AI at the edge solving real-world problems with embedded machine learning
Edge AI is transforming the way computers interact with the real world, allowing IoT devices to make decisions using the 99% of sensor data that was previously discarded due to cost, bandwidth, or power limitations. With techniques like embedded machine learning, developers can capture human intuiti...
| Other Authors: | , |
|---|---|
| Format: | eBook |
| Language: | Inglés |
| Published: |
Sebastopol, CA :
O'Reilly Media, Inc
2023.
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| Edition: | First edition |
| Subjects: | |
| See on Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009712931906719 |
Table of Contents:
- Cover
- 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
- Colophon.
