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Navarro R: Work Reimagined

Work Reimagined

The AI Era

Based on Understanding Characteristics of Customer Well-being

The Creation of User Experience and Evolution of Management

Edge AI is transforming everyday devices by putting intelligence where it matters most: directly inside the hardware. With on-device inference, a camera can recognize a visitor instantly, a phone can translate speech without streaming audio to the cloud, and a wearable can detect anomalies in real time-fast, private, and reliable even when the network disappears.This book is your practical guide to building exactly those kinds of systems with the Raspberry&nbsp;Pi AI&nbsp;HAT+ and the Hailo-8L accelerator. You'll start with clear foundations: core&nbsp;AI and machine-learning concepts, how neural networks work, and what truly distinguishes Edge&nbsp;AI from cloud AI-plus an honest look at ethical considerations and future impacts.Then it's straight to hands-on physical computing. Step by step, you'll set up Raspberry&nbsp;Pi&nbsp;OS, power and cooling, and develop in Python using the Thonny&nbsp;IDE. You'll learn GPIO basics with lights and servos, mount the AI&nbsp;HAT+ hardware, install and verify the Hailo software stack, and connect the right camera-official modules or USB webcams, even multiple cameras.From your first pipeline to real projects, you'll run person detection, pose estimation, segmentation, and depth estimation, then level up with YOLO object detection: smart alerts, guest counters, and custom extensions. You'll even connect vision to motion by combining gesture recognition with servo-driven mechanisms, including a robotic arm.With troubleshooting tips, hardware essentials, and a practical Python refresher, this book turns Edge&nbsp;AI from buzzword into buildable reality.

AI Projects for the Raspberry Pi with the AI HAT+

Edge AI Made Practical

Innovations in Intelligent Internet of Everything IoE

Biomedical Applications of AI and IoMT in Smart Healthcare

Tech, War and the Fight for Humanity

Thundercloud

How Enterprises Build, Govern, and Operationalize AI

Trust at Scale

Theory and Lessons from Leading Companies

AI-Powered Functions

Risk, Governance and Society

From Data Analytics to Global Risk Governance

Evolution of Anomaly Detection

Machine Learning, Artificial Intelligence, and Digital technologies in agritech and food supply chains have significantly enhanced production efficiency and transparency in recent years. Together, they can empower stakeholders across the supply chain to operate more efficiently, transparently, and intelligently, transforming traditional supply chains into more integrated and responsive networks. This two-volume research work makes a broad study of agritech food supply chains.Including contributions from a global team of scholars, this second volume is focused on applications of technology, from farming and harvesting to processing, storage, and transport, and offers a detailed analysis of these applications and their implications for academic research. It will be of great interest to researchers, scholars, and graduate students in the fields of agricultural science, innovation, and supply chain management.Sneha Kumari 
 is an Associate Professor at Symbiosis School of Economics, India. She is an Associate Researcher at Circular Economy and Territories for the European Chair of Excellence. She received the Scientific High-Level Visiting Fellowship from The French Institute in India, Embassy of France in India. Her primary interests are big data, agriculture supply chain, and agriculture value chain.
 V G Venkatesh
 is a Professor at EM Normandie Business School, France. He is a global supply chain enthusiast and consultant with more than two decades of industry and academic experiences at global institutions. He is the active researcher with editorial positions of leading journals, industry board member and keynote speaker of leading forums.
 Jorge Hernández
 is a Full Professor (Professor Titular) at Adolfo Ibáñez University, Chile. His main research areas are modelling, simulation and optimization of enterprise decision-making processes in operations and supply chain management domains. He regularly collaborates with the Ministry of Agriculture of Chile and is also Honorary Professor at the University of Liverpool in the United Kingdom.
 Zakaria El Hathat 
 is an Assistant Professor at Rabat Business School, International University of Rabat, Morocco. His research examines information technology, blockchain, and artificial intelligence (AI). He also received the 2024 PhD Dissertation Award from the Center for Applied Research and Innovation in Supply Chain–Africa (CARISCA).

Food Supply Chains

Digitalisation and Artificial Intelligence in Agritech and Food Supply Chains, Volume II

This book is a comprehensive guide designed to equip engineers, data scientists, and AI practitioners with the principles, tools, and strategies needed to ensure reliability, performance, and accountability in Large Language Models (LLMs). 
The book begins by laying the groundwork with the foundations of observability, introducing LLMs, their significance in modern AI, and the critical role observability plays in maintaining robust systems. It then explores SRE principles, service level objectives, and incident response, while distinguishing the unique observability challenges that arise in AI and ML systems. Building on this foundation, the book dives into measuring performance, from defining SLOs tailored for LLMs to monitoring computational and token-level metrics. Readers gain practical insights into structured logging, debugging, and distributed tracing methods that provide visibility into complex LLM workflows. Scaling challenges are addressed through strategies for cross-model observability, autoscaling, latency reduction, and fault-tolerant infrastructure design. The book further explores chaos engineering, guiding readers through resilience testing in LLMs and the automation of chaos experiments in CI/CD pipelines. Finally, it highlights monitoring, retraining, and ethical considerations in AI observability, including governance, privacy, and accountability.

