Designing and Implementing a Microsoft Azure AI Solution

MS-AI102T00

Type

Virtual

Classroom ILT

Skill Level

Microsoft Techs: Intermediate

Available dates

Learning Path

Robotic Process Automation and AI

Virtual

Duration

5 Days

1 Day

TYPE

Virtual

Classroom ILT

LEARNING PATH

Robotic Process Automation and AI

SKILL LEVEL

Microsoft Techs: Intermediate

DURATION

5 Days

AVAILABLE DATES

Available on request.

For more information click on button below

Course Type

Virtual Course (R11 700,00)

In-Person Course (R12 200,00)

Total Price: -

Introduction

AI-102: Develop AI solutions in Azure is intended for software developers wanting to build AI infused applications that leverage Azure AI Foundry and other Azure AI services. Topics in this course include developing generative AI apps, building AI agents, and solutions that implement computer vision and information extraction.

Audience profile

This course is designed for individuals who want to build, manage, and deploy AI solutions using Azure AI services. The primary audience for this course includes:

Software Engineers: Those who are focused on developing AI-infused applications leveraging Azure AI Services, Azure AI Search, and Azure OpenAI
AI Engineers: Professionals responsible for creating and maintaining AI solutions, including computer vision, language analysis, knowledge mining, intelligent search, and generative AI
Developers: Individuals with experience in programming languages like C# or Python and familiarity with REST-based APIs

Pre-requisites

Before attending this course, delegates must have:

Knowledge of Microsoft Azure and ability to navigate the Azure portal
Knowledge of either C#, Python, or JavaScript

Course Objectives

After completing this course, students will be able to:

Create, configure, deploy, and secure Azure Cognitive Services
Integrate speech services
Integrate text analytics
Create language understanding capabilities with LUIS
Create and manage Azure Cognitive Search solutions
Create intelligent agents using the Bot Framework
Implement Computer Vision solutions

