AI at the Edge: Solving Real-World Problems with Embedded Machine Learning

Copyright
Table of Contents
Foreword
Preface
	About This Book
	What to Expect
	What You Need to Know Already
	Responsible, Ethical, and Effective AI
	Further Resources
	Conventions Used in This Book
	Using Code Examples
	O’Reilly Online Learning
	How to Contact Us
	Acknowledgments
Chapter 1. A Brief Introduction to Edge AI
	Defining Key Terms
		Embedded
		The Edge (and the Internet of Things)
		Artificial Intelligence
		Machine Learning
		Edge AI
		Embedded Machine Learning and Tiny Machine Learning
		Digital Signal Processing
	Why Do We Need Edge AI?
		To Understand the Benefits of Edge AI, Just BLERP
		Edge AI for Good
		Key Differences Between Edge AI and Regular AI
	Summary
Chapter 2. Edge AI in the Real World
	Common Use Cases for Edge AI
		Greenfield and Brownfield Projects
		Real-World Products
	Types of Applications
		Keeping Track of Objects
		Understanding and Controlling Systems
		Understanding People and Living Things
		Transforming Signals
	Building Applications Responsibly
		Responsible Design and AI Ethics
		Black Boxes and Bias
		Technology That Harms, Not Helps
	Summary
Chapter 3. The Hardware of Edge AI
	Sensors, Signals, and Sources of Data
		Types of Sensors and Signals
		Acoustic and Vibration
		Visual and Scene
		Motion and Position
		Force and Tactile
		Optical, Electromagnetic, and Radiation
		Environmental, Biological, and Chemical
		Other Signals
	Processors for Edge AI
		Edge AI Hardware Architecture
		Microcontrollers and Digital Signal Processors
		System-on-Chip
		Deep Learning Accelerators
		FPGAs and ASICs
		Edge Servers
		Multi-Device Architectures
		Devices and Workloads
	Summary
Chapter 4. Algorithms for Edge AI
	Feature Engineering
		Working with Data Streams
		Digital Signal Processing Algorithms
		Combining Features and Sensors
	Artificial Intelligence Algorithms
		Algorithm Types by Functionality
		Algorithm Types by Implementation
		Optimization for Edge Devices
		On-Device Training
	Summary
Chapter 5. Tools and Expertise
	Building a Team for AI at the Edge
		Domain Expertise
		Diversity
		Stakeholders
		Roles and Responsibilities
		Hiring for Edge AI
		Learning Edge AI Skills
	Tools of the Trade
		Software Engineering
		Working with Data
		Algorithm Development
		Running Algorithms On-Device
		Embedded Software Engineering and Electronics
		End-to-End Platforms for Edge AI
	Summary
Chapter 6. Understanding and Framing Problems
	The Edge AI Workflow
		Responsible AI in the Edge AI Workflow
	Do I Need Edge AI?
		Describing a Problem
		Do I Need to Deploy to the Edge?
		Do I Need Machine Learning?
		Practical Exercise
	Determining Feasibility
		Moral Feasibility
		Business Feasibility
		Dataset Feasibility
		Technological Feasibility
		Making a Final Decision
		Planning an Edge AI Project
	Summary
Chapter 7. How to Build a Dataset
	What Does a Dataset Look Like?
