Course Description

Azure AI Engineer Associate

• Your responsibilities include participating in all phases of AI solutions development, including:
  
• Requirements definition and design
• Development
• Deployment
• Integration
• Maintenance
• Performance tuning
• Monitoring

• Build complete and secure end-to-end AI solutions.
• Integrate AI capabilities in other applications and solutions.

• Python
• C#

• Understand the components that make up the Azure AI portfolio and the available data storage options.
• Be able to apply responsible AI principles.

• Plan and manage an Azure AI solution
• Implement decision support solutions
• Implement computer vision solutions
• Implement natural language processing solutions
• Implement knowledge mining and document intelligence solutions
• Implement generative AI solutions

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

Select the appropriate Azure AI service

• 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, create and deploy an Azure AI service

• 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

Manage, monitor, and secure an Azure AI service

• 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 content moderation solutions (10–15%)

Create solutions for content delivery

• Implement a text moderation solution with Azure AI Content Safety
• Implement an image moderation solution with Azure AI Content Safety

Implement computer vision solutions (15–20%)

Analyze images

• 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

Implement custom computer vision models by 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

Analyze videos

• 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

Implement natural language processing solutions (30–35%)

Analyze text by using Azure AI Language

• Extract key phrases
• Extract entities
• Determine sentiment of text
• Detect the language used in text
• Detect personally identifiable information (PII) in text

Process speech by using Azure AI Speech

• 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 language

• 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

Implement and manage a language understanding model by using Azure AI Language

• 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 question answering solution by using Azure AI Language

• Create a 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

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

Implement an Azure AI Search 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

Implement an Azure AI Document Intelligence solution

• 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

Implement generative AI solutions (10–15%)

Use Azure OpenAI Service to generate content

• 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

Optimize generative AI

• 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