Course content

Module 1: Plan and prepare to develop AI solutions on Azure
Microsoft Azure offers multiple services that enable developers to build amazing AI-powered solutions. Proper planning and preparation involve identifying the services you’ll use and creating an optimal working environment for your development team.
Lessons:
Introduction	Responsible Al
What is Al?	Exercise – Prepare for an Al development project
Azure Al services	Module assessment
Azure Al Foundry	Summary
Developer tools and SDKs
After completing this module, you’ll be able to: Identify common AI capabilities that you can implement in applications Describe Azure AI Services and considerations for using them Describe Azure AI Foundry and considerations for using it Identify appropriate developer tools and SDKs for an AI project Describe considerations for responsible AI
Module 2: Choose and deploy models from the model catalog in Azure AI Foundry portal
Choose the various language models that are available through the Azure AI Foundry’s model catalog. Understand how to select, deploy, and test a model, and to improve its performance.
Lessons:
Introduction	Exercise – Explore, deploy, and chat with language models
Explore the model catalog	Module assessment
Deploy a model to an endpoint	Summary
Optimize model performance
After completing this module, you will be able to: Select a language model from the model catalog Deploy a model to an endpoint Test a model and improve the performance of the model
Module 3: Develop an AI app with the Azure AI Foundry SDK.
Use the Azure AI Foundry SDK to develop AI applications with Azure AI Foundry projects.
Lessons:
Introduction	Exercise – Create a generative Al chat app
What is the Azure Al Foundry SDK?	Module assessment
Work with project connections	Summary
Create a chat client
After completing this module, you will be able to: Describe capabilities of the Azure AI Foundry SDK Use the Azure AI Foundry SDK to work with connections in projects Use the Azure AI Foundry SDK to develop an AI chat app
Module 4: Get started with prompt flow to develop language model apps in the Azure AI Foundry
Learn about how to use prompt flow to develop applications that leverage language models in the Azure AI Foundry.
Lessons:
Introduction	Explore variants and monitoring options
Understand the development lifecycle of a large language model (LLM) app	Exercise – Get started with prompt flow
Understand core components and explore flow types	Module assessment
Explore connections and runtimes	Summary
After completing this module, you will be able to: Understand the development lifecycle when creating language model applications Understand what a flow is in prompt flow Explore the core components when working with prompt flow
Module 5: Develop a RAG-based solution with your own data using Azure AI Foundry
Retrieval Augmented Generation (RAG) is a common pattern used in generative AI solutions to ground prompts with your data. Azure AI Foundry provides support for adding data, creating indexes, and integrating them with generative AI models to help you build RAG-based solutions.
Lessons:
Introduction	Implement RAG in a prompt flow
Understand how to ground your language model	Exercise – Create a generative Al app that uses your own data
Make your data searchable	Module assessment
Create a RAG-based client application	Summary
After completing this module, you will be able to: Identify the need to ground your language model with Retrieval Augmented Generation (RAG) Index your data with Azure AI Search to make it searchable for language models Build an agent using RAG on your own data in the Azure AI Foundry portal
Module 6: Fine-tune a language model with Azure AI Foundry
Train a base language model on a chat-completion task. The model catalog in Azure AI Foundry offers many open-source models that can be fine-tuned for your specific model behaviour needs.
Lessons:
Introduction	Exercise – Fine-tune a language model
Understand when to fine-tune a language model	Module assessment
Prepare your data to fine-tune a chat completion model	Summary
Explore fine-tuning language models in Azure Al Studio
After completing this module, you will be able to use the Azure AI Language service to: Understand when to fine-tune a model Prepare your data to fine-tune a chat completion model Fine-tune a base model in the Azure AI Foundry portal
Module 7: Implement a responsible generative AI solution in Azure AI Foundry
Generative AI enables amazing creative solutions but must be implemented responsibly to minimize the risk of harmful content generation.
Lessons:
Introduction	Manage a responsible generative Al solution
Plan a responsible generative Al solution	Exercise – Apply content filters to prevent the output of harmful content
Map potential harms	Module assessment
Measure potential harms	Summary
Mitigate potential harms
By the end of this module, you’ll be able to: Describe an overall process for responsible generative AI solution development Identify and prioritize potential harms relevant to a generative AI solution Measure the presence of harms in a generative AI solution Mitigate harms in a generative AI solution Prepare to deploy and operate a generative AI solution responsibly
