AI-102T00 Designing and Implementing an Azure AI Solution

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 AI Search

2 – Create and consume Azure AI services

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

7 – Analyze images

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

8 – Image classification with custom Azure AI Vision models

• Understand custom model types
• Create a custom project
• Label and train a custom model

9 – Classify images

• Provision Azure resources for Azure AI Custom Vision
• Understand image classification
• Train an image classifier

10 – Detect objects in images

• Understand object detection
• Train an object detector
• Consider options for labeling images

11 – 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

12 – Read Text in images and documents with the Azure AI Vision Service

• Explore Azure AI Vision options for reading text
• Use the Read API

13 – Analyze video

• Understand Azure Video Indexer capabilities
• Extract custom insights
• Use Video Analyzer widgets and APIs

14 – Analyze text with Azure AI Language

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

15 – 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

16 – 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

17 – Create a custom text classification solution

• Understand types of classification projects
• Understand how to build text classification projects

18 – Custom named entity recognition

• Understand custom named entity recognition
• Label your data
• Train and evaluate your model

19 – Translate text with Azure AI Translator service

• Provision an Azure AI Translator resource
• Specify translation options
• Define custom translations

20 – 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

21 – Translate speech with the Azure AI Speech service

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

22 – 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

23 – 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

24 – Create a knowledge store with Azure AI Search

• Define projections
• Define a knowledge store

25 – 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

26 – 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

27 – 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

28 – Perform search reranking with semantic ranking in Azure AI Search

• What is semantic ranking?
• Set up semantic ranking

29 – Perform vector search and retrieval in Azure AI Search

• What is vector search?
• Prepare your search
• Understand embedding

30 – Plan an Azure AI Document Intelligence solution

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

31 – Use prebuilt Document intelligence models

• Understand prebuilt models
• Use the General Document, Read, and Layout models
• Use financial, ID, and tax models

32 – 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

33 – Create a composed Document intelligence model

• Understand composed models
• Assemble composed models

34 – Get started with Azure OpenAI Service

• Access Azure OpenAI Service
• Use Azure AI Studio
• Explore types of generative AI models
• Deploy generative AI models
• Use prompts to get completions from models
• Test models in Azure AI Studio’s playground

35 – Build natural language solutions with Azure OpenAI Service

• Integrate Azure OpenAI into your app
• Use Azure OpenAI REST API
• Use Azure OpenAI SDK

36 – Apply prompt engineering with Azure OpenAI Service

• Understand prompt engineering
• Write more effective prompts
• Provide context to improve accuracy

37 – Generate code with Azure OpenAI Service

• Construct code from natural language
• Complete code and assist the development process
• Fix bugs and improve your code

38 – Generate images with Azure OpenAI Service

• What is DALL-E?
• Explore DALL-E in Azure AI Studio
• Use the Azure OpenAI REST API to consume DALL-E models

39 – 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