AI-102T00: Designing and Implementing a Microsoft Azure AI Solution Training Course

Module 1: Overview of Artificial Intelligence on the Azure Platform

Artificial Intelligence (AI) has become a critical component in modern software development and service delivery. This module examines the essential AI capabilities available within the Microsoft Azure environment, detailing how these technologies are deployed to support organizational objectives. It also addresses the governance, ethical, and operational considerations necessary for the responsible implementation of AI systems.

Lesson Content

• Fundamentals of Artificial Intelligence

• Implementation of AI Capabilities in Azure

Upon completion of this module, participants will be equipped to:

• Outline the strategic and technical considerations for developing AI-enabled applications

• Identify specific Azure services suitable for AI application development

Module 2: Development of AI Applications Utilizing Cognitive Services

Cognitive Services function as the primary infrastructure for integrating artificial intelligence into enterprise applications. This module provides a comprehensive guide on the provisioning, security management, monitoring, and deployment of these cognitive services to ensure operational reliability and compliance.

Lesson Content

• Initial Setup and Configuration of Cognitive Services

• Application of Cognitive Services in Enterprise Contexts

Laboratory Exercise: Initialization of Cognitive Services

Laboratory Exercise: Security Administration of Cognitive Services

Laboratory Exercise: Performance Monitoring of Cognitive Services

Laboratory Exercise: Deployment of Cognitive Services Containers

Upon completion of this module, participants will be equipped to:

• Provision and utilize cognitive services within the Azure environment

• Administer security protocols for cognitive services

• Implement monitoring strategies for cognitive services

• Deploy and manage cognitive services containers

Module 3: Introduction to Natural Language Processing Techniques

Natural Language Processing (NLP) is a specialized domain of artificial intelligence focused on deriving actionable insights from written or spoken language. This module instructs participants on the application of cognitive services to perform advanced text analysis and multilingual translation tasks.

Lesson Content

• Techniques for Textual Analysis

• Procedures for Text Translation

Laboratory Exercise: Implementation of Text Translation

Laboratory Exercise: Execution of Text Analysis

Upon completion of this module, participants will be equipped to:

• Utilize the Text Analytics cognitive service for comprehensive text analysis

• Employ the Translator cognitive service to facilitate text translation

Module 4: Construction of Speech-Enabled Applications

Contemporary applications increasingly feature the ability to process spoken input and generate synthesized responses. This module extends the discussion on natural language processing by focusing on the architectural and technical requirements for building robust speech-enabled applications.

Lesson Content

• Principles of Speech Recognition and Synthesis

• Methodologies for Speech Translation

Laboratory Exercise: Implementation of Speech Recognition and Synthesis

Laboratory Exercise: Application of Speech Translation

Upon completion of this module, participants will be equipped to:

• Deploy the Speech cognitive service for speech recognition and synthesis operations

• Configure the Speech cognitive service to execute speech translation tasks

Module 5: Development of Language Understanding Solutions

Creating applications that intelligently interpret and respond to natural language requires the definition and training of specific language understanding models. This module details the use of the Language Understanding service to construct applications capable of accurately identifying user intent from natural language inputs.

Lesson Content

• Design and Construction of Language Understanding Applications

• Publication and Integration of Language Understanding Models

• Synergistic Use of Language Understanding with Speech Services

Laboratory Exercise: Development of a Language Understanding Client Application

Laboratory Exercise: Construction of a Language Understanding Model

Laboratory Exercise: Integration of Speech and Language Understanding Services

Upon completion of this module, participants will be equipped to:

• Build a functional Language Understanding application

• Develop a client application interface for Language Understanding

• Integrate Language Understanding capabilities with Speech services

Module 6: Construction of Question and Answer (QnA) Solutions

A prevalent interaction model in artificial intelligence involves users submitting natural language queries and receiving intelligent, contextually appropriate responses from AI agents. This module examines the QnA Maker service as a foundational tool for developing such question-answering solutions.