Module 8: Evaluate generative AI performance in Azure AI Foundry portal
Evaluating copilots is essential to ensure your generative AI applications meet user needs, provide accurate responses, and continuously improve over time. Discover how to assess and optimize the performance of your generative AI applications using the tools and features available in the Azure AI Studio.
Lessons:
Introduction	Assess the performance of your generative Al apps
Assess the model performance	Exercise – Evaluate generative Al model performance
Manually evaluate the performance of a model	Module assessment
Automated evaluations	Summary
After completing this module, you will be able to: Understand model benchmarks Perform manual evaluations Assess your generative AI apps with AI-assisted metrics Configure evaluation flows in the Azure AI Foundry portal
Module 9: Get started with AI agent development on Azure
AI agents represent the next generation of intelligent applications. Learn how they can be developed and used on Microsoft Azure.
Lessons:
Introduction	Exercise – Explore Al Agent development
What are Al agents?	Module assessment
Options for agent development	Summary
Azure Al Agent Service
After completing this module, you will be able to: Describe core concepts related to AI agents Describe options for agent development Create and test an agent in the Azure AI Foundry portal
Module 10: Develop an AI agent with Azure AI Foundry Agent Service
This module provides engineers with the skills to begin building agents with Azure AI Foundry Agent Service.
Lessons:
Introduction	Exercise – Build an Al agent
What is an Al agent	Module assessment
How to use Azure Al Foundry Agent Service	Summary
Develop agents with the Azure Al Foundry Agent Service
By the end of this module, you’ll be able to: Describe the purpose of AI agents Explain the key features of Azure AI Foundry Agent Service Build an agent using the Foundry Agent Service Integrate an agent in the Foundry Agent Service into your own application
Module 11: Integrate custom tools into your agent
Built-in tools are useful, but they may not meet all your needs. In this module, learn how to extend the capabilities of your agent by integrating custom tools for your agent to use.
Lessons:
Introduction	Exercise – Build an agent with custom tools
Why use custom tools	Module assessment
Options for implementing custom tools	Summary
How to integrate custom tools
After completing this module, you will be able to: Describe the benefits of using custom tools with your agent Explore the different options for custom tools Build an agent that integrates custom tools using the Azure AI Agent Service
Module 12: Develop a multi-agent solution with Azure AI Foundry Agent Service
Break down complex tasks with intelligent collaboration. Learn how to design multi-agent solutions using connected agents.
Lessons:
Introduction	Exercise – Develop a multiagent app with Azure Al Foundry
Understand connected agents	Module assessment
Design a multi-agent solution with connected agents	Summary
After completing this module, you’ll be able to: Describe how connected agents enable modular, collaborative workflows Design a multi-agent solution by defining main agent tools and connected agent roles Build and run a connected agent solution
Module 13: Integrate MCP Tools with Azure AI Agents
Enable dynamic tool access for your Azure AI agents. Learn how to connect MCP-hosted tools and integrate them seamlessly into agent workflows.
Lessons:
Introduction	Exercise – Connect MCP tools to Azure Al Agents
Understand MCP tool discovery	Module assessment
Integrate agent tools using an MCP server and client	Summary
Use Azure Al agents with MCP servers
After completing this module, you’ll be able to: Explain the roles of the MCP server and client in tool discovery and innovation Wrap MCP tools as asynchronous functions and register them with Azure AI agents Build an Azure AI agent that dynamically accesses and calls MCP tools during runtime
Module 14: Develop an AI agent with Semantic Kernel
This module provides engineers with the skills to begin building Azure AI Agent Service agents with Semantic Kernel.
Lessons:
Introduction	Exercise – Develop an Azure Al agent with the Semantic Kernel SDK
Understand Semantic Kernel Al agents	Knowledge check
Create an Azure Al agent with Semantic Kernel	Summary
Add plugins to Azure Al agent
After completing this module, you’ll be able to: Use Semantic Kernel to connect to an Azure AI Foundry project Create Azure AI Agent Service agents using the Semantic Kernel SDK Integrate plugin functions with your AI agent
Module 15: Orchestrate a multi-agent solution using Semantic Kernel
Learn how to use the Semantic Kernel SDK to develop your own AI agents that can collaborate for a multi-agent solution.
Lessons:
Introduction	Define a chat termination strategy
Understand the Semantic Kernel Agent Framework	Exercise – Develop a multiagent solution
Create an agent group chat	Knowledge check
Design an agent selection strategy	Summary
