Tiny but Mighty: Edge AI Engineering: Quantization, Pruning, and On-Device ML for Embedded Systems

Name: Tiny but Mighty: Edge AI Engineering: Quantization, Pruning, and On-Device ML for Embedded Systems
Brand: Independently Published
SKU: 9798258795298
Price: 2694.0 INR
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by Richard Boozman

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ISBN13: 9798258795298
Binding: Paperback
Subject: N/A
Publisher: Independently Published
Publisher Imprint: Independently Published
Publication Date: May 6, 2026
Pages: 290
Original Price: USD 24.99
Language: English
Edition: N/A
Item Weight: 391 grams
BISAC Subject(s): Artificial Intelligence / Generative AI

About The Book

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.

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