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{ "item_title" : "Tiny but Mighty", "item_author" : [" Richard Boozman "], "item_description" : "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 futureCloud based AI has limitations: latency in real time applicationsdependency on network connectivityprivacy and data concernshigh operational costsEdge 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 environmentsWhat 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 systemsFrom large models to efficient systemsThroughout 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 systemsEach chapter focuses on practical optimization workflows.Practical applicationssmart cameras and vision systemsIoT devices and sensorswearable technologyindustrial edge AI systemsmobile AI applicationsThese examples reflect real world deployments.Who this book is formachine learning engineersembedded systems developersAI engineersIoT developersprofessionals building edge AI solutionsIf 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.", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/9/79/825/879/9798258795298_b.jpg", "price_data" : { "retail_price" : "24.99", "online_price" : "24.99", "our_price" : "24.99", "club_price" : "24.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Tiny but Mighty|Richard Boozman

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

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Overview

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.

This item is Non-Returnable

Details

  • ISBN-13: 9798258795298
  • ISBN-10: 9798258795298
  • Publisher: Independently Published
  • Publish Date: May 2026
  • Dimensions: 9 x 6 x 0.72 inches
  • Shipping Weight: 0.78 pounds
  • Page Count: 290

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