	The Ideal Dataset
	Datasets and Domain Expertise
	Data, Ethics, and Responsible AI
		Minimizing Unknowns
		Ensuring Domain Expertise
	Data-Centric Machine Learning
	Estimating Data Requirements
		A Practical Workflow for Estimating Data Requirements
	Getting Your Hands on Data
		The Unique Challenges of Capturing Data at the Edge
	Storing and Retrieving Data
		Getting Data into Stores
		Collecting Metadata
	Ensuring Data Quality
		Ensuring Representative Datasets
		Reviewing Data by Sampling
		Label Noise
		Common Data Errors
		Drift and Shift
		The Uneven Distribution of Errors
	Preparing Data
		Labeling
		Formatting
		Data Cleaning
		Feature Engineering
		Splitting Your Data
		Data Augmentation
		Data Pipelines
	Building a Dataset over Time
	Summary
Chapter 8. Designing Edge AI Applications
	Product and Experience Design
		Design Principles
		Scoping a Solution
		Setting Design Goals
	Architectural Design
		Hardware, Software, and Services
		Basic Application Architectures
		Complex Application Architectures and Design Patterns
		Working with Design Patterns
	Accounting for Choices in Design
		Design Deliverables
	Summary
Chapter 9. Developing Edge AI Applications
	An Iterative Workflow for Edge AI Development
		Exploration
		Goal Setting
		Bootstrapping
		Test and Iterate
		Deployment
		Support
	Summary
Chapter 10. Evaluating, Deploying, and Supporting Edge AI Applications
	Evaluating Edge AI Systems
		Ways to Evaluate a System
		Useful Metrics
		Techniques for Evaluation
		Evaluation and Responsible AI
	Deploying Edge AI Applications
		Predeployment Tasks
		Mid-Deployment Tasks
		Postdeployment Tasks
	Supporting Edge AI Applications
		Postdeployment Monitoring
		Improving a Live Application
		Ethics and Long-Term Support
	What Comes Next
Chapter 11. Use Case: Wildlife Monitoring
	Problem Exploration
	Solution Exploration
	Goal Setting
	Solution Design
		What Solutions Already Exist?
		Solution Design Approaches
		Design Considerations
		Environmental Impact
		Bootstrapping
		Define Your Machine Learning Classes
	Dataset Gathering
		Edge Impulse
		Choose Your Hardware and Sensors
		Data Collection
		iNaturalist
		Dataset Limitations
		Dataset Licensing and Legal Obligations
		Cleaning Your Dataset
		Uploading Data to Edge Impulse
	DSP and Machine Learning Workflow
		Digital Signal Processing Block
		Machine Learning Block
	Testing the Model
		Live Classification
		Model Testing
		Test Your Model Locally
	Deployment
		Create Library
		Mobile Phone and Computer
		Prebuilt Binary Flashing
		Impulse Runner
		GitHub Source Code
	Iterate and Feedback Loops
	AI for Good
	Related Works
		Datasets
		Research
Chapter 12. Use Case: Food Quality Assurance
	Problem Exploration
	Solution Exploration
	Goal Setting
	Solution Design
		What Solutions Already Exist?
		Solution Design Approaches
		Design Considerations
		Environmental and Social Impact
		Bootstrapping
		Define Your Machine Learning Classes
	Dataset Gathering
		Edge Impulse
		Choose Your Hardware and Sensors
		Data Collection
		Data Ingestion Firmware
		Uploading Data to Edge Impulse
		Cleaning Your Dataset
		Dataset Licensing and Legal Obligations
	DSP and Machine Learning Workflow
		Digital Signal Processing Block
		Machine Learning Block
	Testing the Model
		Live Classification
		Model Testing
	Deployment
		Prebuilt Binary Flashing
		GitHub Source Code
	Iterate and Feedback Loops
	Related Works
		Research
		News and Other Articles
Chapter 13. Use Case: Consumer Products
	Problem Exploration
	Goal Setting
	Solution Design
		What Solutions Already Exist?
		Solution Design Approaches
		Design Considerations
		Environmental and Social Impact
		Bootstrapping
		Define Your Machine Learning Classes
	Dataset Gathering
		Edge Impulse
		Choose Your Hardware and Sensors
		Data Collection
		Data Ingestion Firmware
		Cleaning Your Dataset
		Dataset Licensing and Legal Obligations
	DSP and Machine Learning Workflow
		Digital Signal Processing Block
		Machine Learning Blocks
	Testing the Model
		Live Classification
		Model Testing
	Deployment
		Prebuilt Binary Flashing
		GitHub Source Code
	Iterate and Feedback Loops
	Related Works
		Research
		News and Other Articles
Index
About the Authors
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