Objectives

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

Audience

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.

Prerequisites

Before attending this course, students must have:

Knowledge of Microsoft Azure and ability to navigate the Azure portal

Knowledge of either C# or Python

Familiarity with JSON and REST programming semantics

Duration

Four days

Outline for Designing and Implementing a Microsoft Azure AI Solution Training

Module 1: Introduction to AI on Azure

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

 

Lessons

Introduction to Artificial Intelligence

 

Artificial Intelligence in Azure

 

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'll 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, students will be able to:

 

Provision and consume cognitive services in Azure

 

Manage cognitive services security

 

Monitor cognitive services

 

Use a cognitive services 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 : Translate Text

Lab : Analyze Text

After 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 modern apps and services accept spoken input and can respond by synthesizing text. In this module, you'll 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, 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 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'll learn how to use 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

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

One of the most common kinds of interaction between users and AI software agents is for users to submit questions in natural language, and for the AI agent to respond intelligently with an appropriate answer. In this module, you'll explore how the QnA Maker service enables the development of this kind of solution.

 

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'll 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 Composer

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

Computer vision is an area of artificial intelligence in which software applications interpret visual input from images or video. In this module, you'll start your exploration of computer vision by learning how to use cognitive services to analyze images and video.

 

Lessons

Analyzing Images

 

Analyzing Videos

 

Lab : Analyze Video

Lab : Analyze Images with Computer Vision

After 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 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'll 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 object detection

 

Module 10: Detecting, Analyzing, and Recognizing Faces

Facial detection, analysis, and recognition are common computer vision scenarios. In this module, you'll explore the user 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

After 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, 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

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

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

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