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Designing and Implementing a Microsoft Azure AI Solution Training (AI-102)

Designing and Implementing a Microsoft Azure AI Solution Training (AI-102)

Course 8589

Duration: 4 days
Language: English
Level: Intermediate

AI-102 Designing and Implementing an Azure AI Solution is intended for software developers wanting to build AI-infused applications that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. The course will use C# or Python as the programming language.

Designing and Implementing a Microsoft Azure AI Solution Training (AI-102) Delivery Methods

In-Person
Online

Designing and Implementing a Microsoft Azure AI Solution Training (AI-102) Course Information

In this course, you will learn how to:

Describe considerations for AI-enabled application development.
Create, configure, deploy, and secure Azure Cognitive Services.
Develop applications that analyse 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 analyse images and videos.
Create custom computer vision models.
Develop applications that detect, analyse, and recognise faces.
Develop applications that read and process text in images and documents.
Create intelligent search solutions for knowledge mining.

Prerequisites

Knowledge of Microsoft Azure and ability to navigate the Azure portal
Knowledge of either C# or Python
Familiarity with JSON and REST programming semantics

Certification Information

This course can help you prepare for the following Microsoft role-based certification exam — Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution.

Designing and Implementing a Microsoft Azure AI Solution Training Outline

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 analyse and translate text.

Lessons

Analysing Text
Translating Text

Lab: Analyse Text

Lab: Translate Text

After completing this module, students will be able to:

Use the Text Analytics cognitive service to analyse 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 synthesising 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: Recognise and Synthesise Speech

Lab: Translate Speech

After completing this module, students will be able to:

Use the Speech cognitive service to recognise and synthesise 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 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

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 analyse images and video.

Lessons

Analysing Images
Analysing Videos

Lab: Analyse Images with Computer Vision

Lab: Analyse Video with Video Indexer

After completing this module, students will be able to:

Use the Computer Vision service to analyse images
Use Video Indexer to analyse 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, Analysing, and Recognising 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, Analyse, and Recognise Faces

After completing this module, students will be able to:

Detect faces with the Computer Vision service
Detect, analyse, and recognise 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 analyse those insights.

Lessons

Implementing an Intelligent Search Solution
Developing Custom Skills for an Enrichment Pipeline
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

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Microsoft Azure AI Course FAQs

Yes! This class prepares you for the Microsoft Certified: Azure AI Engineer Associate, Exam AI-102.

Yes! We recommend you take course Learning Tree course 1904, Python Fundamentals Training for Non-Programmers.

We recommend you consider Learning Tree course 8580, Microsoft Azure AI Fundamentals Training (AI-900), before taking AI-102.

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.

Designing and Implementing a Microsoft Azure AI Solution Training (AI-102)

Designing and Implementing a Microsoft Azure AI Solution Training (AI-102) Delivery Methods

Designing and Implementing a Microsoft Azure AI Solution Training (AI-102) Course Information

Designing and Implementing a Microsoft Azure AI Solution Training Outline

Module 1: Introduction to AI on Azure

Module 2: Developing AI Apps with Cognitive Services

Module 3: Getting Started with Natural Language Processing

Module 4: Building Speech-Enabled Applications

Module 5: Creating Language Understanding Solutions

Module 6: Building a QnA Solution

Module 7: Conversational AI and the Azure Bot Service

Module 8: Getting Started with Computer Vision

Module 9: Developing Custom Vision Solutions

Module 10: Detecting, Analysing, and Recognising Faces

Module 11: Reading Text in Images and Documents

Module 12: Creating a Knowledge Mining Solution

Microsoft Azure AI Course FAQs

Does this class prepare you for a Microsoft Certification?

What if I have no programming experience, is there a recommended pre-requisite class?

What if I am new to artificial intelligence, and want an overview of AI capabilities on Azure?

Who should attend this AI training?

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