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Konieczny B: Data Engineering Design
Patterns

Data Engineering Design Patterns

Recipes for Solving the Most Common Data Engineering Problems

AI-Powered Insurance is a comprehensive exploration of how Artificial Intelligence is revolutionizing the global insurance industry. This book brings together cutting-edge research and real-world case studies, covering AI's transformative impact across underwriting, risk management, customer experience, and policy personalization. It examines emerging technologies, ethical challenges, and security concerns while analyzing machine learning applications in insurance policy evaluation. From automation versus human expertise to bibliometric research trends, the book offers diverse perspectives from leading experts. An essential resource for researchers, practitioners, and policymakers, it charts the future of intelligent, data-driven insurance in an increasingly digital world.

Exploring New Frontiers

AI-Powered Insurance

How deep learning—from Google Translate to driverless cars to personal cognitive assistants—is changing our lives and transforming every sector of the economy. The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy. Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.

The Deep Learning Revolution

Banish Your Mouse. The Linux Command Line isn’t just a reference manual. It’s a complete education on how Linux really works. From your first tentative keystrokes to writing powerful shell scripts, you’ll build the confidence and understanding to make your system work for you. Now in its third edition, this best-selling guide has been fully updated with modern examples, nearly 40 pages of new content, and downloadable scripts from LinuxCommand. Used in more than a thousand classrooms and libraries worldwide, it has helped over 150,000 readers gain fluency and confidence in Linux. Author and command line guru William Shotts delivers not just commands, but genuine insight, guided by his philosophy: Make everything as simple as possible, but no simpler. Through short, digestible chapters, you’ll learn to: <ul><li>Navigate the filesystem, manage files, and configure your environment</li><li>Chain commands, redirect input/output, and use regular expressions</li><li>Control processes, manage packages, and work with networking tools</li><li>Edit text with Vim</li><li>Write shell scripts to automate tasks and build custom tools</li><li>Use core utilities like grep, cut, sed, and tar to process data</li><li>Manage filesystems and compile programs from source</li></ul> Whether you’re managing servers, studying cybersecurity, or just curious about your computer, The Linux Command Line gives you the knowledge to command your system with confidence.

A Complete Introduction

The Linux Command Line, 3rd Edition

'Engaging, insightful, panoramic' Mustafa Suleyman, CEO of Microsoft AI &amp; Cofounder of DeepMind 'An eye-opening exploration of a revolution unfolding before our eyes' New York Journal of Books Stunning advances in digital technology have introduced a new wave of human-like AI systems. Chatbots like ChatGPT, Claude, and Gemini are already reshaping economies, challenge democracies, and reshaping society in unpredictable ways. And soon, these AI systems could make autonomous decisions on their users' behalf, transforming everything we do. Understanding how they work is crucial. Can AI systems think, know, and understand?Could they manipulate or deceive you, and if so, what might they make you do? Whose interests do they represent? When will they be able to move beyond words and take action in the real world? Neuroscientist and AI researcher Christopher Summerfield explores these questions, charting AI's evolution from early ideas in the seventeenth century to today's deep neural networks. His book is the most accessible, up-to-date, and authoritative exploration of this radical technology. With an understanding of AI's inner workings, we can address the existential question of our age: can we look forward to a technological utopia, or are we writing ourselves out of history? 'As a leading authority...Summerfield is perfectly situated to explore the meaning and implications of these machines that are so uncannily like - and unlike - ourselves' Brian Christian, co-author of Algorithms to Live by 'You might choose to be alarmed, excited, or indifferent to LLMs, but you should read Chris's book before you decide' Mike Woolridge, author of The Road to Conscious Machines

How AI Learned to Talk and What It Means

These Strange New Minds

A complete guide to deep neural networks – the technology behind AI – covering fundamental and advanced techniques to apply machine learning in real-world scenarios. Deep Learning Crash Course goes beyond the basics of machine learning to delve into modern techniques and applications of great interest right now, and whose popularity will only grow in the future. The book covers topics such as generative models (the technology behind deep fakes), self-supervised learning, attention mechanisms (the tech behind ChatGPT), graph neural networks (the tech behind AlphaFold), and deep reinforcement learning (the tech behind AlphaGo). This book bridges the gap between theory and practice, helping readers gain the confidence to apply deep learning in their work.