Lesson Content

• Establishment of QnA Knowledge Bases

• Deployment and Utilization of QnA Knowledge Bases

Laboratory Exercise: Development of a QnA Solution

Upon completion of this module, participants will be equipped to:

• Utilize QnA Maker to construct a structured knowledge base

• Integrate a QnA knowledge base into applications or chatbot platforms

Module 7: Conversational Artificial Intelligence and the Azure Bot Service

Chatbots represent a growing category of AI applications that facilitate human-like conversational interactions. This module explores the Microsoft Bot Framework and the Azure Bot Service, providing a comprehensive framework for designing, building, and deploying scalable conversational experiences.

Lesson Content

• Fundamental Concepts of Bot Architecture

• Implementation Strategies for Conversational Bots

Laboratory Exercise: Bot Development Using the Bot Framework SDK

Laboratory Exercise: Bot Development Using Bot Framework Composer

Upon completion of this module, participants will be equipped to:

• Develop bots utilizing the Bot Framework Software Development Kit

• Construct bots using the Bot Framework Composer environment

Module 8: Introduction to Computer Vision Technologies

Computer vision is a branch of artificial intelligence that enables software to interpret and analyze visual data from images and video feeds. This module initiates the study of computer vision by demonstrating how cognitive services can be applied to the analysis of visual media.

Lesson Content

• Strategies for Image Analysis

• Techniques for Video Analysis

Laboratory Exercise: Execution of Video Analysis

Laboratory Exercise: Image Analysis Using Computer Vision Services

Upon completion of this module, participants will be equipped to:

• Apply the Computer Vision service for the analysis of images

• Employ the Video Analyzer tool for the examination of video content

Module 9: Development of Custom Vision Solutions

While general-purpose computer vision capabilities address many common scenarios, specialized requirements often necessitate the training of custom models using proprietary visual data. This module focuses on the Custom Vision service, detailing the processes for creating tailored image classification and object detection models.

Lesson Content

• Implementation of Image Classification

• Execution of Object Detection

Laboratory Exercise: Image Classification Using Custom Vision

Laboratory Exercise: Object Detection in Images Using Custom Vision

Upon completion of this module, participants will be equipped to:

• Implement image classification workflows using the Custom Vision service

• Execute object detection tasks using the Custom Vision service

Module 10: Detection, Analysis, and Recognition of Facial Features

Facial detection, analysis, and recognition are standard applications within the computer vision domain. This module explores the use of cognitive services for the precise identification and characterization of human faces, emphasizing appropriate usage guidelines for government and enterprise contexts.

Lesson Content

• Facial Detection Capabilities within the Computer Vision Service

• Application of the Dedicated Face Service

Laboratory Exercise: Procedures for Detecting, Analyzing, and Recognizing Faces

Upon completion of this module, participants will be equipped to:

• Perform facial detection using the Computer Vision service

• Conduct facial detection, analysis, and recognition using the Face service

Module 11: Extraction of Text from Images and Documents

Optical Character Recognition (OCR) is a vital computer vision capability that allows software to extract textual data from images and documents. This module covers the cognitive services designed to detect and read text within various visual formats, including digital forms and static images.

Lesson Content

• Text Reading Capabilities of the Computer Vision Service

• Data Extraction from Forms Using the Form Recognizer Service

Laboratory Exercise: Reading Text Within Images

Laboratory Exercise: Extraction of Data from Digital Forms

Upon completion of this module, participants will be equipped to:

• Use the Computer Vision service to extract text from images and documents

• Employ the Form Recognizer service to retrieve structured data from digital forms

Module 12: Implementation of Knowledge Mining Solutions

Many advanced AI scenarios require the intelligent retrieval of information based on complex user queries. AI-powered knowledge mining is a critical method for constructing search solutions that derive insights from large-scale digital repositories, enabling users to efficiently locate and analyze relevant data.

Lesson Content

• Design and Implementation of Intelligent Search Solutions

• Development of Custom Skills for Data Enrichment Pipelines

• Construction and Management of Knowledge Stores

Laboratory Exercise: Creation of Custom Skills for Azure Cognitive Search

Laboratory Exercise: Implementation of an Azure Cognitive Search Solution

Laboratory Exercise: Establishment of a Knowledge Store with Azure Cognitive Search

Upon completion of this module, participants will be equipped to:

• Develop an intelligent search solution using Azure Cognitive Search

• Implement custom skills within the Azure Cognitive Search enrichment pipeline

• Utilize Azure Cognitive Search to establish and manage a knowledge store