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Generative AI at AWS
Bridge business goals and technical execution to build, deploy, and govern generative AI solutions on AWSKey FeaturesAlign business strategy with practical generative AI use cases on AWSBuild MVPs, agents, and production systems with Bedrock and SageMakerApply governance, scaling, and responsible AI practices across industriesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionCut through the noise around generative AI and learn how to turn promising ideas into secure, scalable solutions on AWS. This book helps you connect business priorities with technical execution, so you can identify worthwhile use cases, select the right models and services, and move from pilot to production with confidence.You explore the fundamentals of generative AI, understand how foundation models and agents work, and see where services such as Amazon Bedrock AgentCore and Amazon SageMaker AI fit into a modern AI stack. From there, the book guides you through preparing data, building an MVP, deploying production-ready applications, and designing for scalability, governance, and responsible AI.Real-world industry examples and practical decision frameworks help you evaluate when to use generative AI, when traditional approaches are a better fit, and how to measure business value. You also examine advanced topics such as agentic AI, emerging patterns, and the future direction of enterprise AI.By the end of this book, you will be able to plan, build, and govern generative AI solutions on AWS that deliver measurable value for your organization.What you will learnUnderstand how generative AI creates business valueCompare models, prompts, fine-tuning, and RAGNavigate the AWS generative AI stack with confidencePrepare data and select models for real use casesBuild MVPs and production-ready AI applicationsApply governance, ethics, and responsible AI controlsEvaluate agentic AI patterns and emerging trendsMeasure impact across enterprise AI initiativesWho this book is forDevelopers, solutions architects, technical product managers, innovation leaders, CTOs, and business decision-makers who want to plan, build, and scale generative AI solutions on AWS. It is ideal for teams moving from experimentation to production and for leaders aligning AI initiatives with business outcomes. A basic understanding of cloud concepts and software delivery is helpful.
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System Innovation in an AI-Driven World
As artificial intelligence fundamentally restructures contemporary socio-technical infrastructure, reductive technological paradigms fail to capture emerging institutional complexities. System Innovation in an AI-Driven World establishes a rigorous theoretical framework for transitioning from discrete algorithmic deployment to comprehensive systemic transformation. This volume equips scholars and strategic architects with the epistemological and methodological tools required to analyze, model, and govern complex socio-technical transitions under the influence of pervasive automation.The monograph interrogates the dialectical relationship between advanced computational architectures—specifically deep neural networks, autonomous multi-agent systems, and predictive modeling—and legacy institutional frameworks. Utilizing cross-disciplinary case studies from cyber-physical systems, public health logistics, and decentralized socio-ecological regimes, the text explicates the mechanics of systemic innovation within volatile technological landscapes. The foundational findings indicate that optimal AI integration is not a function of raw algorithmic efficacy; rather, it depends on systemic absorptive capacity, adaptive regulatory polycentricity, and dynamic feedback integration. The research offers structural typologies for mapping non-linear systemic feedback loops, mitigating algorithmic opacity, and synthesizing human-machine governance frameworks. Consequently, the volume demonstrates that sustainable institutional efficacy resides in embedding computational intelligence within fundamentally reconceptualized, non-linear system architectures.This volume is explicitly designed for academic researchers, systems theorists, computational sociologists, and advanced policymakers. It constitutes an indispensable resource for graduate-level seminars and senior scholars investigating the intersection of technology policy, complex adaptive systems, and institutional design amid the contemporary digital transition.
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Essentials of Generative AI
This book provides a concise yet comprehensive introduction to generative artificial intelligence.The first part explains the foundational technologies and architectures that support the realization of generative models. It covers evolved and deepened elements, word embeddings as a representative example of representation learning, and the Transformer as a network foundation, along with its underlying attention mechanism. Reinforcement learning, which became essential for elevating large-scale language models to language generation models, is also discussed in detail, focusing on essential aspects.The second part deals with language generation. It starts by elucidating language modelsand introduces large-scale language models with broad applications as the foundational architecture of language processing, further discussing language generation models as their evolution. Though not common terminology, in this book, models such as ChatGPT and Llama 2, which are large-scale language models fine-tuned using reinforcement learning, are referred to as generative language models.The third part addresses image generation, discussing variational autoencoders and the remarkable diffusion models. Additionally, it explains Generative Adversarial Networks(GAN). Although GAN poses challenges due to unstable learning, their conceptual framework is widely applicable, especially Wasserstein GAN seems suitable for introducing optimal trans- port distance, which is utilized in various scenarios.This book primarily serves as a companion for researchers or graduate students in machine learning, aiming to help them understand the essence of generative AI and lay the groundwork for advancing their own research.