In conclusion, this book provides a holistic roadmap to building reliable, transparent, and future-ready LLM systems.

What you will learn:
How to design observability pipelines for LLMs, including token-level logging, prompt tracing, and 



latency analysis.
Techniques for applying chaos engineering principles to test LLM robustness under stress and



failure scenarios.
Methods for building SLOs, SLAs, and dashboards tailored to inference quality and model



reliability.
Strategies for monitoring hallucinations, drift, bias, and ethical failures in real-time.




Who this book is for:
This book is for AI infrastructure engineers, SREs, machine learning platform teams, and applied AI practitioners deploying or maintaining LLM-based applications.

Site Reliability and Chaos Engineering for AI at Scale

Observability for Large Language Models

FOUNDATIONS OF FINANCIAL DATA SCIENCE: METHODS, MODELS, AND APPLICATIONS

'Michael Auerbach has one of the most original minds I've ever encountered . . . This is a dazzling and entertaining book . . . But it is also an urgent warning' PATRICK RADDEN KEEFE, author of Empire of Pain A brilliant exploration of the phenomenon of prediction markets that asks: what happens when democracy becomes another commodity? Prediction markets are sweeping the world, fundamentally changing the nature of gambling - and the very ways in which we attempt to divine the future. Posed as binary 'Yes/No' questions about future events, prediction markets allow any difference of opinion - whether on pop culture or politics, wars or assassinations - to be financialised. A regulatory minefield with moral implications that divide nations, governments and bettors, they have proved as effective at accurately predicting the timing of missile strikes as they have at calling the outcomes of elections.But are these markets really a new phenomenon? And what does it mean when those tasked with safeguarding democracy hold financial stakes in its volatility?From Renaissance Rome, where merchants wagered on papal conclaves, through nineteenth-century London's wartime coffee house gossip, to the Pentagon's ill-fated terrorism futures exchange and blockchain-based betting on Polymarket today, The Big Bet tells the gripping five-hundred-year story of a permissive regime that no one designed and no one voted for.Drawing on deep expertise, Michael Auerbach examines how these markets are changing our world, and what they mean for the future where democracy becomes just another commodity to be priced and traded. Because prediction markets are not an anomaly - they are the logical endpoint of a society that has applied market capitalism to every aspect of life. And in a world where everything is a wager and nothing is off limits, knowing where the chips will fall matters more than ever.

Prediction Markets and the Future of Democracy

The Big Bet

In today’s AI-driven business environment, the ability to translate data into sound decisions is increasingly important. This textbook introduces the foundations and practical applications of business analytics, machine learning, and artificial intelligence. Readers learn how to explore and visualize business data, build and evaluate predictive models, and apply analytical methods to support business decisions.The book progresses from core concepts such as data visualization, optimization, model evaluation, supervised learning, and unsupervised learning to advanced topics in deep learning, computer vision, generative AI, text mining, natural language processing, and large language models. It also addresses the limitations and ethical challenges of AI. Step-by-step explanations, business examples, and Python exercises help readers connect analytical concepts with real-world applications.Designed for students and professionals with little or no prior experience in programming or machine learning, the book develops skills progressively from basic concepts to advanced methods. It prepares readers to select, implement, interpret, and evaluate AI-based analytical approaches for a wide range of business problems.Hyunhwan “Aiden” Lee
 is Assistant Professor of Marketing at California State University, Long Beach, USA. His research focuses on marketing analytics, brand and content analytics, and the application of artificial intelligence to business and marketing problems. His work uses machine learning, natural language processing, computer vision, location-based data analysis, large-scale data analysis, and mathematical modeling.
 Reo Song
 &nbsp;is Professor of Marketing and Director of the M.S. in Marketing Analytics program at California State University, Long Beach. His research focuses on machine learning, AI, business analytics, and digital marketing, with publications in leading journals including&nbsp;
 Journal of Marketing
 &nbsp;and&nbsp;
 Production and Operations Management
 . He has advised organizations on AI and machine learning applications, delivers frequent invited talks on AI and business analytics, and contributes regularly to newspaper columns on AI and digital transformation.

An Advanced Guide to Data-Driven Decision Making

Business Analytics and Artificial Intelligence

Work Reimagined

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