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Konieczny B: Data Engineering Design
Patterns

Data Engineering Design Patterns

Recipes for Solving the Most Common Data Engineering Problems

Transforming Tourism and Hospitality: AI-Powered Service Delivery Innovations explores the profound impact of Artificial Intelligence, IoT, and emerging technologies on the tourism and hospitality industry. This edited volume presents scholarly insights and real-world case studies on AI-driven customer service, sustainable operations, smart maintenance, fraud detection, dynamic pricing, and enhanced guest security. It examines tools like VR, blockchain, and AI-powered HR systems, emphasizing ethical adoption and human-AI collaboration. With robust empirical validation and practical examples, the book offers a valuable resource for academics, professionals, and policymakers seeking to understand and implement AI innovations in service delivery across the sector.

AI-Powered Service Delivery Innovations

Transforming Tourism and Hospitality

ICISAS 2025 is the signature research conference of Curtin University Dubai. It brings together leading experts to discuss how innovation, sustainability, and applied sciences can shape the future of business and society.

Integrating Sustainable Technologies with Data-Driven Solutions

The Future of Business and Society

Large language models (LLMs) have transformed natural language processing, but deploying them in applications introduces numerous technical challenges. Large Language Models: The Hard Parts offers a clear, practical examination of the limitations developers and AI engineers face when building LLM-based applications. With a focus on implementation pitfalls (not just capabilities), this book provides actionable strategies supported by reproducible Python code and open source tools. Readers will learn how to navigate key obstacles in application evaluation, input management, testing, and safety. Designed for builders and technical product leads, this guide emphasizes practical solutions to real-world problems and promotes a grounded understanding of LLM constraints and trade-offs. <ul> <li>Design testing and evaluation strategies for nondeterministic systems</li> <li>Manage context, RAG, &amp; long-context retrieval</li> <li>Address output inconsistency and structural unreliability</li> <li>Implement safety and content moderation frameworks</li> <li>Explore alignment challenges and mitigation techniques</li> <li>Leverage open source models locally</li></ul>

Open Source AI Solutions for Common Pitfalls

Large Language Models - The Hard Part

Red Rock Publishers

The gap between AI's promise and its performance isn't a technology problem. It's a human one.

Most AI projects don't fail loudly. They fail quietly - after months of work, when nothing actually changes.

It doesn't matter whether you run a five-person business or a global enterprise. The question is the same: are you solving a real problem, or chasing a shiny solution?

Our own team once built a chatbot that answered business questions in plain English. In testing, it was magical - 85% accuracy and applause all around. Then we rolled it out to real users, and accuracy fell below 45%. Real people asked entirely different questions than the ones we'd trained for. Months of work. Zero adoption.

Klarna, Duolingo, and IBM all made versions of the same mistake - at far greater scale. The problem was never the technology. It was the question nobody asked before building it.

The AI Decision Map is written for the person who has to make AI work - not the engineer who builds it, but the leader who must answer for it. No jargon. No code.

Drawing on two decades of technology leadership across three continents, doctoral research into why people resist - or embrace - new technology, and candid conversations with hundreds of executives on his podcast, Vin Mitty delivers hard-won clarity about what works, what fails, and why.
Two frameworks. One system.The AI Decision Map(TM) - decide where to apply intelligence, what problem to solve, and how to measure risk, before you build anything.
The AI Value Review(TM) - measure whether it's working, learn from outcomes, and sustain performance long after launch.



This book is for you if you have ever sat in a meeting where someone said "We should be doing more with AI" - and felt a quiet dread.
For the small business owner wondering whether to invest in AI - or wait
For the manager who needs to lead a technology initiative without a technical background
For the executive being pressured by a board that has been reading too many headlines
For anyone accountable for AI outcomes who was never handed a playbook



Readers who have enjoyed Thinking, Fast and Slow, The Innovator's Dilemma, or Good to Great will find this book speaks the same language: clear thinking, human insight, and ideas that genuinely change how you see a problem.

Built from two decades of practice, doctoral research on AI adoption, and real conversations with leaders across healthcare, financial services, legal, education, manufacturing, and retail - this is the decision-making guide executives and owners actually need.

Excitement isn't a strategy. Frameworks turn energy into real value.

