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

Module 1: Introduction to AI on Azure

Artificial Intelligence (AI) is becoming central to modern applications and services. In this module, you will explore common AI capabilities available for integration into your applications and understand how these are implemented within Microsoft Azure. Additionally, you will learn about the critical considerations for designing and implementing AI solutions responsibly.

Lessons

• Introduction to Artificial Intelligence

• Artificial Intelligence in Azure

Upon completing this module, students will be able to:

• Describe key considerations for developing AI-enabled applications

• Identify appropriate Azure services for AI application development

Module 2: Developing AI Apps with Cognitive Services

Cognitive Services serve as the fundamental building blocks for integrating AI capabilities into applications. In this module, you will learn the processes for provisioning, securing, monitoring, and deploying cognitive services effectively.

Lessons

• Getting Started with Cognitive Services

• Using Cognitive Services for Enterprise Applications

Lab : Get Started with Cognitive Services

Lab : Manage Cognitive Services Security

Lab : Monitor Cognitive Services

Lab : Use a Cognitive Services Container

Upon completing this module, students will be able to:

• Provision and consume cognitive services in Azure

• Manage security for cognitive services

• Monitor the performance of cognitive services

• Utilize a cognitive services container

Module 3: Getting Started with Natural Language Processing

Natural Language Processing (NLP) is a branch of AI focused on extracting insights from written or spoken language. In this module, you will learn how to utilize cognitive services to analyze and translate text content.

Lessons

• Analyzing Text

• Translating Text

Lab : Translate Text

Lab : Analyze Text

Upon completing this module, students will be able to:

• Use the Text Analytics cognitive service to analyze text

• Use the Translator cognitive service to translate text

Module 4: Building Speech-Enabled Applications

Many contemporary applications and services support spoken input and can respond by synthesizing text. This module continues the exploration of natural language processing capabilities by teaching you how to build applications that recognize and generate speech.

Lessons

• Speech Recognition and Synthesis

• Speech Translation

Lab : Recognize and Synthesize Speech

Lab : Translate Speech

Upon completing this module, students will be able to:

• Use the Speech cognitive service to recognize and synthesize speech

• Use the Speech cognitive service to translate speech

Module 5: Creating Language Understanding Solutions

To build applications that intelligently interpret and respond to natural language input, it is necessary to define and train a model for language understanding. In this module, you will learn how to use the Language Understanding service to create applications that identify user intent from natural language inputs.

Lessons

• Creating a Language Understanding App

• Publishing and Using a Language Understanding App

• Using Language Understanding with Speech

Lab : Create a Language Understanding Client Application

Lab : Create a Language Understanding App

Lab : Use the Speech and Language Understanding Services

Upon completing this module, students will be able to:

• Create a Language Understanding app

• Create a client application for Language Understanding

• Integrate Language Understanding and Speech capabilities

Module 6: Building a QnA Solution

A common interaction pattern between users and AI agents involves users submitting questions in natural language and receiving intelligent responses. This module explores how the QnA Maker service facilitates the development of such solutions.

Lessons

• Creating a QnA Knowledge Base

• Publishing and Using a QnA Knowledge Base

Lab : Create a QnA Solution

Upon completing this module, students will be able to:

• Use QnA Maker to create a knowledge base

• Integrate a QnA knowledge base into an app or bot

Module 7: Conversational AI and the Azure Bot Service

Bots form the foundation of a growing category of AI applications where users engage in conversations with AI agents, often mimicking interactions with human agents. This module examines the Microsoft Bot Framework and the Azure Bot Service, which together provide a platform for creating and delivering conversational experiences.

Lessons

• Bot Basics

• Implementing a Conversational Bot

Lab : Create a Bot with the Bot Framework SDK

Lab : Create a Bot with Bot Framework Composer

Upon completing this module, students will be able to:

• Use the Bot Framework SDK to create a bot

• Use the Bot Framework Composer to create a bot

Module 8: Getting Started with Computer Vision

Computer vision is an AI domain where software applications interpret visual data from images or video. In this module, you will begin exploring computer vision by learning how to use cognitive services to analyze images and video content.

Lessons

• Analyzing Images

• Analyzing Videos

Lab : Analyze Video

Lab : Analyze Images with Computer Vision

Upon completing this module, students will be able to:

• Use the Computer Vision service to analyze images

• Use Video Analyzer to analyze videos

Module 9: Developing Custom Vision Solutions

While pre-defined general computer vision capabilities are useful in many scenarios, there are times when you need to train a custom model using your own visual data. This module explores the Custom Vision service and how to use it to create custom models for image classification and object detection.

Lessons

• Image Classification

• Object Detection

Lab : Classify Images with Custom Vision

Lab : Detect Objects in Images with Custom Vision

Upon completing this module, students will be able to:

• Use the Custom Vision service to implement image classification

• Use the Custom Vision service to implement object detection

Module 10: Detecting, Analyzing, and Recognizing Faces

Facial detection, analysis, and recognition are frequent use cases in computer vision. In this module, you will explore the usage of cognitive services to identify human faces.

Lessons

• Detecting Faces with the Computer Vision Service

• Using the Face Service

Lab : Detect, Analyze, and Recognize Faces

Upon completing this module, students will be able to:

• Detect faces with the Computer Vision service

• Detect, analyze, and recognize faces with the Face service

Module 11: Reading Text in Images and Documents

Optical Character Recognition (OCR) is another common computer vision scenario where software extracts text from images or documents. In this module, you will explore cognitive services that can be used to detect and read text in images, documents, and forms.

Lessons

• Reading text with the Computer Vision Service

• Extracting Information from Forms with the Form Recognizer service

Lab : Read Text in Images

Lab : Extract Data from Forms

Upon completing this module, students will be able to:

• Use the Computer Vision service to read text in images and documents

• Use the Form Recognizer service to extract data from digital forms

Module 12: Creating a Knowledge Mining Solution

Ultimately, many AI scenarios involve intelligently searching for information based on user queries. AI-powered knowledge mining is an increasingly vital method for building intelligent search solutions that use AI to extract insights from large repositories of digital data, enabling users to find and analyze those insights effectively.

Lessons

• Implementing an Intelligent Search Solution

• Developing Custom Skills for an Enrichment Pipeline

• Creating a Knowledge Store

Lab : Create a Custom Skill for Azure Cognitive Search

Lab : Create an Azure Cognitive Search solution

Lab : Create a Knowledge Store with Azure Cognitive Search

Upon completing this module, students will be able to:

• Create an intelligent search solution with Azure Cognitive Search

• Implement a custom skill in an Azure Cognitive Search enrichment pipeline

• Use Azure Cognitive Search to create a knowledge store