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

by Daniel Situnayake, Jenny Plunkett

Artificial Intelligence

Book Details

Book Title

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

Author

Daniel Situnayake, Jenny Plunkett

Publisher

O’Reilly Media, Inc. City: Sebastopol, CA

Publication Date

2023

ISBN

9781098120207

Number of Pages

492

Language

English

Format

PDF

File Size

6.3MB

Subject

Artificial Intelligence

Table of Contents

  • Foreword
  • Preface
  • 1. A Brief Introduction to Edge AI
  • Defining Key Terms
  • Why Do We Need Edge AI?
  • Summary
  • 2. Edge AI in the Real World
  • Common Use Cases for Edge AI
  • Types of Applications
  • Building Applications Responsibly
  • Summary
  • 3. The Hardware of Edge AI
  • Sensors, Signals, and Sources of Data
  • Processors for Edge AI
  • Summary
  • 4. Algorithms for Edge AI
  • Feature Engineering
  • Artificial Intelligence Algorithms
  • Summary
  • 5. Tools and Expertise
  • Building a Team for AI at the Edge
  • Tools of the Trade
  • Summary
  • 6. Understanding and Framing Problems
  • The Edge AI Workflow
  • Do I Need Edge AI?
  • Determining Feasibility
  • Summary
  • 7. How to Build a Dataset
  • What Does a Dataset Look Like?
  • The Ideal Dataset
  • Datasets and Domain Expertise
  • Data, Ethics, and Responsible AI
  • Data-Centric Machine Learning
  • Estimating Data Requirements
  • Getting Your Hands on Data
  • Storing and Retrieving Data
  • Ensuring Data Quality
  • Preparing Data
  • Building a Dataset over Time
  • Summary
  • 8. Designing Edge AI Applications
  • Product and Experience Design
  • Architectural Design
  • Accounting for Choices in Design
  • Summary
  • 9. Developing Edge AI Applications
  • An Iterative Workflow for Edge AI Development
  • Summary
  • 10. Evaluating, Deploying, and Supporting Edge AI Applications
  • Evaluating Edge AI Systems
  • Deploying Edge AI Applications
  • Supporting Edge AI Applications
  • What Comes Next
  • 11. Use Case: Wildlife Monitoring
  • Problem Exploration
  • Solution Exploration
  • Goal Setting
  • Solution Design
  • Dataset Gathering
  • DSP and Machine Learning Workflow
  • Testing the Model
  • Deployment
  • Iterate and Feedback Loops
  • AI for Good
  • Related Works
  • 12. Use Case: Food Quality Assurance
  • Problem Exploration
  • Solution Exploration
  • Goal Setting
  • Solution Design
  • Dataset Gathering
  • DSP and Machine Learning Workflow
  • Testing the Model
  • Deployment
  • Iterate and Feedback Loops
  • Related Works
  • 13. Use Case: Consumer Products
  • Problem Exploration
  • Goal Setting
  • Solution Design
  • Dataset Gathering
  • DSP and Machine Learning Workflow
  • Testing the Model
  • Deployment
  • Iterate and Feedback Loops
  • Related Works
  • Index
  • About the Authors