Tiny but Mighty: Edge AI Engineering: Quantization, Pruning, and On-Device ML for Embedded Systems (Paperback) (ISBN-13: 9798258795298)

Powerful AI does not have to live in the cloud.

From smart cameras to wearable devices and industrial sensors, intelligent systems are moving closer to the edge. The challenge is making models smaller, faster, and efficient enough to run on limited hardware.

"Tiny but Mighty" is a practical, engineering focused guide to building optimized AI systems for edge devices using Python and modern machine learning frameworks.

This book teaches you how to compress, optimize, and deploy models that perform efficiently in real world embedded environments.

Why edge AI is the future

Cloud based AI has limitations:

• latency in real time applications
• dependency on network connectivity
• privacy and data concerns
• high operational costs

Edge AI solves these challenges by running models directly on devices.

With the right techniques, you can:

• reduce inference latency
• improve privacy and security
• operate offline
• lower infrastructure costs
• deploy AI in constrained environments

What you will learn

• fundamentals of edge AI systems
• model quantization techniques
• pruning and model compression
• optimizing neural networks for efficiency
• hardware aware model design
• deploying models on embedded devices
• working with edge AI frameworks
• performance benchmarking and tuning
• balancing accuracy and efficiency
• building real time on device AI systems

From large models to efficient systems

Throughout the book, you will learn how to:

• shrink large models without losing performance
• optimize inference speed
• deploy models on constrained hardware
• design efficient AI pipelines
• test and improve on device performance
• build reliable edge AI systems

Each chapter focuses on practical optimization workflows.

Practical applications

• smart cameras and vision systems
• IoT devices and sensors
• wearable technology
• industrial edge AI systems
• mobile AI applications

These examples reflect real world deployments.

Who this book is for

• machine learning engineers
• embedded systems developers
• AI engineers
• IoT developers
• professionals building edge AI solutions

If you want to deploy AI models outside the cloud and into real devices, this book provides the roadmap.

Optimize aggressively.
Deploy efficiently.
Build AI at the edge.

Author: Richard Boozman
ISBN-13: 9798258795298
Publisher: Independently Published
Language: English
Published: 05/06/2026
Pages: 290
Format: Paperback
Weight: 0.78lbs
Size: 9.00h x 6.00w x 0.72d