After completing this module, you’ll be able to: Build AI agents using the Semantic Kernel SDK Develop multi-agent solutions Create custom selection and termination strategies for agent collaboration
Module 16: Analyze text with Azure AI Language
The Azure AI Language service enables you to create intelligent apps and services that extract semantic information from text.
Lessons:
Introduction	Extract entities
Provision an Azure Al Language resource	Extract linked entities
Detect language	Exercise – Analyze text
Extract key phrases	Module assessment
Analyze sentiment	Summary
After completing this module, you’ll be able to: Detect language from text Analyze text sentiment Extract key phrases, entities, and linked entities
Module 17: Create question answering solutions with Azure AI Language
The question answering capability of the Azure AI Language service makes it easy to build applications in which users ask questions using natural language and receive appropriate answers.
Lessons:
Introduction	Use a knowledge base
Understand question answering	Improve question answering performance
Compare question answering to Azure Al Language understanding	Exercise – Create a question answering solution
Create a knowledge base − Implement multi-turn conversation	Module assessment
Test and publish a knowledge base	Summary
After completing this module, you’ll be able to: Understand question answering and how it compares to language understanding Create, test, publish, and consume a knowledge base Implement multi-turn conversation and active learning Create a question answering bot to interact with using natural language
Module 18: Build a conversational language understanding model
The Azure AI Language conversational language understanding service (CLU) enables you to train a model that apps can use to extract meaning from natural language.
Lessons:
Introduction	Use pre-built entity components
Understand prebuilt capabilities of the Azure Al Language service	Train, test, publish, and review a conversational language understanding model
Understand resources for building a conversational language understanding model	Exercise – Build an Azure Al services conversational language understanding model
Define intents, utterances, and entities	Module assessment
Use patterns to differentiate similar utterances	Summary
After completing this module, you’ll be able to: Provision Azure resources for Azure AI Language resource Define intents, utterances, and entities Use patterns to differentiate similar utterances Use pre-built entity components Train, test, publish, and review an Azure AI Language model
Module 19: Create a custom text classification solution
The Azure AI Language service enables processing of natural language to use in your own app. Learn how to build a custom text classification project.
Lessons:
Introduction	Developer tools and SDKs
What is Al?	Responsible Al
Azure Al services	Exercise – Prepare for an Al development project
Azure Al Foundry	Module assessment
Summary
Introduction	Exercise – Classify text
Understand types of classification projects	Module assessment
Understand how to build text classification projects	Summary
After completing this module, you’ll be able to: Understand types of classification projects Build a custom text classification project Tag data, train, and deploy a model Submit classification tasks from your own app
Module 20: Custom named entity recognition
Build a custom entity recognition solution to extract entities from unstructured documents.
Lessons:
Introduction	Exercise – Extract custom entities
Understand custom named entity recognition	Module assessment
Label your data	Summary
Train and evaluate your model
After completing this module, you’ll be able to: Understand tagging entities in extraction projects Understand how to build entity recognition projects
Module 21: Translate text with Azure AI Translator service
The Translator service enables you to create intelligent apps and services that can translate text between languages.
Lessons:
Introduction	Define custom translations
Provision an Azure Al Translator resource	Exercise – Translate text with the Azure Al Translator service
Understand language detection, translation, and transliteration	Module assessment
Specify translation options	Summary
After completing this module, you’ll be able to: Provision a Translator resource Understand language detection, translation, and transliteration Specify translation options Define custom translations
Module 22: Create speech enabled apps with Azure AI services
The Azure AI Speech service enables you to build speech enabled applications. This module focuses on using the speech-to-text and text to speech APIs, which enable you to create apps that are capable of speech recognition and speech synthesis.
Lessons:
Introduction	Use Speech Synthesis Markup Language
Provision an Azure resource for speech	Exercise – Create a speech enabled app
Use the Azure Al Speech to Text API	Module assessment
Use the text to speech API	Summary
Configure audio format and voices
