Azure AI agent with Semantic Kernel
    • Add plugins to Azure AI agent
    • Exercise - Develop an Azure AI agent with the Semantic Kernel SDK
    • Knowledge check
  • Orchestrate a multi-agent solution using Semantic Kernel
    • Understand the Semantic Kernel Agent Framework
    • Create an agent group chat
    • Design an agent selection strategy
    • Define a chat termination strategy
    • Exercise - Develop a multi-agent solution
    • Knowledge check
  • Analyze text with Azure AI Language
    • Provision an Azure AI Language resource
    • Detect language
    • Extract key phrases
    • Analyze sentiment
    • Extract entities
    • Extract linked entities
    • Exercise - Analyze text
    • Module assessment
  • Create question answering solutions with Azure AI Language
    • 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 assessment
  • Build a conversational language understanding model
    • 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 assessment
  • Create a custom text classification solution
    • Understand types of classification projects
    • Understand how to build text classification projects
    • Exercise - Classify text
    • Module assessment
  • Custom named entity recognition
    • Understand custom named entity recognition
    • Label your data
    • Train and evaluate your model
    • Exercise - Extract custom entities
    • Module assessment
  • Translate text with Azure AI Translator service
    • 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 assessment
  • Create speech-enabled apps with Azure AI services
    • 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 assessment
  • Translate speech with the Azure AI Speech service
    • Provision an Azure resource for speech translation
    • Translate speech to text
    • Synthesize translations
    • Exercise - Translate speech
    • Module assessment
  • Analyze images
    • Provision an Azure AI Vision resource
    • Analyze an image
    • Exercise - Analyze images
    • Module assessment
  • Read text in images and documents with the Azure AI Vision Service
    • Explore Azure AI options for reading text
    • Use the Read API
    • Exercise - Read text in images
    • Module assessment
  • Detect, analyze, and recognize faces
    • Plan a face detection, analysis, or recognition solution
    • Detect and analyze faces
    • Verify and identify faces
    • Responsible AI considerations for face-based solutions
    • Exercise - Detect and analyze faces
    • Module assessment
  • Classify images
    • Provision Azure resources for Azure AI Custom Vision
    • Understand image classification
    • Train an image classifier
    • Exercise - Classify images with Azure AI Custom Vision
    • Module assessment
  • Detect objects in images
    • Understand object detection
    • Train an object detector
    • Consider options for labeling images
    • Exercise - Detect objects in images with Azure AI Custom Vision
    • Module assessment
  • Analyze video
    • Understand Azure Video Indexer capabilities
    • Extract custom insights
    • Use Video Analyzer widgets and APIs
    • Exercise - Analyze video
    • Module assessment
  • Develop a vision-enabled generative AI application
    • Deploy a multimodal model
    • Develop a vision-based chat app
    • Exercise - Develop a vision-enabled chat app
    • Module assessment
  • Generate images with AI
    • What is DALL-E?
    • Explore DALL-E in Azure AI Foundry portal
    • Use the Azure OpenAI REST API to consume DALL-E models
    • Exercise - Generate images with a DALL-E model
    • Module assessment
  • Create an Azure AI Search solution
    • 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 assessment
  • Create a custom skill for Azure AI Search
    • Define the custom skill schema
    • Add a custom skill
    • Custom text classification skill
    • Machine learning custom skill
    • Exercise - Create a Custom Skill for Azure AI Search
    • Module assessment
  • Create a knowledge store with Azure AI Search
    • Define projections
    • Define a knowledge store
    • Exercise - Create a knowledge store
    • Module assessment
  • Implement advanced search features in Azure AI Search
    • Improve the ranking of a document with term boosting
    • Improve the relevance of results by adding scoring profiles
    • Improve an index with analyzers and tokenized terms
    • Enhance an index to include multiple languages
    • Improve search experience by ordering results by distance from a given reference point
    • Exercise: Implement enhancements to search results
    • Module assessment
  • Search data outside the Azure platform in Azure AI Search using Azure Data Factory
    • Index data from external data sources using Azure Data Factory
    • Index any data using the Azure AI Search push API
    • Exercise: Add to an index using the push API
    • Module assessment
  • Maintain an Azure AI Search solution
    • Manage security of an Azure AI Search solution
    • Optimize performance of an Azure AI Search solution
    • Manage costs of an Azure AI Search solution
    • Improve reliability of an Azure AI Search solution
    • Monitor an Azure AI Search solution
    • Debug search issues using the Azure portal
    • Exercise - Debug search issues
    • Module assessment
  • Perform search reranking with semantic ranking in Azure AI Search
    • What is semantic ranking?
    • Set up semantic ranking
    • Exercise - Use semantic ranking on an index
    • Module assessment
  • Perform vector search and retrieval in Azure AI Search
    • What is vector search?
    • Prepare your search
    • Understand embedding
    • Exercise - Use the REST API to run vector search queries
    • Module assessment
  • Plan an Azure AI Document Intelligence solution
    • Understand AI Document Intelligence
    • Plan Azure AI Document Intelligence resources
    • Choose a model type
    • Module assessment
  • Use prebuilt Document intelligence models
    • 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 assessment
  • Extract data from forms with Azure Document intelligence
    • 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 assessment
  • Create a composed Document intelligence model
    • Understand composed models
    • Assemble composed models
    • Exercise: Create a composed model
    • Module assessment
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Schedule

4 options available

  • Guaranteed to Run
    Jun 10, 2025 - Jun 13, 2025 (4 days)
    Live Virtual | 9:00AM – 5:00PM EDT
    Language English
    Select from 1 options below
    Live Virtual |9:00AM – 5:00PM EDT
    Live Virtual | 9:00AM – 5:00PM EDT
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  • Aug 12, 2025 - Aug 15, 2025 (4 days)
    Live Virtual | 9:00AM – 5:00PM EDT
    Language English
    Select from 1 options below
    Live Virtual |9:00AM – 5:00PM EDT
    Live Virtual | 9:00AM – 5:00PM EDT
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  • Oct 14, 2025 - Oct 17, 2025 (4 days)
    Live Virtual | 9:00AM – 5:00PM EDT
    Language English
    Select from 1 options below
    Live Virtual |9:00AM – 5:00PM EDT
    Live Virtual | 9:00AM – 5:00PM EDT
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    Enroll Add to quote
  • Dec 9, 2025 - Dec 12, 2025 (4 days)
    Live Virtual | 9:00AM – 5:00PM EST
    Language English
    Select from 1 options below
    Live Virtual |9:00AM – 5:00PM EST
    Live Virtual | 9:00AM – 5:00PM EST
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FAQ

How do I get a Microsoft exam voucher?

Pearson Vue Exam vouchers can be requested and ordered with your course purchase or can be ordered separately by clicking here.

  • Vouchers are non-refundable and non-returnable. Vouchers expire 12 months from the date they are issued unless otherwise specified in the terms and conditions.
  • Voucher expiration dates cannot be extended. The exam must be taken by the expiration date printed on the voucher.

Do Microsoft courses come with post lab access?

Most Microsoft official courses will include post-lab access ranging from 30 to 180 calendar days after instructor led course delivery. A lab training key in class will be provided that can be leveraged to continue connecting to a remote lab environment for the individual course attendee.

Does the course schedule include a Lunchbreak?

Lunch is normally an hour-long after 3-3.5 hours of the class day.

What languages are used to deliver training?

Microsoft courses are conducted in English unless otherwise specified.

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