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portada Tiny but Mighty. Edge AI Engineering: Quantization, Pruning, and On-Device ML for Embedded Systems
Type
Physical Book
Year
2026
Language
English
Pages
290
Format
Paperback
Dimensions
22.9x15.2x1.5 cm
ISBN13
9798258795298

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

Richard Boozman (Author) · Independently published · Paperback

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

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

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 applicationsdependency on network connectivityprivacy and data concernshigh operational costs

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

With the right techniques, you can:

reduce inference latencyimprove privacy and securityoperate offlinelower infrastructure costsdeploy AI in constrained environments
What you will learnfundamentals of edge AI systemsmodel quantization techniquespruning and model compressionoptimizing neural networks for efficiencyhardware aware model designdeploying models on embedded devicesworking with edge AI frameworksperformance benchmarking and tuningbalancing accuracy and efficiencybuilding 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 performanceoptimize inference speeddeploy models on constrained hardwaredesign efficient AI pipelinestest and improve on device performancebuild reliable edge AI systems

Each chapter focuses on practical optimization workflows.


Practical applicationssmart cameras and vision systemsIoT devices and sensorswearable technologyindustrial edge AI systemsmobile AI applications

These examples reflect real world deployments.


Who this book is formachine learning engineersembedded systems developersAI engineersIoT developersprofessionals 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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