Оглавление
Vinnie Bansal. Machine Learning with Dynamics 365 and Power Platform
Table of Contents
List of Tables
List of Illustrations
Guide
Pages
Machine Learning with Dynamics 365 and Power Platform. The Ultimate Guide to Apply Predictive Analytics
Foreword
Preface
WHY DID WE WRITE THIS BOOK?
WHAT WAS THE INSPIRATION BEHIND THIS SUBJECT?
Acknowledgments
About the Authors
CHAPTER 1 Dynamics 365, Power Platform, and Machine Learning. INTRODUCTION TO DYNAMICS 365
Microsoft Business Applications
Customer Relationship Management
Enterprise Resource Planning
Dynamics 365 for Small and Medium‐Sized Businesses
Common Data Model
INTRODUCTION TO POWER PLATFORM
What Is Power Platform?
Power Apps
Power Automate
Power BI
Power Virtual Agents
Cross‐Cutting Features
Dataverse
Connectors
AI Builder
Dynamics 365 Customer Voice
WHAT IS MACHINE LEARNING: HOW HAS IT EVOLVED?
DEFINITION OF MACHINE LEARNING
Evolution of Machine Learning
Lifecycle of Machine Learning
Data Preparation
Select Algorithm and Model (Modeling)
Validation
Deployment
Advantages of Machine Learning
Drawbacks of Machine Learning
Machine Learning + Dynamics 365 / Power Platform: Insights Everywhere
CHAPTER 2 Artificial Intelligence and Pre‐Built Machine Learning in Dynamics 365. AZURE AI PLATFORM
Machine Learning
Azure Machine Learning
Machine Learning Studio
AZURE MACHINE LEARNING SERVICE
AI‐powered Apps and Agents
Azure Cognitive Services
Azure Bot Service
KNOWLEDGE MINING
CHAPTER 3 ML/AI Features and Their Applications in Dynamics 365
CUSTOMER INSIGHTS
CUSTOMER SERVICE INSIGHTS
SALES INSIGHTS
Free Sales Insights Features
Assistant
Auto Capture
Email Engagement
Advanced Sales Insights Features
Conversation Intelligence
PRODUCT INSIGHTS
VIRTUAL AGENT FOR CUSTOMER SERVICE IN DYNAMICS 365
Fraud Protection
ARTIFICIAL INTELLIGENCE IN POWER APPS WITH AI BUILDER
WHAT IS MIXED REALITY?
Dynamics 365 Apps for Mixed Reality
Microsoft Hololens
Dynamics 365 Mixed Reality Apps
Dynamics 365 Remote Assist
Dynamics 365 Guides
CHAPTER 4 Dynamics 365 and Custom ML Models Using Azure ML
AZURE MACHINE LEARNING
Azure Machine Learning Concepts
AZURE MACHINE LEARNING STUDIO
Authoring Experience in Azure ML Studio
When to Use Automated ML
How Does the Automated ML Machine Learning Process Work?
Classification
Regression
Time‐series Forecasting
Which Features Should We Select to Train the Models?
How to Select Algorithms
Which Parameters Should We Select to Tune?
Ensemble Models
Designer Authoring Experience in Azure ML Studio
Azure Machine Learning Pipelines
Registered Model
Published Pipelines
Real‐Time Endpoints
Notebooks
AZURE MACHINE LEARNING SERVICE
CHAPTER 5 Deep Dive in Machine Learning Custom Models
AZURE CLI EXTENSION
VISUAL STUDIO CODE
Customer Churn Prediction
Customer Lifetime Value
Product Recommendation System
Chapter 6 Machine Learning with Dynamics 365 Use Cases
ML FOR FINANCE
First Use Case: Process Automation
Second Use Case: Security
Third Use Case: Algorithmic Trading
Fourth Use Case: Customer Data Management and Credit Scoring
Accounts Payable
Automation of Vendor Payment Proposals
Accounts Receivable
Collection Process Setup Page
Credit and Collection Module
Travel Expense
Fraud Detection
Purchase Protection
Diagnose Experience Tool
Risk Diagnostic Report
Use Evaluate Experience for Fraud Protection
Use Protect Experience for Fraud Protection
Acceptance Booster Service
Account Protection
Loss Prevention
DEMAND FORECASTING
CONNECTED STORE
ML FOR HUMAN RESOURCES MANAGEMENT
MACHINE LEARNING AT THE WORKPLACE
MyAnalytics Dashboard
Afterword
Index
WILEY END USER LICENSE AGREEMENT
