Designing and Implementing a Microsoft Azure AI Solution AI-102

Course Certification

This course helps you prepare to take the:
Exam AI-102 Designing and Implementing a Microsoft Azure AI Solution ;

Course Outline

• 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 decision support 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 univariate anomaly detection solution with Azure AI Anomaly Detector
• Implement a multivariate anomaly detection solution Azure AI Anomaly Detector
• Implement a data monitoring solution with Azure AI Metrics Advisor

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

• 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 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 a Cognitive 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 Cognitive 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

• 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

Course Mode

Instructor-Led Remote Live Classroom Training;

Trainers

Trainers are authorized Instructors in Microsoft and certified in other IT technologies, with years of hands-on experience in the industry and in Training.

Lab Topology

For all types of delivery, the participant can access the equipment and actual systems in our laboratories or directly in international data centers remotely, 24/7. Each participant has access to implement various configurations, Thus immediately applying the theory learned. Below are some scenarios drawn from laboratory activities.