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Hybrid Immersion: AI-102 - Designing and Implementing an Azure AI Solution

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Hybrid Immersion: AI-102 - Designing and Implementing an Azure AI Solution Course Outline

Special Note to New Hampshire Residents
This course has not yet been approved by the New Hampshire Department of Education. Please contact us for an update on when the class will be available in New Hampshire.

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.

Why Choose ONLC's Hybrid Immersion Classes?

Accelerated Learning: Our unique blend of live instructor-led sessions and on-demand training allows you to master AI concepts quickly and efficiently.

Flexibility: Enjoy the benefits of both in-person instruction and self-paced learning, giving you the freedom to learn at your own pace.

Free Second-Shot Exam Voucher: If you don't pass your certification exam on the first attempt using the Microsoft-provided voucher, we'll give you a second chance at no additional cost.

How Our Hybrid Immersion Classes Work

Kick-Off Session: We start with an orientation to familiarize you with course content, exam preparation, and the learning management system.

Condensed Instructor-Led Training: Live sessions with an expert instructor are typically half the duration of regular classes, allowing you to grasp key concepts quickly.

On-Demand Learning: Access Microsoft Official content through our learning management system to reinforce your understanding and practice at your own pace.

Free Second-Shot Exam: If you don't pass the exam on your first attempt, simply share a screenshot of your exam score, and we'll provide you with a second-shot exam voucher.

Before attending this course, students must have:
Azure Fundamentals or equivalent
C# knowledge

Audience profile
This course is aimed at Cloud Solution Architects, Azure artificial intelligence designers, and AI developers.

Instructor-led Content Note
Because this Hybrid Immersion class combines instructor-led and on-demand training, every topic in this outline will not be covered by the instructor. Learners will need to review the remaining topics through ONLC's Learning Management System which includes links to the complete Microsoft Official Curriculum.

Who Should Attend?
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 Prerequisites
Before attending this course, students must have:
Knowledge of Microsoft Azure and ability to navigate the Azure portal
Knowledge of either C# or Python
Familiarity with JSON and REST programming semantics

Recommended course prerequisites
AI-900T00: Microsoft Azure AI Fundamentals course

Course Outline

1 - Prepare to develop AI solutions on Azure
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

2 - Create and consume Azure AI services
Provision an Azure AI services resource
Identify endpoints and keys
Use an SDK

3 - Secure Azure AI services
Consider authentication
Implement network security

4 - Monitor Azure AI services
Monitor cost
Create alerts
View metrics
Manage diagnostic logging

5 - Deploy Azure AI services in containers
Understand containers
Use Azure AI services containers

6 - Analyze images
Provision an Azure AI Vision resource
Analyze an image
Generate a smart-cropped thumbnail and remove background

7 - Classify images
Provision Azure resources for Azure AI Custom Vision
Understand image classification
Train an image classifier

8 - Detect, analyze, and recognize faces
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

9 - Read Text in images and documents with the Azure AI Vision Service
Explore Azure AI Vision options for reading text
Use the Read API

10 - Analyze video
Understand Azure Video Indexer capabilities
Extract custom insights
Use Video Analyzer widgets and APIs

11 - Analyze text with Azure AI Language
Provision an Azure AI Language resource
Detect language
Extract key phrases
Analyze sentiment
Extract entities
Extract linked entities

12 - Build a question answering solution
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

13 - 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

14 - Create a custom text classification solution
Understand types of classification projects
Understand how to build text classification projects

15 - Custom named entity recognition
Understand custom named entity recognition
Label your data
Train and evaluate your model

16 - Translate text with Azure AI Translator service
Provision an Azure AI Translator resource
Specify translation options
Define custom translations

17 - 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

18 - Translate speech with the Azure AI Speech service
Provision an Azure resource for speech translation
Translate speech to text
Synthesize translations

19 - 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

20 - Create a custom skill for Azure AI Search
Create a custom skill
Add a custom skill to a skillset

21 - Create a knowledge store with Azure AI Search
Define projections
Define a knowledge store

22 - Plan an Azure AI Document Intelligence solution
Understand AI Document Intelligence
Plan Azure AI Document Intelligence resources
Choose a model type

23 - Use prebuilt Form Recognizer models
Understand prebuilt models
Use the General Document, Read, and Layout models
Use financial, ID, and tax models

24 - 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

25 - Get started with Azure OpenAI Service
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

26 - Build natural language solutions with Azure OpenAI Service
Integrate Azure OpenAI into your app
Use Azure OpenAI REST API
Use Azure OpenAI SDK

27 - Apply prompt engineering with Azure OpenAI Service
Understand prompt engineering
Write more effective prompts
Provide context to improve accuracy

28 - Generate code with Azure OpenAI Service
Construct code from natural language
Complete code and assist the development process
Fix bugs and improve your code

29 - Generate images with Azure OpenAI Service
What is DALL-E?
Explore DALL-E in Azure OpenAI Studio
Use the Azure OpenAI REST API to consume DALL-E models

30 - 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

31 - Fundamentals of Responsible Generative AI
Plan a responsible generative AI solution
Identify potential harms
Measure potential harms
Mitigate potential harms
Operate a responsible generative AI solution
View outline in Word


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