After completing this module, you’ll be able to: Provision an Azure resource for the Azure AI Speech service Implement speech recognition with the Azure AI Speech to text API Use the Text to speech API to implement speech synthesis Configure audio format and voices Use Speech Synthesis Markup Language (SSML)
Module 23: Translate speech with the Azure AI Speech service
Translation of speech builds on speech recognition by recognizing and transcribing spoken input in a specified language and returning translations of the transcription in one or more other languages.
Lessons:
Introduction	Exercise – Translate speech
Provision an Azure resource for speech translation	Module assessment
Translate speech to text	Summary
Synthesize translations
After completing this module, you’ll be able to: Provision Azure resources for speech translation Generate text translation from speech Synthesize spoken translations
Module 24: Develop an audio enabled generative AI application
A voice carries meaning beyond words, and audio-enabled generative AI models can interpret spoken input to understand tone, intent, and language. Learn how to build audio-enabled chat apps that listen and respond to audio.
Lessons:
Introduction	Exercise – Develop an audio-enabled chat app
Deploy a multimodal model	Module assessment
Develop an audio-based chat app	Summary
After completing this module, you’ll be able to: Deploy an audio-enabled generative AI model in Azure AI Foundry Create a chat app that submits audio-based prompts
Module 25: Analyze images
With the Azure AI Vision service, you can use pre-trained models to analyze images and extract insights and information from them.
Lessons:
Introduction	Module assessment
Provision an Azure Al Vision resource	Summary
Exercise – Analyze images with Azure Al Vision
Analyze an image
After completing this module, you’ll be able to: Provision an Azure AI Vision resource Use the Azure AI Vision SDK to connect to your resource Write code to analyze an image
Module 26: Read text in images
Azure’s AI Vision service uses algorithms to process images and return information. This module teaches you how to use the Image Analysis API for optical character recognition (OCR).
Lessons:
Introduction	Exercise – Read text in images
Explore Azure Al options for reading text	Module assessment
Use the Read API	Summary
After completing this module, you’ll be able to: Describe the OCR capabilities of Azure AI Vision’s Image Analysis API Use the Azure AI Vision service Image Analysis API to extract text from images
Module 27: Detect, analyze, and recognize faces
The ability for applications to detect human faces, analyze facial features and emotions, and identify individuals is a key artificial intelligence capability.
Lessons:
Introduction	Responsible Al considerations for face-based solutions
Plan a face detection, analysis, or recognition solution	Exercise – Detect and analyse faces
Detect and analyse faces	Module assessment
Verify and identify faces	Summary
After completing this module, you’ll be able to: Describe the capabilities of the Azure AI Vision Face service Write code to detect and analyse faces in an image Describe facial recognition support in Azure AI Vision Face Describe responsible AI considerations when developing facial solutions
Module 28: Classify images
Image classification is used to determine the main subject of an image. You can use the Azure AI Custom Vision services to train a model that classifies images based on your own categorizations.
Lessons:
Introduction	Exercise – Classify images
Azure Al Custom Vision	Module assessment
Train an image classification model	Summary
Create an image classification client application
After completing this module, you’ll be able to: Provision Azure resources for Azure AI Custom Vision Train an image classification model Use the Azure AI Custom Vision SDK to create an image classification client application
Module 29: Detect objects in images
Object detection is used to locate and identify objects in images. You can use Azure AI Custom Vision to train a model to detect specific classes of object in images.
Lessons:
Introduction	Exercise – Detect objects in images
Use Azure AI Custom Vision for object detection	Module assessment
Train an object detector	Summary
Develop an object detection client application
After completing this module, you’ll be able to: Provision Azure resources for Azure AI Custom Vision Understand object detection Train an object detector Consider options for labelling images
Module 30: Analyse video
Azure Video Indexer is a service to extract insights from video, including face identification, text recognition, object labels, scene segmentations, and more.
Lessons:
Introduction	Exercise – Analyse video
Understand Azure Video Indexer capabilities	Module assessment
Extract custom insights	Summary
Use Video Analyzer widgets and APIs
After completing this module, you’ll be able to: Describe Azure Video Indexer capabilities Extract custom insights Use Azure Video Indexer widgets and APIs
Module 31: Develop a vision-enabled generative AI application
A picture says a thousand words, and multimodal generative AI models can interpret images to respond to visual prompts. Learn how to build vision-enabled chat apps.