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Generative AI for Business Innovation
This book is a comprehensive guide for business leaders, CXOs, and technical architects looking to adopt Generative AI technologies to create innovative applications for their organizations and clients. The book is divided into three parts. In Part 1, readers will learn the fundamental concepts of Generative AI, such as foundational models and large language models like ChatGPT. It provides an overview of various Generative AI techniques, including Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). This section helps leaders understand the benefits and risks associated with Generative AI, supported by industry case studies showcasing real-world applications. Part 2 focuses on developing a winning strategy for implementing Generative AI. It discusses choosing the right approach, building the necessary architecture and infrastructure, and ensuring data quality for training models. It also addresses technical challenges like computing power and offers best practices for execution. In Part 3, the book explores best practices for adopting Generative AI, emphasizing governance, talent development, and continuous improvement. It also discusses emerging trends and the societal impact of Generative AI, particularly concerning sustainability. By the end, readers will be equipped with the knowledge and strategies to effectively harness Generative AI, paving the way for innovation in their organizations. What you will learn: What are the various Generative AI technologies - Foundational models, LLM, Auto encoders, GANs, VAEs, RAG, prompt engineering and more.How to build a successful strategy for architecting and implementing Generative AIUnderstand Preprocessing and setting up quality data for training generative AI modelsWhat are the future trends in generative AI to build sustainable and trustworthy solutions Who this book is for: Individuals in leadership positions, such as CEOs, CTOs, CIOs, and other senior executives, who are responsible for driving innovation and strategy. decision-making in their organizations and interested in adopting Gen AI for that purpose. Professionals who design and implement technical solutions and infrastructure using Generative AI and machine learning systems.
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Generative Artificial Intelligence
This book comprehensively covers designing and implementing misinformation intervention strategies and risk communication approaches for generative artificial intelligence (AI) technologies. Key Features: • Showcases cutting‑edge technology and offers insights into one of the most rapidly evolving fields in AI. • Provides a comprehensive understanding of the underlying principles, algorithms, and applications of this cutting‑edge technology, offering insights into both its theoretical foundations and practical implementations. • Discusses generative AI models, unraveling the contrasts between machine learning, deep learning, and generative AI. • Presents AI‑based models for the detection and generation of fraudulent content. • Covers topics such as speech generation, deepfake technology, pose synthesis for animation, and sports performance analysis. This book is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, and information technology.
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Generative AI Career Masterplan
Kick-start your agentic AI career with insights from leaders at Google, Microsoft, and Liquid AI. Learn the skills, strategies, and tools to build intelligent agents, transition faster, and lead in the era of autonomous systems.Key FeaturesDiscover the only comprehensive career roadmap for the Generative AI eraLearn directly from top industry experts, leaders, and AI innovatorsBuild future-proof skills, from prompt engineering to ethical AI strategyPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThis book is a landmark publication, the first-ever collaborative guide authored by a powerhouse lineup of industry leaders from Google, Microsoft, IBM and Liquid AI. This all-in-one resource demystifies the Agentic AI revolution and delivers a practical, career-centered framework for every professional, from graduates to senior executives.You'll start by understanding Gen AI's impact on the job market and decoding core concepts like LLMs, transformers, and prompt engineering. With detailed role descriptions and leveling matrices to identify and build a roadmap tailored to your background.Next, dive into the Gen AI tech stack: RAG architectures, vector databases, function calling, multi-agent systems, and LLMOps. Explore essential frameworks including LangChain, LlamaIndex, Haystack, & Hugging Face, learning when and how to apply each tool effectively. Beyond technical skills, you'll cultivate the human advantages AI cannot replicate, critical thinking, adaptability, creativity, and ethical judgment. Real-world use cases span finance, healthcare, software development, and marketing, demonstrating how Gen AI transforms industries.More than a technical guide, this is a career transformation blueprint. It integrates practical frameworks for skill development, job search strategies, and personal branding in an AI-augmented world.What you will learnUnderstand the global impact of Generative and Agentic AI on jobs and industriesMaster key GenAI & Agentic AI concepts for any professional backgroundIdentify your best-fit AI career path