AI-102: Designing and Implementing a Microsoft Azure AI Solution

Audience Profile

Software engineers concerned with building, managing and deploying AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. They are familiar with C# or Python and have knowledge on using REST-based APIs to build computer vision, language analysis, knowledge mining, intelligent search, and conversational AI solutions on Azure.

  Skill

• Describe considerations for AI-enabled application development
• Create, configure, deploy, and secure Azure Cognitive Services
• Develop applications that analyze text
• Develop speech-enabled applications.
• Create applications with natural language understanding capabilities
• Create QnA applications
• Create conversational solutions with bots.
• Use computer vision services to analyze images and videos
• Create custom computer vision models.
• Develop applications that detect, analyze, and recognize faces
• Develop applications that read and process text in images and documents.
• Create intelligent search solutions for knowledge mining.
• Create intelligent search, and conversational AI solutions on Azure.

Pre-requisites

Before attending this course, students must have:

• General knowledge of networking, computing, and cloud concepts

• Knowledge of Microsoft Azure and ability to navigate the Azure portal

• Knowledge of either C# or Python

• Familiarity with JSON and REST programming semantics

Module 1: Introduction to AI on Azure

Artificial Intelligence (AI) is increasingly at the core of modern apps and services. In this module, you will learn about some common AI capabilities that you can leverage in your apps, and how those capabilities are implemented in Microsoft Azure. You will also learn about some considerations for designing and implementing AI solutions responsibly.

Lessons

• Introduction to Artificial Intelligence
• Artificial Intelligence in Azure
• Select a cloud deployment       

After completing this module, students will be able to:

• Describe considerations for creating AI-enabled applications.
• Identify Azure services for AI application development.

Module 2: Developing AI Apps with Cognitive Services

Cognitive Services are the core building blocks for integrating AI capabilities into your apps. In this module, you will learn how to provision, secure, monitor, and deploy 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

After completing this module, you will be able to:

• Provision and consume cognitive services in Azure.
• Manage cognitive services security.
• Monitor cognitive services.
• Use a cognitive service container

Module 3: Getting Started with Natural Language Processing

Natural Language processing (NLP) is a branch of artificial intelligence that deals with extracting insights from written or spoken language. In this module, you'll learn how to use cognitive services to analyze and translate text.

Lessons

• Analyzing Text
• Translating Text

Lab: Analyze Text
Lab: Translate Text

After completing this module, you 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 apps and services accept spoken input and can respond by synthesizing text. In this module, you will continue your exploration of natural language processing capabilities by learning how to build speech-enabled applications.

Lessons

• Speech Recognition and Synthesis
• Speech Translation

Lab: Recognize and Synthesize Speech
Lab: Translate Speech

After completing this module, you will be able to:

• Use the Speech cognitive service to recognize and synthesize speech.
• Use the Speech cognitive service to translate speech.

Module 5: Omnichannel

Creating Language Understanding Solutions. To build an application that can intelligently understand and respond to natural language input, you must define and train a model for language understanding. In this module, you will learn how to use the Language Understanding service to create an app that can identify user intent from natural language input.

Lessons

• Publishing and Using a Language Understanding App
• Using Language Understanding with Speech
  
• Describe the service life cycle in Microsoft 365

Lab: Create a Language Understanding App
Lab: Create a Language Understanding Client Application
Lab: Use the Speech and Language Understanding Services

After 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

Module 6: Building a QnA Solution

Learn more about Microsoft 365 licensing, service, and support options.

Lessons

• Creating a QnA Knowledge Base
• Publishing and Using a QnA Knowledge Base

Lab: Create a QnA Solution

After completing this module, students will be able to:

• Use QnA Maker to create a knowledge base.
• Use a QnA knowledge base in an app or bot.

Module 7: Conversational AI and the Azure Bot Service

Bots are the basis for an increasingly common kind of AI application in which users engage in conversations with AI agents, often as they would with a human agent. In this module, you will explore 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 Cposerom

After 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

Getting Started with Computer Vision

Lessons

• Getting Started with Computer Vision
• Getting Started with Computer Vision

Lab: Analyze Images with Computer Vision

Lab: Analyze Video with Video Indexer

After completing this module, students will be able to:

• Use the Computer Vision service to analyze images.
• Use Video Indexer to analyze videos.

Module 9: Developing Custom Vision Solutions

While there are many scenarios where pre-defined general computer vision capabilities can be useful, sometimes you need to train a custom model with your own visual data. In this module, you will explore 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

After completing this module, students will be able to:

• Use the Custom Vision service to implement image classification.
• Use the Custom Vision service to implement image classification.

Module 10: Detecting, Analyzing, and Recognizing Faces

Facial detection, analysis, and recognition are common computer vision scenarios. In this module, you will explore the user of cognitive services to identify human faces.

Lessons

• Detecting Faces with the Computer Vision Service
• Detecting Faces with the Computer Vision Service

Lab: Detect, Analyze, and Recognize Faces

After completing this module, students will be able to

• After completing this module, students will be able to:
• 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, in which software extracts text from images or documents. In this module, you'll 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

After 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

In this module, you will learn how to use Dynamics 365 Connected Customer Service to take data and help execute actual business actions on the data. Ultimately, many AI scenarios involve intelligently searching for information based on user queries. AI-powered knowledge mining is an increasingly important way to build intelligent search solutions that use AI to extract insights from large repositories of digital data and enable users to find and analyze those insights.

Lessons

• Lesson 1: Getting started with Connected Customer Service
• Lesson 2: Registering devices
• Implementing an Intelligent Search Solution
• Implementing an Intelligent Search Solution
• Creating a Knowledge Store

Lab: Create an Azure Cognitive Search solution

Lab Create a Custom Skill for Azure Cognitive Search

Lab: Create a Knowledge Store with Azure Cognitive Search

After 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.