AI at the Edge - Helion

ebook

Autor: Daniel Situnayake, Jenny Plunkett
ISBN: 9781098120160
stron: 514, Format: ebook
Data wydania: 2023-01-10
Księgarnia: Helion

Cena książki: 245,65 zł (poprzednio: 285,64 zł)
Oszczędzasz: 14% (-39,99 zł)

Osoby, które kupiły tę książkę, wybierały także »

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 intuition and deploy it to any target--from ultra-low power microcontrollers to embedded Linux devices.

This practical guide gives engineering professionals, including product managers and technology leaders, an end-to-end framework for solving real-world industrial, commercial, and scientific problems with edge AI. You'll explore every stage of the process, from data collection to model optimization to tuning and testing, as you learn how to design and support edge AI and embedded ML products. Edge AI is destined to become a standard tool for systems engineers. This high-level road map helps you get started.

Develop your expertise in AI and ML for edge devices
Understand which projects are best solved with edge AI
Explore key design patterns for edge AI apps
Learn an iterative workflow for developing AI systems
Build a team with the skills to solve real-world problems
Follow a responsible AI process to create effective products

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Spis treści

AI at the Edge eBook -- spis treści

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
- OReilly Online Learning
- How to Contact Us
- Acknowledgments
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
    - Bandwidth
    - Latency
    - Economics
    - Reliability
    - Privacy
    - Using BLERP
  - Edge AI for Good
  - Key Differences Between Edge AI and Regular AI
    - Training on the edge is rare
    - The focus of edge AI is on sensor data
    - ML models can get very small
    - Learning from feedback is limited
    - Compute is diverse and heterogeneous
    - Good enough is often the goal
    - Tools and best practices are still evolving
- Summary
2. Edge AI in the Real World
- Common Use Cases for Edge AI
  - Greenfield and Brownfield Projects
  - Real-World Products
    - Preventing forest fires using power line fault detection
    - Protecting first responders with intelligent wearables
    - Understanding elephant behavior with smart collars
- Types of Applications
  - Keeping Track of Objects
    - Key benefits for object tracking
  - Understanding and Controlling Systems
    - Key benefits for understanding and controlling systems
  - Understanding People and Living Things
    - Key benefits for understanding people and living things
  - Transforming Signals
    - Key benefits for transforming signals
- Building Applications Responsibly
  - Responsible Design and AI Ethics
  - Black Boxes and Bias
  - Technology That Harms, Not Helps
    - The costs of negligence
    - Mitigating societal harms
- Summary
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
    - Low-end MCUs
    - High-end MCUs
    - Digital signal processors (DSPs)
  - System-on-Chip
  - Deep Learning Accelerators
  - FPGAs and ASICs
  - Edge Servers
  - Multi-Device Architectures
  - Devices and Workloads
- Summary
4. Algorithms for Edge AI
- Feature Engineering
  - Working with Data Streams
  - Digital Signal Processing Algorithms
    - Resampling
    - Filtering
    - Spectral analysis
    - Image feature detection
  - Combining Features and Sensors
- Artificial Intelligence Algorithms
  - Algorithm Types by Functionality
    - Classification
    - Regression
    - Object detection and segmentation
    - Anomaly detection
    - Clustering
    - Dimensionality reduction
    - Transformation
  - Algorithm Types by Implementation
    - Conditionals and heuristics
    - Classical machine learning
    - Deep learning
    - Combining algorithms
    - Postprocessing algorithms
    - Fail-safe design
  - Optimization for Edge Devices
    - Choice of algorithm
    - Compression and optimization
  - On-Device Training
- Summary
5. Tools and Expertise
- Building a Team for AI at the Edge
  - Domain Expertise
  - Diversity
  - Stakeholders
  - Roles and Responsibilities
    - Knowledge and understanding
    - Planning and execution
    - Algorithm development
    - Product engineering
    - Technical services
  - Hiring for Edge AI
  - Learning Edge AI Skills
    - Practice
    - Theory
- Tools of the Trade
  - Software Engineering
    - Operating systems
    - Programming and scripting languages
    - Dependency management
    - Containerization
    - Distributed computing
    - Cloud providers
  - Working with Data
    - Data capture
    - IoT device management
    - Data storage and management
    - Data pipelines
  - Algorithm Development
    - Mathematical and scientific computing libraries
    - Data visualization
    - Interactive computing environments
    - Digital signal processing
    - Deep learning frameworks
    - Model compression and optimization
    - Experiment tracking
    - Automated machine learning (AutoML)
    - Machine learning operations (MLOps)
  - Running Algorithms On-Device
    - Math and DSP libraries
    - Machine learning inference
    - On-device learning
  - Embedded Software Engineering and Electronics
    - Embedded hardware tools
    - Development boards
    - Embedded software tools
    - Emulators and simulators
    - Embedded Linux
    - Automated hardware testing
  - End-to-End Platforms for Edge AI