and transferable skillsBuild familiarity with LlamaIndex, Haystack, Hugging Face, and core RAG workflowsBuild an AI-enhanced personal brand using the 5-Layer LinkedIn Strategy and Portfolio Showcase FrameworkApply ethical and responsible AI principles and governance in real-world practiceCreate a lifelong learning and upskilling roadmap for sustained growthWho this book is forThis book is for graduates, professionals, and leaders seeking to future-proof their careers and capitalize on the rise of Generative AI. Whether you’re entering the job market, transitioning from a traditional tech or non-tech role, or aiming to lead AI transformation, this guide provides the clarity, tools, and confidence to take control of your trajectory. It’s an indispensable resource for engineers, data scientists, product managers, business analysts, educators, and executives determined to stay relevant in the new world of
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Designing Instruction with Generative AI
Designing Instruction with Generative AI offers a novel set of tools and strategies for leveraging generative AI to create engaging and personalized learning experiences. While instructional designers are a tremendous asset to higher education, not all colleges or universities have the robust staff needed to support all instructors on staff or large student populations. Drawing on a wealth of research, professional experience, and strategic insights, this book equips new and seasoned teaching faculty and trainers with step-by-step directions on how freely accessible artificial intelligence software can assist with all aspects of the course creation and instruction process and cater to the needs of diverse learners. Each chapter offers forward-thinking and empirically validated ways to help faculty create and improve instructional materials, course design, and learning environments while supporting their digital literacies. Rather than introduce AI as a means of outsourcing subject-area expertise, critical thinking, or cognitive processes, the author instead emphasizes its potential to build on traditionally honed knowledge and foundational instructional design practices. From optimizing course alignment and accessibility practices to fostering active learning, motivation, and engagement, educators will find new solutions to common teaching and learning challenges with greater efficiency in time and capacity.The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives (CC BY-NC-ND) 4.0 license.
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Building Generative AI Services with Fastapi
Ready to build production-grade applications with generative AI? This practical guide takes you through designing and deploying AI services using the FastAPI web framework. Learn how to integrate models that process text, images, audio, and video while seamlessly interacting with databases, filesystems, websites, and APIs. Whether you're a web developer, data scientist, or DevOps engineer, this book equips you with the tools to build scalable, real-time AI applications.Author Alireza Parandeh provides clear explanations and hands-on examples covering authentication, concurrency, caching, and retrieval-augmented generation (RAG) with vector databases. You'll also explore best practices for testing AI outputs, optimizing performance, and securing microservices. With containerized deployment using Docker, you'll be ready to launch AI-powered applications confidently in the cloud.Build generative AI services that interact with databases, filesystems, websites, and APIsManage concurrency in AI workloads and handle long-running tasksStream AI-generated outputs in real time via WebSocket and server-sent eventsSecure services with authentication, content filtering, throttling, and rate limitingOptimize AI performance with caching, batch processing, and fine-tuning techniques
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Generative Artificial Intelligence and Explainable Artificial Intelligence for Real-world Applications
This book integrates the concepts of explainable artificial intelligence, and generative explainable artificial intelligence, showcasing how explainability can be incorporated into generative models. It highlights the importance of making generative models transparent, offering strategies to achieve this through interpretability techniques.Focuses on the principles and methodologies of explainable artificial intelligence, providing readers with a thorough understanding of how artificial intelligence models can be made transparent and interpretable.Explores explainable artificial intelligence techniques, including feature importance, SHAP values, LIME, and counterfactual explanations.Offers an in-depth examination of generative artificial intelligence, covering cutting-edge advancements in generative models like GANs, VAEs, and transformer-based architectures.Bridges the gap between theoretical concepts and their practical applications, making advanced artificial intelligence technologies accessible to the readers.Includes numerous case studies and real-world examples that demonstrate the successful application of generative artificial intelligence, and explainable artificial intelligence.The text is primarily written for graduate students and academic researchers in electrical engineering, electronics, and communication engineering, computer science and engineering, biomedical engineering, and information technology.
