About this Book

Several chapters in this book are also part of the open book Machine Learning Systems, which we invite you to read.

“Edge AI Engineering: Hands-on with the Raspberry Pi” is designed as a practical, project-based learning resource that bridges theoretical AI concepts with tangible implementations. This book is part of the open-source Machine Learning Systems initiative, democratizing access to AI education and applications.

Key Features

  1. Progressive Learning Path: The book structure follows a natural progression from basic to advanced concepts, beginning with foundational computer vision applications and advancing to generative AI techniques.
  2. Model-Specific Optimizations: Each chapter provides targeted guidance for different Raspberry Pi models, helping you maximize performance whether using a Pi Zero 2W or Pi 5.
  3. Open-Source Foundation: We emphasize accessible tools and frameworks, including Edge Impulse Studio, TensorFlow Lite, PyTorch, Transformers, and Ollama, ensuring you can continue your learning journey with widely available resources.
  4. Practical Problem-Solving: Rather than abstract exercises, each project addresses real-world challenges that demonstrate the practical value of Edge AI.
  5. Resource Optimization Techniques: Learn essential strategies for deploying AI on resource-constrained devices, balancing performance needs with hardware limitations.
  6. Cross-Domain Applications: Explore implementations spanning computer vision, natural language processing, and physical computing, showcasing the versatility of Edge AI.

Structure and Organization

The book is organized into two main sections:

  1. Fixed Function AI (Computer Vision): Chapters covering image classification, object detection, and specialized applications like object counting.
  2. Generative AI (Language and Vision Models): Chapters exploring Small Language Models, Vision-Language Models, physical computing integration, and advanced optimization techniques.

Each chapter follows a consistent format that includes:

  • Conceptual background and theory
  • Step-by-step implementation guides
  • Practical projects with complete code
  • Performance optimization strategies
  • Ideas for further exploration

Prerequisites

While designed to be accessible, readers will benefit from:

  • Basic Python programming knowledge
  • Familiarity with Linux command-line basics
  • Elementary understanding of machine learning concepts
  • Previous experience with Raspberry Pi (helpful but not required)

By completing this book, you’ll possess the skills to design, implement, and optimize Edge AI applications across a wide range of use cases, leveraging the unique capabilities of the Raspberry Pi platform to bring intelligence to the edge.