Designing and Implementing a Microsoft Azure AI Solution (AI-102T00)

Module 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 Azure OpenAI Service
• Understand capabilities of Azure AI Search

Module 2 : Create and consume Azure AI services

• Create Azure AI services resources in an Azure subscription.
• Identify endpoints, keys, and locations required to consume an Azure AI services resource.
• Use a REST API and an SDK to consume Azure AI services.

Module 3 : Secure Azure AI services

• Consider authentication for Azure AI services
• Manage network security for Azure AI services

Module 4 : Monitor Azure AI services

• Monitor Azure AI services costs.
• Create alerts and view metrics for Azure AI services.
• Manage Azure AI services diagnostic logging.

Module 5 : Deploy Azure AI services in containers

• Create containers for reuse
• Deploy to a container and secure a container
• Consume Azure AI services from a container

Module 6 : Analyze images

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

Module 7 : Classify images

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

Module 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

Module 9 : Read Text in images and documents with the Azure AI Vision Service

• Read text from images using OCR
• Use the Azure AI Vision service Image Analysis with SDKs and the REST API
• Develop an application that can read printed and handwritten text

Module 10 : Analyze video

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

Module 11 : Analyze text with Azure AI Language

• Detect language from text
• Analyze text sentiment
• Extract key phrases, entities, and linked entities

Module 12 : Create question answering solutions with Azure AI Language

• 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 13 : Build a conversational language understanding model

• 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 14 : Create a custom text classification solution

• 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 15 : Custom named entity recognition

• Understand tagging entities in extraction projects
• Understand how to build entity recognition projects

Module 16 : Translate text with Azure AI Translator service

• Provision a Translator resource
• Understand language detection, translation, and transliteration
• Specify translation options
• Define custom translations

Module 17 : Create speech-enabled apps with Azure AI services

• Provision an Azure resource for the Azure AI Speech service
• Use the Azure AI Speech to text API to implement speech recognition
• Use the Text to speech API to implement speech synthesis
• Configure audio format and voices
• Use Speech Synthesis Markup Language (SSML)

Module 18 : Translate speech with the Azure AI Speech service

• Provision Azure resources for speech translation.
• Generate text translation from speech.
• Synthesize spoken translations.

Module 19 : Create an Azure AI Search solution

• Create an Azure AI Search solution
• Develop a search application

Module 20 : Create a custom skill for Azure AI Search

• Implement a custom skill for Azure AI Search
• Integrate a custom skill into an Azure AI Search skillset

Module 21 : Create a knowledge store with Azure AI Search

• Create a knowledge store from an Azure AI Search pipeline
• View data in projections in a knowledge store

Module 22 : Plan an Azure AI Document Intelligence solution

• Describe the components of an Azure AI Document Intelligence solution.
• Create and connect to Azure AI Document Intelligence resources in Azure.
• Choose whether to use a prebuilt, custom, or composed model.

Module 23 : Use prebuilt Form Recognizer models

• Identify business problems that you can solve by using prebuilt models in Forms Analyzer.
• Analyze forms by using the General Document, Read, and Layout models.
• Analyze forms by using financial, ID, and tax prebuilt models

Module 24 : Extract data from forms with Azure Document Intelligence

• Identify how Azure Document Intelligence's layout service, prebuilt models, and custom service can automate processes
• Use Azure Document Intelligence's Optical Character Recognition (OCR) capabilities with SDKs, REST API, and Azure Document Intelligence Studio
• Develop and test custom models

Module 25 : Get started with Azure OpenAI Service

• Create an Azure OpenAI Service resource and understand types of Azure OpenAI base models.
• Use the Azure OpenAI Studio, console, or REST API to deploy a base model and test it in the Studio's playgrounds.
• Generate completions to prompts and begin to manage model parameters.

Module 26 : Build natural language solutions with Azure OpenAI Service

• Integrate Azure OpenAI into your application
• Differentiate between different endpoints available to your application
• Generate completions to prompts using the REST API and language specific SDKs

Module 27 : Apply prompt engineering with Azure OpenAI Service

• Understand the concept of prompt engineering and its role in optimizing Azure OpenAI models' performance.
• Know how to design and optimize prompts to better utilize AI models.
• Include clear instructions, request output composition, and use contextual content to improve the quality of the model's responses.

Module 28 : Generate code with Azure OpenAI Service

• Use natural language prompts to write code
• Build unit tests and understand complex code with AI models
• Generate comments and documentation for existing code

Module 29 : Generate images with Azure OpenAI Service

• Describe the capabilities of DALL-E in the Azure openAI service
• Use the DALL-E playground in Azure OpenAI Studio
• Use the Azure OpenAI REST interface to integrate DALL-E image generation into your apps

Module 30 : Implement Retrieval Augmented Generation (RAG) with Azure OpenAI Service

• Describe the capabilities of Azure OpenAI on your data
• Configure Azure OpenAI to use your own data
• Use Azure OpenAI API to generate responses based on your own data

Module 31 : Fundamentals of Responsible Generative AI

• 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