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Generative AI for Software Developers
Master Generative AI in software development with hands-on guidance, from coding and debugging to testing and deployment, using GitHub Copilot, Amazon Q Developer, and OpenAI APIs to build scalable, AI-powered applicationsKey FeaturesHands-on guidance for mastering AI-powered coding, debugging, and deployment with real-world examplesComprehensive coverage of GenAI concepts, prompt engineering, fine-tuning, and SDLC integrationPractical strategies for architecting and scaling production-ready AI-driven applicationsBook DescriptionGenerative AI for Software Developers is your practical guide to mastering AI-powered development and staying ahead in a fast-changing industry. Through a structured, hands-on approach, this book helps you understand, implement, and optimize Generative AI in modern software engineering. From AI-assisted coding, debugging, and documentation to testing, deployment, and system design, it equips you with the skills to integrate AI seamlessly into your workflows.You’ll work with tools such as GitHub Copilot, Amazon Q Developer, and OpenAI APIs while learning strategies for prompt engineering, fine-tuning, and building scalable AI-powered applications. Featuring real-world use cases, best practices, and expert insights, this book bridges the gap between experimenting with AI and production deployment.Whether you’re an aspiring AI developer, experienced engineer, or solutions architect, this guide gives you the clarity, confidence, and tactical knowledge to thrive in the GenAI-driven future of software development. Armed with these insights, you’ll be ready to build, integrate, and scale intelligent solutions that enhance every stage of the software development lifecycle.What you will learnBuild a secure GenAI application with expert guidanceUnderstand the fundamentals of GenAI and its applications in software engineeringAutomate coding tasks with tools like GitHub Copilot, Amazon Q Developer, and OpenAI APIsApply AI for debugging, testing, documentation, and deployment workflowsGet to grips with prompt engineering and fine-tuning techniques to optimize AI outputsImplement best practices for architecting and scaling AI-powered applicationsBuild end-to-end GenAI projects, moving from experimentation to productionWho this book is forThis book is for software developers, engineers, architects, and tech professionals who want to understand the core concepts of Generative AI and its real-world applications, master AI-driven development workflows to improve efficiency and code quality, and leverage tools like GitHub Copilot, Amazon Q Developer, and OpenAI APIs to automate coding tasks.
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Generative AI and Generative AI of Things for Sustainable Smart Cities
This pioneering book invites readers on a compelling journey into Generative Artificial Intelligence (AI) and its groundbreaking convergence with AI of Things (AIoT), introducing GAIoT as a transformative frontier in urban computing and intelligence. GAIoT signals a paradigm shift towards more intelligent, self-learning, self-evolving, and context-aware systems—capable of generating adaptive, forward-looking solutions to the complex infrastructuraland environmental challenges confronting sustainable smart cities.With its combination of theoretical depth, applied innovation, and interdisciplinary scope, the book offers a comprehensive examination of deep generative models—namely Generative Adversarial Networks, Variational Autoencoders, Diffusion Models, Transformers, and hybrid architectures—and their applications in environmental sustainability, climate resilience, infrastructure optimization, dynamic decision-making, and data-driven urban management and planning. From synthetic data generation, data augmentation, and data imputation to predictive modeling and scenario simulation, GAIoT is driving the next wave of climate-responsive, environmentally conscious smart city innovation.What sets this book apart is its first-of-its-kind focus on GAIoT as a pathbreaking force in shaping the future of sustainable urban development. It delivers actionable insights, conceptual and operational frameworks, case studies, and policy guidance—equipping diverse stakeholders with the tools to build cities that not only respond to change but also anticipate and shape it. Targeting a broad and cross-disciplinary audience, the book shares state-of-the-art research, presents innovative solutions, and forecasts future trends in urban transformation. As both a seminal reference and a practical resource for researchers, technologists, practitioners, professionals, and policymakers, it provides essential guidance for those engaged in advancing the next frontier in urban computing and intelligence.
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ARTIFICIAL INTELLIGENCE FOR ALL: A HANDBOOK ON PROMPTING AND AGENTS
Artificial Intelligence for All: A Handbook on Prompting and Agents is a comprehensive practical guide designed for the age of Large Language Models (LLMs) and AI Agents. Centered on the core concepts of prompting, AI cognition, and intelligent agent collaboration, the book provides a complete framework that bridges foundational AI understanding with real-world execution.Starting from the fundamentals of AI communication and prompt engineering, the book systematically explores cognitive differences between humans and AI systems, advanced prompt structures, AI workflow design, and industry-specific AI applications. It further expands into AI Agent orchestration, multi-modal systems, local deployment, privacy protection, and AI governance.Unlike conventional introductory AI books that focus only on concepts or tools, this handbook emphasizes practical implementation through reusable frameworks, real-world case studies, structured prompt methodologies, and deployment strategies. Readers will learn how to transform AI from a passive chatbot into an active collaborative assistant capable of planning, reasoning, automation, and decision support.With applications across education, healthcare, finance, marketing, and enterprise productivity, this book is suitable for general readers, professionals, universities, corporate training programs, and organizations seeking AI transformation.
