Azure Training | Designing And Implementing An Azure AI Solution in Chester

Designing and implementing a Microsoft Azure AI Solution AI-102 Course Outline

Module 1: Plan and Prepare to Develop AI Solutions on Azure

• Introduction
• What is AI
• Azure AI Services
• Azure AI Foundry
• Developer Tools and SDKs
• Responsible AI
• Explore the Azure AI Foundry Portal

Module 2: Choose and Deploy Models from the Model Catalogue in Azure AI Foundry Portal

• Introduction
• Explore the Model Catalogue
• Deploy a Model to an Endpoint
• Optimize Model Performance
• Exercise - Explore, Deploy, and Chat with Language Models

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

• Introduction
• What is the Azure AI Foundry SDK
• Work with Project Connections
• Create a Chat Client
• Exercise - Create a Generative AI Chat App

Module 4: Get Started with Prompt Flow to Develop Language Model Apps in the Azure AI Foundry

• Introduction
• 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 5: Develop a RAG-Based Solution with Your Own Data Using Azure AI Foundry

• Introduction
• 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 6: Fine-Tune a Language Model with Azure AI Foundry

• Introduction
• 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 Foundry Portal
• Exercise - Fine-Tune a Language Model

Module 7: Implement a Responsible Generative AI Solution in Azure AI Foundry

• Introduction
• 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 Output of Harmful Content

Module 8: Evaluate Generative AI Performance in Azure AI Foundry Portal

• Introduction
• Assess Model Performance
• Manually Evaluate Performance of a Model
• Automated Evaluations
• Exercise - Evaluate Generative AI Model Performance

Module 9: AI agent development on Azure

• Introduction
• What are AI agents?
• Options for agent development
• Azure AI Foundry Agent Service
• Exercise - Explore AI Agent development

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

• Introduction
• What is an AI Agent
• How to Use Azure AI Foundry Agent Service
• Develop Agents with the Azure AI Foundry Agent Service
• Exercise - Build an AI Agent

Module 11: Develop AI Agents with the Azure AI Foundry Extension in Visual Studio Code

• Introduction
• Get Started with the Azure AI Foundry Extension
• Develop AI Agents in Visual Studio Code
• Extend AI Agent Capabilities with Tools
• Exercise - Build an AI Agent Using the Azure AI Foundry Extension

Module 12: Integrate Custom Tools into Your Agent

• Introduction
• Why Use Custom Tools
• Options for Implementing Custom Tools
• How to Integrate Custom Tools
• Exercise - Build an Agent with Custom Tools

Module 13: Develop a Multi-Agent Solution with Azure AI Foundry Agent Service

• Introduction
• Understand Connected Agents
• Design a Multi-Agent Solution with Connected Agents
• Exercise - Develop a Multi-Agent App with Azure AI Foundry

Module 14: Integrate MCP Tools with Azure AI Agents

• Introduction
• Understand MCP Tool Discovery
• Integrate Agent Tools Using an MCP Server and Client
• Use Azure AI Agents with MCP Servers
• Exercise - Connect MCP Tools to Azure AI Agents

Module 15: Develop an AI Agent with Microsoft Agent Framework

• Introduction
• Understand Microsoft Agent Framework AI Agents
• Create an Azure AI Agent with Microsoft Agent Framework
• Add Tools to Azure AI Agent
• Exercise - Develop an Azure AI Agent with the Microsoft Agent Framework SDK

Module 16: Orchestrate a Multi-Agent Solution Using the Microsoft Agent Framework

• Introduction
• Understand the Microsoft Agent Framework
• Understand Agent Orchestration
• Use Concurrent Orchestration
• Use Sequential Orchestration
• Use Group Chat Orchestration
• Use Handoff Orchestration
• Use Magentic Orchestration
• Exercise - Develop a Multi-Agent Solution

Module 17: Discover Azure AI Agents with A2A

• Introduction
• Define an A2A Agent
• Implement an Agent Executor
• Host an A2A Server
• Connect to Your A2A Agent
• Exercise - Connect to Remote Azure AI Agents with the A2A Protocol

Module 18: Analyse Text with Azure AI Language

• Provision an Azure AI Language Resource
• Detect Language
• Extract Key Phrases
• Analyse Sentiment
• Extract Entities
• Extract Linked Entities
• Exercise - Analyse Text

Module 19: 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 20: 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 Prebuilt Entity Components
• Train, Test, Publish, and Review a Conversational Language Understanding Model
• Exercise - Build an Azure AI Services Conversational Language Understanding Model

Module 21: Create Custom Text Classification Solutions

• Introduction
• Understand Types of Classification Projects
• Understand How to Build Text Classification Projects
• Exercise - Classify Text

Module 22: Custom Named Entity Recognition

• Introduction
• Understand Custom Named Entity Recognition
• Label Your Data
• Train and Evaluate Your Model
• Exercise - Extract Custom Entities

Module 23: 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 24: 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 25: 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 26: Develop an Audio-Enabled Generative AI Application

• Introduction
• Deploy a Multimodal Model
• Develop an Audio-Based Chat App
• Exercise - Develop an Audio-Enabled Chat App

Module 27: Develop an Azure AI Voice Live Agent

• Introduction
• Explore the Azure Voice Live API
• Explore the AI Voice Live Client Library for Python
• Exercise - Develop an Azure AI Voice Live Agent

Module 28: Analyse Images

• Introduction
• Provision an Azure AI Vision Resource
• Analyse an Image
• Exercise - Analyse Images