- Summary
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?
    - Things that dont work well on the edge
    - Disadvantages of edge compute
  - Do I Need Machine Learning?
    - Reasons to use ML
    - The drawbacks of ML
    - Knowing when to use ML
  - Practical Exercise
- Determining Feasibility
  - Moral Feasibility
  - Business Feasibility
    - Proving benefit
    - Understanding constraints
  - Dataset Feasibility
  - Technological Feasibility
    - Framing problems
    - Device capabilities and solution choice
  - Making a Final Decision
  - Planning an Edge AI Project
    - Defining acceptable performance
    - Understanding time and resource constraints
- 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
  - 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
    - Avoiding label noise
  - Common Data Errors
  - Drift and Shift
  - The Uneven Distribution of Errors
- Preparing Data
  - Labeling
    - Not all problems require labels
    - Semi-supervised and active learning algorithms
    - Bias in labeling
    - Labeling tools
      - Annotation tools
      - Crowdsourced labeling
      - Assisted and automated labeling
      - Semi-supervised and active learning
  - Formatting
  - Data Cleaning
    - Auditing your dataset
    - Fixing issues
      - Amending values
      - Substituting values
      - Excluding records
    - Evaluation and automation
    - Fixing balance issues
  - Feature Engineering
  - Splitting Your Data
    - How is data split?
    - Pitfalls when splitting data
  - Data Augmentation
  - Data Pipelines
- Building a Dataset over Time
- Summary
8. Designing Edge AI Applications
- Product and Experience Design
  - Design Principles
  - Scoping a Solution
  - Setting Design Goals
    - Systemic goals
    - Technical goals
    - Values-based design goals
- Architectural Design
  - Hardware, Software, and Services
  - Basic Application Architectures
    - Basic flow
    - Ensemble flow
    - Parallel flow
    - Series flow
    - Cascading flow
    - Sensor fusion flow
  - Complex Application Architectures and Design Patterns
    - Heterogeneous cascade
    - Multi-device cascade
    - Cascade to the cloud
    - Intelligent gateway
    - Human-in-the-loop
  - Working with Design Patterns
- Accounting for Choices in Design
  - Design Deliverables
- Summary
9. Developing Edge AI Applications
- An Iterative Workflow for Edge AI Development
  - Exploration
  - Goal Setting
    - Calling it quits
  - Bootstrapping
    - Why bootstrapping is helpful
    - Developing a baseline algorithm
    - Our first hardware
    - Responsible AI review
  - Test and Iterate
    - Feedback loops
    - Iterations in practice
      - Updating your plans
    - Ethical AI review
  - Deployment
  - Support
- Summary
10. Evaluating, Deploying, and Supporting Edge AI Applications
- Evaluating Edge AI Systems
  - Ways to Evaluate a System
    - Evaluating individual components
    - Evaluating integrated systems
    - Simulated real-world testing
    - Real-world testing
      - Quality assurance testing
      - Usability testing
    - Monitoring a deployed system
  - Useful Metrics
    - Algorithmic performance
      - Loss
      - Accuracy
      - Confusion matrix
      - Precision and recall
      - Positive and negative rates
      - F1 score and MCC
      - ROC and AUC
      - Error metrics
      - Mean average precision
    - Computational and hardware performance
      - Memory
      - Floating-point operations (FLOPs)
      - Latency
      - Duty cycle
      - Energy
      - Thermal
  - Techniques for Evaluation
  - Evaluation and Responsible AI
- Deploying Edge AI Applications
  - Predeployment Tasks
  - Mid-Deployment Tasks
  - Postdeployment Tasks
- Supporting Edge AI Applications
  - Postdeployment Monitoring
    - Types of feedback from deployed systems
      - Data samples
      - Distribution changes
      - Application metrics
      - Outcomes
      - User reports
  - Improving a Live Application
    - Solving problems using feedback
    - Refining an algorithm over time
    - Supporting multiple deployed algorithms
  - Ethics and Long-Term Support
    - Performance degradation
    - New information
    - Evolving cultural norms
    - Changing legal standards
- What Comes Next
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
    - Public project
  - Choose Your Hardware and Sensors
    - Hardware configuration
  - Data Collection
    - Connecting your device directly to Edge Impulse for 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
    - Visual mode
    - EON Tuner
- 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
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
    - Edge Impulse public project
  - Choose Your Hardware and Sensors
    - Hardware configuration
  - 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
    - Visual mode
- 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
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
    - Edge Impulse public project
  - Choose Your Hardware and Sensors
    - Hardware configuration
  - Data Collection
  - Data Ingestion Firmware
    - Mobile phone
    - nRF Edge Impulse mobile phone application
  - Cleaning Your Dataset
  - Dataset Licensing and Legal Obligations
- DSP and Machine Learning Workflow
  - Digital Signal Processing Block
  - Machine Learning Blocks
    - Visual mode
    - Anomaly detection
- 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