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System Design with AI Interview Guide
System Design with AI Interview Guide equips you with the insights, skills, and hands-on practice needed to ace even the toughest system design interview questions. This book takes a structured approach to mastering system architecture, blending AI-driven decision-making with modern design principles to help you confidently tackle complex interview scenarios.Divided into three parts, the book begins by establishing a strong foundation in system design methodology, emphasizing scalability, performance optimization, and AI integration. You'll learn how to analyze design challenges, apply domain-driven strategies, and incorporate AI agents to enhance system intelligence. The second section delves into technical fundamentals, covering data modeling, API design, microservices, high availability, and security. It provides essential strategies for scaling AI workloads, optimizing cloud storage, and building resilient, fault-tolerant systems. The final section brings real-world case studies into focus, showcasing AI-driven solutions for ride-sharing platforms, e-commerce applications, fraud detection, and payment processing. These hands-on examples reinforce key principles, helping you apply your knowledge to real interview questions.By the end of this book, you'll have a deep understanding of AI-powered system design and the confidence to structure, articulate, and defend your solutions in interviews. Whether you're an aspiring software architect, an experienced developer preparing for top-tier interviews, or a professional looking to sharpen your design expertise, this guide provides the practical insights and hands-on experience needed to succeed. What you will learn:Learn a structured, step-by-step approach to designing scalable, efficient, and AI-powered systems for interviews.How to develop the skills to align system design with business objectives, user needs, and domain-specific constraints.Explore designing fault-tolerant, distributed systems with AI-enhanced security and performance optimization.Understand the future of AI-driven system design, ethical considerations, and best practices for staying competitive in interviews.Who this book is for:Software developers and engineers preparing for system design interviews. Software architects seeking a structured approach to designing scalable, resilient applications with AI considerations.
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Integrating Generative AI in Education to Achieve Sustainable Development Goals
A new challenge has become present in the field of generative artificial intelligence (AI). The fundamental nature of education, a vital element for advancing the United Nations' Sustainable Development Goals (SDGs), now grapples with the transformative impact of AI technologies. As we stand at this intersection of progress and pedagogy, critical questions surface about the future roles of educators and the integrity of assessment processes. AI's rapid progression prompts an exploration of the competencies our education systems must cultivate in a world where human and machine intelligence are becoming increasingly interconnected. Against this backdrop of transformative uncertainty, Integrating Generative AI in Education to Achieve Sustainable Development Goals addresses profound challenges and offers promising solutions at the crossroads of AI and education. This book assembles distinguished academics, researchers, and practitioners, forming a collective voice on the intersection of Generative AI and education. The three-part structure dissects the technical aspects of AI-powered innovations in educational design, explores multidisciplinary applications enhancing educational content, and highlights AI-driven solutions to address equality and inclusion concerns within educational systems. The book also underscores the importance of ethical considerations of generative AI to ensure a future where technology serves the broader goals of sustainability and equitable education. This book's primary objective is to serve as a resource for academicians, independent researchers, research scholars, students, scientists, libraries, and industry experts. Through a diverse range of topics, it investigates how generative AI can advance Sustainable Development Goals, making education more accessible, individualized, and effective. By harnessing the power of AI, the book argues, education can act as a catalyst for positive change, empowering individuals and fostering progress toward a sustainable and equitable future. The recommended topics within each part provide a structured framework for deepening the understanding of Generative AI's role in shaping the future of education, making this book a valuable reference for scholars and practitioners alike.
