Prepare for the Azure AI Fundamentals certification examination. This book covers the basics of implementing various Azure AI services in your business. The book not only helps you get ready for the AI-900 exam, but also helps you get started in the Artificial Intelligence (AI) world.
The book starts with a short overview of the AI-900 exam and takes you through the exam prerequisites and the structure of the exam. You will then learn basic and advanced AI in Azure. Principles of responsible AI, Azure Machine Learning (ML), Azure Cognitive Services, and Bot Services are covered, followed by a practice test. You will go through ML fundamental concepts, model training, and validation along with case studies and a practice test for better preparation. The book includes the fundamentals of Azure and computer vision cognitive services. Various vision services and face services are demonstrated as well as analyzing image and text using OCR. You will understand concepts of natural language processing (NLP) such as text analysis, language modelling, entity recognition, sentiment analysis, speech recognition, and synthesis and also learn how to leverage Microsoft Azure for NLP.
The goal of the group of technologies known as “Artificial Intelligence” (AI) is to make intelligent systems that can see, think, and help people. The goal of AI, which stands for “artificial intelligence,” is to give computers abilities that are similar to those of humans. This goal is to make computers that can do things as humans can. If AI is incorporated into the process, businesses will be able to create digital experiences that are intelligent, quick, and helpful to end users. This will give AI the ability to empower businesses.
Artificial Intelligence gives businesses the chance to start over, which has the potential to change not only business processes but also whole industries and the way customers interact with businesses. The Microsoft Azure AI development framework is a powerful tool that can be used for the creation of artificial intelligence solutions in a variety of different fields. Some of these fields are conversational AI, Machine Learning, Data Sciences, robotics, the Internet of Things (IoT), and many more.
After reading this book, you will be able to implement various Azure AI services and prepare for the Azure AI Fundamentals certification exam, AI-900.
What Will You Learn
Understand AI fundamentals and responsibilities
Know the Microsoft Azure offerings for AI
Understand foundational concepts for ML and Azure offerings for ML
Understand Azure Cognitive Services such as Custom Vision, Face, Form Recognizer, Text-to-Speech, and Image Analysis
Who This Book Is For
Azure and AI users working with ML services
Author(s): Krunal S. Trivedi
Series: Certification Study Companion Series
Publisher: Apress
Year: 2023
Language: English
Pages: 205
Table of Contents
About the Author
About the Technical Reviewer
Acknowledgments
Introduction
Chapter 1: Overview of AI-900 Exam Preparation
Exam Overview
Exam Prerequisites: Who Will Take This Examination?
Taking the Exam
First Thing First: Signing Up
Practice Test
Scheduling the Exam
Choosing Your Time Block
Exam Format
Modules and Weightage in the Exam
Module Description
Module 1: Describe Artificial Intelligence Workloads and Consideration (20–25%)
Lesson 1: Identify Features of Common AI Workloads
Lesson 2: Identify Guiding Principles of Responsible AI
Module 2: Describe Fundamental Principles of Machine Learning on Azure (25–30%)
Lesson 1: Identify Common Machine Learning Types
Lesson 2: Describe Core Machine Learning Concepts
Lesson 3: Describe Capabilities of Visual Tools in Azure Machine Learning Studio
Module 3: Describe Features of Computer Vision Workloads on Azure (15–20%)
Lesson 1: Identify Common Types of Computer Vision Solutions
Lesson 2: Identify Azure Tools and Services for Computer Vision Tasks
Module 4: Describe Features of Natural Language Processing (NLP) Workloads on Azure (25–30%)
Lesson 1: Identify Features of Common NLP Workload Scenarios
Lesson 2: Identify Azure Tools and Services for NLP Workloads
Lesson 3: Identify Considerations for Conversational AI Solutions on Azure
Summary
Chapter 2: Fundamentals of Artificial Intelligence
What Is Artificial Intelligence?
Strong AI
Weak AI
Examples of Weak AI
Understanding Artificial Intelligence Workloads
Machine Learning
Anomaly Detection
Computer Vision
Natural Language Processing
Knowledge Mining
Principles of Responsible AI
Fairness
Reliability and Safety
Transparency
Accountability
Understanding Artificial Intelligence in Microsoft Azure
Data Storage
Compute
Compute Instances
Compute Clusters
Inference Clusters
Attached Compute
Services
AI Services in Microsoft Azure
Azure Machine Learning
Azure Cognitive Services
Azure Bot Service
Azure Cognitive Search
Introspective Practice
Solutions to Practice Test
References: Microsoft Learn
Summary
Chapter 3: Machine Learning Fundamental Concepts
What Is Machine Learning?
