AI-102: Designing and Implementing a Microsoft Azure AI Solution

• Plan and manage an Azure AI solution (15–20%)

• Implement content moderation solutions (10–15%)

• Implement computer vision solutions (15–20%)

• Implement natural language processing solutions (30–35%)

• Implement knowledge mining and document intelligence solutions (10–15%)

• Implement generative AI solutions (10–15%)

• Select the appropriate service for a computer vision solution

• Select the appropriate service for a natural language processing solution

• Select the appropriate service for a speech solution

• Select the appropriate service for a generative AI solution

• Select the appropriate service for a document intelligence solution

• Select the appropriate service for a knowledge mining solution

• Plan for a solution that meets Responsible AI principles

• Create an Azure AI resource

• Determine a default endpoint for a service

• Integrate Azure AI services into a continuous integration and continuous delivery (CI/CD) pipeline

• Plan and implement a container deployment

• Configure diagnostic logging

• Monitor an Azure AI resource

• Manage costs for Azure AI services

• Manage account keys

• Protect account keys by using Azure Key Vault

• Manage authentication for an Azure AI Service resource

• Manage private communications

• Implement a text moderation solution with Azure AI Content Safety

• Implement an image moderation solution with Azure AI Content Safety

• Select visual features to meet image processing requirements

• Detect objects in images and generate image tags

• Include image analysis features in an image processing request

• Interpret image processing responses

• Extract text from images using Azure AI Vision

• Convert handwritten text using Azure AI Vision

• Choose between image classification and object detection models

• Label images

• Train a custom image model, including image classification and object detection

• Evaluate custom vision model metrics

• Publish a custom vision model

• Consume a custom vision model

• Use Azure AI Video Indexer to extract insights from a video or live stream

• Use Azure AI Vision Spatial Analysis to detect presence and movement of people in video

• Extract key phrases

• Extract entities

• Determine sentiment of text

• Detect the language used in text

• Detect personally identifiable information (PII) in text

• Implement text-to-speech

• Implement speech-to-text

• Improve text-to-speech by using Speech Synthesis Markup Language (SSML)

• Implement custom speech solutions

• Implement intent recognition

• Implement keyword recognition

• Translate text and documents by using the Azure AI Translator service

• Implement custom translation, including training, improving, and publishing a custom model

• Translate speech-to-speech by using the Azure AI Speech service

• Translate speech-to-text by using the Azure AI Speech service

• Translate to multiple languages simultaneously

• Create intents and add utterances

• Create entities

• Train, evaluate, deploy, and test a language understanding model

• Optimize a language understanding model

• Consume a language model from a client application

• Backup and recover language understanding models

• Create a custom question answering project

• Add question-and-answer pairs manually

• Import sources

• Train and test a knowledge base

• Publish a knowledge base

• Create a multi-turn conversation

• Add alternate phrasing

• Add chit-chat to a knowledge base

• Export a knowledge base

• Create a multi-language question answering solution

• Provision an Azure AI Search resource

• Create data sources

• Create an index

• Define a skillset

• Implement custom skills and include them in a skillset

• Create and run an indexer

• Query an index, including syntax, sorting, filtering, and wildcards

• Manage Knowledge Store projections, including file, object, and table projections

• Provision a Document Intelligence resource

• Use prebuilt models to extract data from documents

• Implement a custom document intelligence model

• Train, test, and publish a custom document intelligence model

• Create a composed document intelligence model

• Implement a document intelligence model as a custom Azure AI Search skill

• Provision an Azure OpenAI Service resource

• Select and deploy an Azure OpenAI model

• Submit prompts to generate natural language

• Submit prompts to generate code

• Use the DALL-E model to generate images

• Use Azure OpenAI APIs to submit prompts and receive responses

• Use large multimodal models in Azure OpenAI

• Configure parameters to control generative behavior

• Apply prompt engineering techniques to improve responses

• Use your own data with an Azure OpenAI model

• Fine-tune an Azure OpenAI model

8 Weeks

none

Intermediate

Microsoft Azure

AI Engineer