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Generative AI with LangChain
Get to grips with the LangChain framework to develop production-ready applications, including agents and personal assistants, integrating with web searches, and code execution. Purchase of the print or Kindle book includes a free PDF eBook.Key FeaturesLearn how to leverage LLMs’ capabilities and work around their inherent weaknessesDelve into the realm of LLMs with LangChain and go on an in-depth exploration of their fundamentals, ethical dimensions, and application challengesGet better at using ChatGPT and GPT models, from heuristics and training to scalable deployment, empowering you to transform ideas into realityBook DescriptionChatGPT and the GPT models by OpenAI have brought about a revolution not only in how we write and research but also in how we can process information. This book discusses the functioning, capabilities, and limitations of LLMs underlying chat systems, including ChatGPT and Bard. It also demonstrates, in a series of practical examples, how to use the LangChain framework to build production-ready and responsive LLM applications for tasks ranging from customer support to software development assistance and data analysis – illustrating the expansive utility of LLMs in real-world applications.Unlock the full potential of LLMs within your projects as you navigate through guidance on fine-tuning, prompt engineering, and best practices for deployment and monitoring in production environments. Whether you're building creative writing tools, developing sophisticated chatbots, or crafting cutting-edge software development aids, this book will be your roadmap to mastering the transformative power of generative AI with confidence and creativity.What you will learnUnderstand LLMs, their strengths and limitationsGrasp generative AI fundamentals and industry trendsCreate LLM apps with LangChain like question-answering systems and chatbotsUnderstand transformer models and attention mechanismsAutomate data analysis and visualization using pandas and PythonGrasp prompt engineering to improve performanceFine-tune LLMs and get to know the tools to unleash their powerDeploy LLMs as a service with LangChain and apply evaluation strategiesPrivately interact with documents using open-source LLMs to prevent data leaksWho this book is forThe book is for developers, researchers, and anyone interested in learning more about LLMs. Whether you are a beginner or an experienced developer, this book will serve as a valuable resource if you want to get the most out of LLMs and are looking to stay ahead of the curve in the LLMs and LangChain arena.Basic knowledge of Python is a prerequisite, while some prior exposure to machine learning will help you follow along more easily.
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Exploring Generative AI with Computational Intelligence
This book demystifies the concept of AI-driven creativity with an in-depth understanding of the underlying principles, algorithms, and applications of generative AI. It begins with a coverage of AI fundamentals, followed by a technical discussion including detailed explanations of model architectures, training processes, and optimisation techniques. It explores case studies showcasing how generative AI is reshaping industries such as entertainment, marketing, design, and healthcare. The book also tackles ethical considerations, addressing issues related to bias, transparency, and responsible AI development.Features:Provides deep understanding of generative AI, including in-depth coverage of various generative models such as GANs, VAEs, and RNNs.Includes practical, real-world examples of how generative AI is applied in different industries.Dedicates significant attention to ethical considerations.Equips readers with the knowledge and tools to build, train, and deploy their own generative AI systems.Offers thorough explanations of AI algorithms, neural network architectures, and training processes.This book is aimed at graduate students and researchers in computer engineering, AI and ML.
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Generative Systems and Intelligent Tutoring Systems
This book constitutes the refereed proceedings of the 21st International Conference on Intelligent Tutoring Systems, ITS 2025, held in Alexandroupolis, Greece, during June 2-6, 2025. The 21 full papers, 27 short papers and 5 posters included in this book were carefully reviewed and selected from 67 submissions. The papers are organized in the following topical conference tracks: Part I: Generative Tutoring Systems. The goal of this part is to show how new techniques inspired by artificial intelligence (AI) and new methods in education can improve learning, teaching, and generate the capacity for knowledge acquisition and much more. Part II: Application areas, environments, and techniques for AI systems. This part shows the progress of research investigating the different application areas (such as education, health), techniques (such as neural networks, data mining, natural language processing) and environments (such as games, virtual reality, cognitive robots) for effective AI systems.