The AI Decision Map

Grasp how to deliver fully deployed and scalable, trusted and responsible AI and avoid expensive failed solutions and limited proof-of-concepts with this practical guide covering the lifecyle process of AI product development.

Grasp how to deliver fully deployed and scalable, trusted and responsible AI and avoid expensive failed solutions and limited proof-of-concepts with this practical guide covering the lifecyle process of AI product development.

From AI Ambition to Operational Success

Decision-Quality AI

Taylor and Francis Proceedings in Computer Science and Engineering

Emerging Challenges and Research Frontiers in Digital Transformation

Easy and effective strategies for using AI chatbots Turn your business into a 24/7 customer service powerhouse with AI chatbots that work around the clock! Perfect for anyone drowning in customer emails or missing sales opportunities while they sleep, AI Chatbots For Dummies explains how to build intelligent chatbots from scratch - no coding required. You'll discover how major companies use chatbots to boost efficiency and customer satisfaction and learn to create your own automated assistants for customer service, lead generation, and marketing campaigns that practically run themselves. Inside ... <ul><li>Build no-code solutions</li><li>Create 24/7 customer support</li><li>Accelerate lead generation</li><li>Convert leads into customers</li><li>Reduce overhead and workload</li><li>Improve customer satisfaction</li><li>Scale your company</li><li>Integrate the newest tools</li></ul>

AI ChatBots For Dummies

New Machine Learning Methods for the Sustainable Development Goals

Transforming Risk: AI Applications in Modern Insurance explores how artificial intelligence is revolutionizing the insurance industry. The book examines AI-driven innovations in risk assessment, underwriting, fraud detection, and claims management. By integrating big data analytics, machine learning, and predictive modeling, insurers can make faster, more accurate decisions while enhancing customer experience. It also addresses ethical considerations, regulatory challenges, and the future role of human expertise in an AI-powered landscape. Case studies illustrate real-world applications, from personalized policy pricing to proactive risk prevention. This insightful guide is essential for insurance professionals, technology leaders, and policymakers navigating the evolving digital insurance ecosystem.

AI Applications in Modern Insurance

Transforming Risk

While headlines focus on risks and regulations associated with AI, this groundbreaking book offers business leaders what they truly need-a visionary yet practical roadmap to harness artificial intelligence as a catalyst for unprecedented human and organizational growth.

Winning in Business with Superintelligent AI

Smarter than You

Every project carries uncertainty. Costs overrun, schedules slip, and revenues disappoint - not because project managers lack skill, but because most risk analyses rely on single-point estimates that conceal the true range of possible outcomes. This book provides a structured framework for project risk management, combines it with rigorous quantitative risk analysis, and shows how artificial intelligence makes both accessible to every practitioner, regardless of budget or technical background. Artificial Intelligence and Risk Analysis in Projects delivers a complete, practitioner-focused framework covering the full project risk management lifecycle - from risk identification and qualitative assessment through quantitative risk analysis, response planning, and monitoring and control. The quantitative core of the book addresses Monte Carlo simulation, probabilistic NPV and IRR appraisal, decision trees, Expected Monetary Value, sensitivity analysis, and the calibration of probability distributions from real project data. A case study running through the book compares two capital investment projects under deterministic and probabilistic analysis, demonstrating concretely how single-point estimates overstate expected returns and conceal the probability of loss - in one case by more than 70%. The AI dimension is integrated throughout rather than treated as a separate topic: readers learn how large language models support risk identification and qualitative analysis, how AI enhances the accuracy of Monte Carlo simulation inputs, how structured prompt engineering directs AI toward specific risk management tasks, and how AI performs as an independent model auditor and stress-testing partner. The book addresses the governance, validation, and accountability structures that responsible AI deployment in project environments requires, and closes by demonstrating Monte Carlo simulation using AI alone - making rigorous probabilistic analysis accessible to practitioners who lack access to commercial simulation software.Written for project risk managers, project controls professionals, and students of project management, this is the first book to unite a structured project risk management framework, rigorous quantitative risk analysis, and applied artificial intelligence in a single integrated treatment. It is both a professional reference and a practical guide - grounded in real case studies, immediately applicable to real project decisions, and positioned at the frontier of where the project risk management profession is heading.

Artificial Intelligence and Risk Analysis in Projects

Data Engineering Design Patterns

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