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

Module 1: Introduction to AI on Azure

Artificial Intelligence (AI) is becoming a central component of modern applications and services. This module introduces common AI capabilities available for your apps and explains how they are implemented within Microsoft Azure. It also covers key considerations for designing and implementing AI solutions responsibly.

Lessons

• Introduction to Artificial Intelligence

• Artificial Intelligence in Azure

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

• Describe the considerations involved in creating AI-enabled applications.

• Identify Azure services suitable 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 your applications. In this module, you will learn how to provision, secure, monitor, and deploy these cognitive services.

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 completion of this module, participants will be able to:

• Provision and consume cognitive services in Azure.

• Manage the security of cognitive services.

• Monitor the performance of cognitive services.

• Utilize cognitive services containers.

Module 3: Getting Started with Natural Language Processing

Natural Language Processing (NLP) is a branch of artificial intelligence focused on extracting insights from written or spoken language. This module demonstrates how to use cognitive services to analyze and translate text.

Lessons

• Analyzing Text

• Translating Text

Lab : Translate Text

Lab : Analyze Text

Upon completion of this module, participants 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 modern applications and services accept spoken input and respond by synthesizing text. This module continues the exploration of natural language processing capabilities by teaching how to build speech-enabled applications.

Lessons

• Speech Recognition and Synthesis

• Speech Translation

Lab : Recognize and Synthesize Speech

Lab : Translate Speech

Upon completion of this module, participants 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 an application that intelligently understands and responds to natural language input, you must define and train a language understanding model. This module guides you through using the Language Understanding service to create an app that can identify user intent from natural language input.

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 completion of this module, participants will be able to:

• Create a Language Understanding app.

• Develop a client application for Language Understanding.

• Integrate Language Understanding with Speech services.

Module 6: Building a QnA Solution

A common interaction pattern between users and AI agents involves users submitting questions in natural language, with the AI responding intelligently with appropriate answers. 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 completion of this module, participants 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 basis of a growing category of AI applications where users engage in conversations with AI agents, often mirroring 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 completion of this module, participants 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 area of artificial intelligence where software applications interpret visual input from images or video. This module initiates your exploration of computer vision by teaching 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 completion of this module, participants 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, custom models trained with specific visual data are sometimes required. This module explores the Custom Vision service and how to use it to create custom image classification and object detection models.

Lessons

• Image Classification

• Object Detection

Lab : Classify Images with Custom Vision

Lab : Detect Objects in Images with Custom Vision

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

• Implement image classification using the Custom Vision service.

• Implement object detection using the Custom Vision service.

Module 10: Detecting, Analyzing, and Recognizing Faces

Facial detection, analysis, and recognition are standard computer vision scenarios. This module explores the use 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 completion of this module, participants will be able to:

• Detect faces using the Computer Vision service.

• Detect, analyze, and recognize faces using 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. This module covers cognitive services used to detect and read text within 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 completion of this module, participants 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 a critical 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 completion of this module, participants 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.