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Generative AI on Kubernetes
Generative AI is revolutionizing industries, and Kubernetes has fast become the backbone for deploying and managing these resource-intensive workloads. This book serves as a practical, hands-on guide for MLOps engineers, software developers, Kubernetes administrators, and AI professionals ready to unlock AI innovation with the power of cloud native infrastructure. Authors Roland Huß and Daniele Zonca provide a clear road map for training, fine-tuning, deploying, and scaling GenAI models on Kubernetes, addressing challenges like resource optimization, automation, and security along the way.With actionable insights with real-world examples, readers will learn to tackle the opportunities and complexities of managing GenAI applications in production environments. Whether you're experimenting with large-scale language models or facing the nuances of AI deployment at scale, you'll uncover expertise you need to operationalize this exciting technology effectively.Learn to run GenAI models on Kubernetes for efficient scalabilityGet techniques to train and fine-tune LLMs within Kubernetes environmentsSee how to deploy production-ready AI systems with automation and resource optimizationDiscover how to monitor and scale GenAI applications to handle real-world demandUncover the best tools to operationalize your GenAI workloadsLearn how to run agent-based and AI-driven applications
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Modular Design Automation of Intelligent Robot Systems
Comprehensive guide to streamlining the design optimization process of intelligent robots using MODENA (MOdular DEsigN Automation) Modular Design Automation of Intelligent Robot Systems introduces MODENA (MOdular DEsigN Automation) as a new approach to the design of intelligent robots by harnessing computational intelligence techniques like genetic programming and constrained multi-objective evolutionary algorithms. It also covers different aspects of robot design, including physical structure, control, and vision systems. Case studies are included throughout the text to aid in the practical application of concepts. The book also provides information on: Methods in the design automation of electronic systems, micro-electro-mechanical systems (MEMS), complex mechatronic systems, and intelligent robotic systemsMODENA’s advantage in avoiding the frequent trial-and-error processes inherent in traditional design methods, instead promoting the automatic discovery of innovative designsMODENA as a more interpretable alternative to supplement ChatGPT and other generative AI based on LLMsAutomated design of intelligent robotic systems like teaching manipulators, mechanical printers, and quarter-car suspensions as well as swarm robotic systems Modular Design Automation of Intelligent Robot Systems is an excellent reference on the subject for professionals, researchers, and students in robotics, automation, and artificial intelligence.
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Responsible Implementations of Generative AI for Multidisciplinary Use
Generative artificial intelligence (GAI) represents a profound leap in technological advancement, empowering machines to create content that closely mimics human creativity in various forms. As this technology continues to evolve and permeate multiple industries, it is essential to address the accompanying ethical considerations that arise from its use. Furthermore, there is a need for transparency in how GAI systems are developed and deployed to ensure that they are used responsibly and that their outputs are reliable and fair. Balancing innovation with ethical practices will be crucial to harnessing the benefits of GAI while mitigating its risks and ensuring its positive contribution to society. Responsible Implementations of Generative AI for Multidisciplinary Use highlights both the immense potential of GAI and the ethical challenges it presents. This book demystifies GAI by breaking down complex concepts into accessible language and offering real-world examples that illustrate the implications of its applications. Covering topics such as chatbots, ethical leadership, and the metaverse, this book is an excellent resource for technology professionals and developers, ethicists, policymakers, academicians, researchers, business leaders and executives, legal experts, students, educators, and more.
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R 519.00
Generative Systems and Intelligent Tutoring Systems
This book constitutes the refereed proceedings of the 21st International Conference on Intelligent Tutoring Systems, ITS 2025, held in Alexandroupolis, Greece, during June 2-6, 2025. The 21 full papers, 27 short papers and 5 posters included in this book were carefully reviewed and selected from 67 submissions. The papers are organized in the following topical conference tracks: Part I: Generative Tutoring Systems. The goal of this part is to show how new techniques inspired by artificial intelligence (AI) and new methods in education can improve learning, teaching, and generate the capacity for knowledge acquisition and much more. Part II: Application areas, environments, and techniques for AI systems. This part shows the progress of research investigating the different application areas (such as education, health), techniques (such as neural networks, data mining, natural language processing) and environments (such as games, virtual reality, cognitive robots) for effective AI systems.