Core Machine Learning Concepts
Dataset, Features, and Labels
Dataset
Training Set
Validation Set
Testing Set
Features and Labels
Machine Learning Algorithm
Machine Learning Workflow
Data Processing
Data Modeling
Deployment
Model Evaluation Metrics
Types of Machine Learning
Supervised Machine Learning
The Two Classes of Supervised Machine Learning
Regression
Classification
Unsupervised Machine Learning
Clustering
The Two Important Elements: Model Training and Validation
Introducing Azure Machine Learning
Tools for Azure Machine Learning
Azure Machine Learning Studio
Azure Machine Learning Designer
What Is Automated Machine Learning?
Practical Labs
Using Azure Machine Learning Designer to Build a Regression Model
Create Azure Machine Learning Workspace
Create Compute
Create Pipeline in Designer
Add and Explore a Dataset
Add Data Transformations
Cleaning
Training Our Model
Scoring Model
Evaluation
Submission
Scored Labels
Evaluation Result
Exploration
Delete Resources
Introspective Practice
Solutions to the Practice Test
References: Microsoft Learn
Summary
Chapter 4: Computer Vision
Getting Started with Azure Cognitive Services
Benefits of Cognitive Services
Azure Cognitive Services
Speech
Language
Vision
Decision
OpenAI Service
What Is Computer Vision
Computer Vision Core Elements: Image Classification and Object Detection
Image Classification
Object Detection
Computer Vision Application
Semantic Segmentation
Image Analysis
Optical Character Recognition (OCR)
Exploring Various Vision Services
Computer Vision
Detecting Object
Detect Texts
Categorizing an Image
Describe the Image
Detecting Faces
Detect the Color Scheme
Get the Area of Interest
Custom Vision
Image Classification Using the Azure Custom Vision Service
Object Detection Using Azure Custom Vision Service
Face
Identifying Faces in a Group
Identifying Similar Faces
Face Detection
Emotion Recognition
Face Grouping
Form Recognizer
Simple Text Extraction
Customized Results
Flexible Deployment
Built-In Security
Understanding of Optical Character Reader
Practical Labs
Computer Vision API – Text Extraction
Create Computer Vision Resource
Connect a Console App to Computer Vision Resource
Introspective Practice Test
Solutions for the Practice Test
References: Microsoft Learn
Summary
Untitled
Chapter 5: Fundamentals of Natural Language Processing
Getting Started with Natural Language Processing
What Is Natural Language Processing?
What Are the Business Applications of NLP?
How Does NLP Function?
Stages of Natural Language Processing (NLP)
Core NLP Responsibilities
Text Analysis and Entity Recognition
Text Analysis
Entity Recognition
Organize Tickets in Customer Support
Learn from Customer Feedback
Content Suggestion
Resumes of Processes
Sentiment Analysis
Indeterminate Sentiment
Speech Recognition and Synthesis
Speech Recognition
Speech Synthesis
Mobile Phones
Word Processing Software
Education
Customer Care
Applications in Healthcare
Reporting in Court
Recognizing Emotions
Hands-Free Communication Is Possible
Machine Translation
The Use of Rules in Machine Translation
Statistics in Machine Translation Technology
Semantic Language Modeling
AI for Conversational Interactions
Advantages of Conversational AI for Businesses
Improve Client Service
Drive Marketing and Sales Initiatives
Improve Agent Skills
Reduce Response Times
Personalize the Customer Experience
Microsoft Azure for NLP
Core Azure NLP Workloads: Language, Speech, and Translator
Language
Language Detection
Key Phrase Extraction
Entity Detection
Sentiment Analysis
Brand Monitoring with Sentiment Analysis
Market Research and Analysis Using Sentiment Analysis
Question Answering
Conversational Language Understanding
Utterances
Entities
Intents
Speech
Text-to-Speech
Speech-to-Text
Speech Translation
Translator
Literal and Semantic Translation
Text and Speech Translation
Microsoft Azure Platform for Conversational AI
Azure Bot Service
Develop a Knowledge Base
Custom Question Answering
Test the Knowledge Base
Extend and Customize the Bot
Join Channels
Practical Labs
Creating a Custom Question-Answering Knowledge Base
Editing Your Knowledge Base
Training and Testing the Knowledge Base
Creating an Informational Bot for the Knowledge Base
Introspective Test
Solutions to the Practice Test
References: Microsoft Learn
Summary
Index
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