Module 29: Read Text in Images

• Introduction
• Explore Azure AI Options for Reading Text
• Read Text with Azure AI Vision Image Analysis
• Exercise - Read Text in Images

Module 30: Detect, Analyse, and Recognize Faces

• Introduction
• Plan a Face Detection, Analysis, or Recognition Solution
• Detect and Analyse Faces
• Verify and Identify Faces
• Responsible AI Considerations for Face-Based Solutions
• Exercise - Detect and Analyse Faces

Module 31: Classify Images

• Introduction
• Azure AI Custom Vision
• Train an Image Classification Model
• Create an Image Classification Client Application
• Exercise - Classify Images

Module 32: Detect Objects in Images

• Introduction
• Use Azure AI Custom Vision for Object Detection
• Train an Object Detector
• Develop an Object Detection Client Application
• Exercise - Detect Objects in Images

Module 33: Analyse Video

• Introduction
• Understand Azure Video Indexer Capabilities
• Extract Custom Insights
• Use Video Analyzer Widgets and APIs
• Exercise - Analyse Video

Module 34: Develop a Vision-Enabled Generative AI Application

• Introduction
• Deploy a Multimodal Model
• Develop a Vision-Based Chat App
• Exercise - Develop a Vision-Enabled Chat App

Module 35: Generate Images with AI

• Introduction
• What are Image-Generation Models
• Explore Image-Generation Models in Azure AI Foundry Portal
• Create a Client Application That Uses an Image Generation Model
• Exercise - Generate Images with AI

Module 36: Create a Multimodal Analysis Solution with Azure AI Content Understanding

• Introduction
• What is Azure AI Content Understanding
• Create a Content Understanding Analyzer
• Use the Content Understanding REST API
• Exercise - Extract Information from Multimodal Content

Module 37: Create an Azure AI Content Understanding Client Application

• Introduction
• Prepare to Use the AI Content Understanding REST API
• Create a Content Understanding Analyzer
• Analyse Content
• Exercise - Develop a Content Understanding Client Application

Module 38: Use Prebuilt Document Intelligence Models

• Introduction
• Understand Prebuilt Models
• Use the General Document, Read, and Layout Models
• Use Financial, ID, and Tax Models
• Exercise - Analyse a Document Using Azure AI Document Intelligence

Module 39: 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 40: Create a Knowledge Mining Solution with Azure AI Search

• Introduction
• What is Azure AI Search
• Extract Data with an Indexer
• Enrich Extracted Data with AI Skills
• Search an Index
• Persist Extracted Information in a Knowledge Store
• Exercise - Create a Knowledge Mining Solution

Who should attend this Designing and Implementing a Microsoft Azure AI Solution AI-102 Course?

This Designing and Implementing a Microsoft Azure AI Solution (AI-102) Course aims to equip the delegates with necessary knowledge and skills to use Azure services to create AI solutions. This course can be beneficial for a wide range of professionals, including:

• AI Developers
• Data Scientists
• Cloud Solutions Architects
• Azure AI Engineers
• Application Developers
• Innovation Managers
• Technical Team Leads

Prerequisites of the Designing and implementing a Microsoft Azure AI Solution AI-102 Course

For attending the Designing and Implementing a Microsoft Azure AI Solution AI102 Course, delegates should have a basic knowledge of programming languages like Python, C#, or JavaScript, and prior knowledge of Azure AI Services such as Azure Machine Learning, Azure Cognitive Services, and Azure Bot Services. Being familiar with programming semantics like REST and JSON can also be beneficial for delegates.

Designing and Implementing a Microsoft Azure AI Solution AI-102 Course Overview

Designing and Implementing a Microsoft Azure AI Solution AI-102 is essential for creating intelligent applications using cloud-based AI services. With businesses increasingly relying on artificial intelligence to enhance decision-making, automate processes, and personalise customer experiences, expertise in Azure's AI tools becomes critical. This solution enables professionals to design AI-driven applications that meet the growing demands for innovation and efficiency.

This Designing And Implementing A Microsoft Azure AI Solution AI-102 Training Course is particularly suited for AI Engineers, Data Scientists, Cloud Architects, and Software Developers. It also benefits IT professionals involved in AI Model Deployment, Machine Learning, and the development of intelligent solutions within cloud environments. Those seeking to enhance their skills in building scalable, AI-driven applications will find this course highly relevant.

This 4-day Designing And Implementing A Microsoft Azure AI Solution AI-102 Training Course by The Knowledge Academy will provide professionals with practical knowledge of Microsoft Azure’s AI tools. Delegates will learn to implement AI solutions, integrate machine learning models, and deploy cognitive services, empowering them to build innovative applications that meet industry standards.

Course Objectives

• To understand Microsoft Azure AI services
• To implement machine learning models within AI solutions
• To deploy and manage cognitive services in applications
• To design intelligent bots and natural language processing solutions
• To optimise and scale AI applications using Azure tools
• To integrate AI solutions with other Azure services

Upon completion of the course, delegates will have the skills to design and deploy AI solutions effectively using Microsoft Azure, enabling them to drive innovation and efficiency in real-world applications across various industries.

What’s included in this Designing and implementing a Microsoft Azure AI Solution AI-102 Course?

• World-Class Training Sessions from Experienced Instructors
• Designing and Implementing a Microsoft Azure AI Solution AI-102 Certificate
• Digital Delegate Pack