Intro -- Preface -- Digital Playbook and the AIoT User Group -- How to Get Involved -- Vision -- About This Book -- Structure of the Digital Playbook -- Key Plays of the Digital Playbook -- How to Read This Book -- Contents -- Part I: Introduction -- Chapter 1: AIoT 101: What, Why, How, Who -- 1.1 What: Smart, Connected Products and Solutions with AIoT -- 1.2 Why: Purpose and AIoT-Enabled Business Outcomes -- 1.3 How: Getting Things (and AI) Done -- 1.4 Who: AIoT Roles and Responsibilities -- Chapter 2: Artificial Intelligence 101 -- 2.1 Introduction -- 2.2 Supervised Learning -- 2.3 Unsupervised Learning -- 2.4 Reinforcement Learning -- 2.5 Deep Learning and Artificial Neural Networks -- 2.6 Summary: AI & -- Data Analytics -- Chapter 3: Data 101 -- 3.1 Enterprise Data -- 3.2 Data Management -- 3.3 Analytics Platforms -- 3.4 Data Engineering -- 3.4.1 Data Pipeline -- 3.4.2 Edge Vs. Cloud -- 3.4.3 The Big Loop -- 3.5 Data Science -- 3.5.1 Understanding AIoT Data Categories and Matching AI Methods -- 3.5.2 Data Sets -- 3.5.3 Data Labeling -- 3.6 Domain Knowledge -- 3.7 Chicken Vs. Egg -- Chapter 4: Digital Twin 101 -- 4.1 Introduction -- 4.2 Example -- 4.3 Digital Twin and AIoT -- 4.3.1 Example 1: Electric Vehicle -- 4.3.2 Example 2: Particle Collider -- 4.4 DT Resolution and Update Frequency -- 4.5 Advanced Digital Twins: Physics Simulation and Virtual Sensors -- Chapter 5: Internet of Things 101 -- 5.1 Introduction -- 5.2 IoT Architecture -- 5.3 IoT Sensors and Actuators -- 5.4 IoT Protocol Layers -- 5.5 IoT Connectivity -- 5.6 Over-the-Air Updates -- 5.6.1 Distribution -- 5.6.2 Deployment -- 5.7 AIoT AppStores -- 5.7.1 Example 1: OEM with Closed AppStore -- 5.7.2 Example 2: OEM with Open AppStore -- 5.8 Expert Opinion: Nik Willetts, President & -- CEO of TM Forum -- Chapter 6: Hardware 101 -- 6.1 Smart, Connected Products.