Deep Learning Crash Course

A detailed and up-to-date walkthrough for entrepreneurs with limited (or non-existent) coding skills who want to build profitable software companies using new gen-AI tools In The Vibe Coding Playbook: Building Your Tech Business With AI, renowned AI and data science educator Siraj Raval walks you through exactly what you need to do to build a technology business with generative AI-powered code assistants. Raval offers step-by-step guidance for non-technical professionals and entrepreneurs interested in creating scalable, profitable enterprises without spending years learning how to code. This book conceives of new artificial intelligence tools, like Cursor, as "co-founders," lighting your way to constructing valuable software products and services. You'll learn to build minimally viable products (MVPs), iterate on your software products as you develop and after launch, and grow your company while maintaining a lean, efficient, solopreneur-focused structure. Inside the book: <ul> <li>Detailed guidance for entrepreneurs interested in creating powerful tech solutions for niche problems and markets without hiring expensive software developers</li> <li>Strategies for using generative AI tools to substitute for traditional technical co-founders</li> <li>Illustrative case studies from real-world founders who built successful technology businesses without learning to code</li> <li>Useful tools for non-technical entrepreneurs, including prompt libraries, decision trees, QR codes linking to video tutorials demonstrating key techniques, and access to an exclusive online community of like-minded founders</li> </ul> Perfect for ambitious professionals and entrepreneurs who want to build a successful technology company now - using commercially available AI tools - The Vibe Coding Playbook is your personal roadmap to creating useful and profitable software for customers without learning how to code.

Building Your Tech Business with AI

The Vibe Coding Playbook

AI Marketing Essentials: Concepts and Practice for the Digital Age explores the dynamic intersection of AI technology and marketing, providing theoretical frameworks for understanding its fast-growing potential in the development and implementation of successful marketing strategies.By guiding the reader through the principles, tools, and techniques of AI in marketing, this comprehensive textbook will help readers understand how they can leverage AI to transform different marketing functions throughout the lifecycle of a product, from initial strategy to product development, pricing, and distribution. This book combines theoretical understanding with insights from industry to reinforce learning, featuring real-life examples and case studies from different industries and countries across the world. In the first section, the book introduces the AI marketing discipline and how it has evolved. It explains the fundamental AI concepts such as machine learning, big data, NLP, and algorithms, which are crucial to understanding how they work.In further sections, the applications of these technologies are explored and explained in various marketing functions through case studies from multiple industries and concepts from marketing and technology. The application of these functions across retail, products, service, and business-to-business operations is then explored, followed by a critical discussion of the social and ethical dimensions and future directions for the use of AI in marketing.An accessible guide for understanding the AI marketing space, this book will suit undergraduate and postgraduate students as well as marketing professionals looking to build foundational knowledge in the concepts and impact of AI in marketing.

Concepts and Practice for the Digital Age

AI Marketing Essentials

Build AI applications that inspire trust in your users and solve real-world problems Making AI Work for People: A Framework for Designing and Building Impactful AI-Powered Applications empowers software engineers, product managers, and app designers to create AI-enabled applications that inspire trust, solve hard problems, and prioritize human needs. Written by Asmaa Ibrahim, Senior AI Consultant at Google and leader of the Gemini in Firebase product launch, this book shows you how to use rapid technological advancement to build successful products that your users can't get enough of. At the heart of the book is the PRESS framework, a systematic approach ensuring AI products embody five essential principles: People-first design, Responsible in the core, Explainability and interpretability, Safety via benchmarking, and Sustainability and long-term impact. The book demonstrates how to identify AI integration opportunities, design human-centered solutions, measure success with business-focused KPIs, and scale from MVP to production-grade systems while managing costs and infrastructure challenges. This comprehensive guide is essential for software engineers integrating AI into product development, product managers leading AI initiatives, and app designers creating human-AI interfaces. It's a must-read for executives and technical team managers seeking systematic approaches to responsible AI development that drive measurable business outcomes while maintaining trust, accessibility, and ethical integrity throughout the product lifecycle.

A Framework for Designing and Building Impactful AI-Powered Applications

Making AI Work for People

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

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.

Data Engineering Design Patterns

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