Lessons:
Introduction	Exercise – Develop a visionenabled chat app
Deploy a multimodal model	Module assessment
Develop a vision-based chat app	Summary
After completing this module, you’ll be able to: Deploy a vision-enabled generative AI model in Azure AI Foundry Test an image-based prompt in the chat playground Create a chat app that submits image-based prompts
Module 32: Generate images with AI
In Azure AI Foundry, you can use the OpenAI DALL-E model to generate original images based on natural language prompts.
Lessons:
Introduction	Create a client application that uses an image generation model
What is image-generation models?	Exercise – Generate images with Al
Explore image-generation models in Azure	Module assessment
Al Foundry portal	Summary
After completing this module, you’ll be able to: Describe the capabilities of DALL-E models Use the Images playground in Azure AI Foundry portal Integrate DALL-E model image generation into your apps
Module 33: Create a multimodal analysis solution with Azure AI Content Understanding
Use Azure AI Content Understanding for multimodal content analysis and information extraction.
Lessons:
Introduction	Exercise – Extract information from multimodal content
What is Azure Al Content Understanding?	Module assessment
Create a Content Understanding analyser	Summary
Use the Content Understanding REST API
After completing this module, you’ll be able to: Describe capabilities of Azure AI Content Understanding Use Azure AI Content Understanding to build a content analyser Consume a Content Understanding analyser by using the REST API
Module 34: Create an Azure AI Content Understanding client application
In Azure AI Foundry, you can use the OpenAI DALL-E model to generate original images based on natural language prompts.
Lessons:
Introduction	Exercise – Develop a Content Understanding client application
Prepare to use the Al Content Understanding REST API	Module assessment
Create a Content Understanding analyser	Summary
Analyze content
After completing this module, you’ll be able to: Use the Azure AI Content Understanding REST API to build a content analyser Use the Azure AI Content Understanding REST API to consume an analyser
Module 35: Use prebuilt Document intelligence models
Learn what data you can analyse by choosing prebuilt Forms Analyzer models and how to deploy these models in a Document intelligence solution.
Lessons:
Introduction	Exercise – Analyse a document using Azure Al Document Intelligence
Understand prebuilt models	Module assessment
Use the General Document, Read, and Layout models	Summary
Use financial, ID, and tax models
After completing this module, you’ll be able to: Identify business problems that you can solve by using prebuilt models in Forms Analyzer Analyse forms by using the General Document, Read, and Layout models Analyse forms by using financial, ID, and tax prebuilt models
Module 36: Extract data from forms with Azure Document intelligence
Document intelligence uses machine learning technology to identify and extract key-value pairs and table data from form documents with accuracy, at scale. This module teaches you how to use the Azure Document intelligence cognitive service.
Lessons:
Introduction	Use the Azure Document Intelligence Studio
What is Azure Document Intelligence?	Exercise – Extract data from custom forms
Get started with Azure Document Intelligence	Module assessment
Train custom models − Use Azure Document Intelligence models	Summary
After completing this module, you’ll be able to: Identify how Document intelligence’s layout service, prebuilt models, and custom models can automate processes Use Document intelligence’s capabilities with SDKs, REST API, and Document Intelligence Studio Develop and test custom models
Module 37: Create a knowledge mining solution with Azure AI Search
Unlock the hidden insights in your data with Azure AI Search. In this module, you’ll learn how to implement a knowledge mining solution that extracts and enriches data, making it searchable and ready for deeper analysis.
Lessons:
Introduction	Persist extracted information in a knowledge store
What is Azure Al Search?	Exercise – Create a knowledge mining solution
Extract data with an indexer	Module assessment
Enrich extracted data with Al skills	Summary
Search an index
After completing this module, you’ll be able to: Implement indexing with Azure AI Search Use AI skills to enrich data in an index Search an index to find relevant information Persist extracted information in a knowledge store

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Designing and Implementing a Microsoft Azure AI Solution

Type

Skill Level

Available dates

Learning Path

Duration

TYPE

Virtual

Classroom ILT

LEARNING PATH

SKILL LEVEL

DURATION

AVAILABLE DATES

Microsoft Overview

contact

Vendor
Authorised

Details

Menu

newsletter

Designing and Implementing a Microsoft Azure AI Solution

Type

Skill Level

Available dates

Learning Path

Duration

TYPE

Virtual

Classroom ILT

LEARNING PATH

SKILL LEVEL

DURATION

AVAILABLE DATES

Microsoft Overview

contact

Vendor Authorised

Details

Menu

newsletter

Vendor
Authorised