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Genai on Google Cloud
In today's AI landscape, success depends not just on using large language models but on moving them into production in a way that's scalable, compliant, and cost-effective. GenAI on Google Cloud: Enterprise Generative AI Systems and Agents is your hands-on guide to bridging that gap. Whether you're an ML engineer tackling your first multi-agent application or a product leader responsible for scaling a GenAI strategy, this book offers a practical game plan for taking your LLM applications from promising prototype to enterprise-grade solution. Written by practitioners with deep experience in MLOps, data engineering, and GenAI infrastructure, this guide takes you through real-world workflows using Google Cloud's Vertex AI--from training and deployment to monitoring and integration. With concrete examples, field-tested frameworks, and honest insights about what actually works, you'll learn how to build production systems that deliver measurable business value. Bridge the prototype-to-production chasm that stalls 75% of LLM projects using systematic frameworks developed across hundreds of deploymentsNavigate the unique complexities of LLM operations through practical guidance on data orchestration, evaluation frameworks, and responsible AI practicesBuild enterprise-grade multimodal systems that handle text, images, and video with robust agent architectures and tool integrationImplement proven optimization strategies for cost management, performance tuning, and production monitoring at scaleApply battle-tested patterns from practitioners who've guided organizations through both breakthrough successes and costly failures
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Generative AI and Generative AI of Things for Sustainable Smart Cities
This pioneering book invites readers on a compelling journey into Generative Artificial Intelligence (AI) and its groundbreaking convergence with AI of Things (AIoT), introducing GAIoT as a transformative frontier in urban computing and intelligence. GAIoT signals a paradigm shift towards more intelligent, self-learning, self-evolving, and context-aware systems—capable of generating adaptive, forward-looking solutions to the complex infrastructuraland environmental challenges confronting sustainable smart cities.With its combination of theoretical depth, applied innovation, and interdisciplinary scope, the book offers a comprehensive examination of deep generative models—namely Generative Adversarial Networks, Variational Autoencoders, Diffusion Models, Transformers, and hybrid architectures—and their applications in environmental sustainability, climate resilience, infrastructure optimization, dynamic decision-making, and data-driven urban management and planning. From synthetic data generation, data augmentation, and data imputation to predictive modeling and scenario simulation, GAIoT is driving the next wave of climate-responsive, environmentally conscious smart city innovation.What sets this book apart is its first-of-its-kind focus on GAIoT as a pathbreaking force in shaping the future of sustainable urban development. It delivers actionable insights, conceptual and operational frameworks, case studies, and policy guidance—equipping diverse stakeholders with the tools to build cities that not only respond to change but also anticipate and shape it. Targeting a broad and cross-disciplinary audience, the book shares state-of-the-art research, presents innovative solutions, and forecasts future trends in urban transformation. As both a seminal reference and a practical resource for researchers, technologists, practitioners, professionals, and policymakers, it provides essential guidance for those engaged in advancing the next frontier in urban computing and intelligence.
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R 2,595.00
Generative AI
From algorithms that draft clinical notes in seconds to autonomous agents that triage emergency room backlogs, artificial intelligence is quietly reshaping every facet of healthcare. Generative AI: Unlocking the Next Chapter in Healthcare is the first comprehensive guide to this revolution—charting how we got here, what’s working now, and where the technology could take us next.Drawing on frontline case studies from leading health systems, biotech labs, and tech giants, the book reveals how large language models, synthetic data engines, and multi agent ecosystems are boosting diagnostic accuracy, accelerating drug discovery, and personalizing patient engagement. It also confronts the hard questions—privacy, bias, liability, and the fear of “machines taking over”—offering clear, actionable frameworks for ethical, human centered deployment.Written for clinicians, executives, innovators, and policy makers and patients alike, this definitive volume distils the expertise of industry pioneers into an accessible roadmap. You’ll explore:• The evolution from early machine learning to today’s generative powerhouses• Real world wins—ambient documentation, AI assisted radiology, virtual nursing, and more• The players shaping the landscape: Epic, Google, Microsoft, NVIDIA, Salesforce, and a wave of insurgent startups• Guardrails that preserve trust, protect privacy, and keep humans in the loop• Bold yet plausible futures—from digital twin medicine to globe-spanning agent networksWhether you’re leading a hospital, building health tech solutions, a tech startup, or simply curious about the future of care, Generative AI: Unlocking the Next Chapter in Healthcare delivers the insight, inspiration, and practical guidance you need to thrive in the era where human empathy and machine intelligence work side by side.
R 1,955.66
Copyright Law and Generative AI
Is it lawful for generative AI to draw on protected works without permission? The question sits at the heart of today’s most urgent debates on AI, and the answer is anything but simple. This book takes a comparative and interdisciplinary approach to unpack how copyright exceptions apply—or fail to apply—in the age of generative AI. It examines how major jurisdictions, including the United States, China, the European Union, Japan, the United Kingdom, and Singapore, are confronting these challenges.The book begins by explaining the basic principles of copyright law and why they matter for both AI training and AI-generated content. It then surveys the key copyright exceptions and licensing routes currently available, highlighting how legal uncertainty has complicated efforts to regulate the way generative AI systems access and use protected material. To make sense of this uncertainty, the book looks beyond the law and examines the broader forces shaping the debate: emerging norms around AI ethics and copyright, the evolving business models built on generative AI, concerns about the market impact of AI-generated products, and the technical realities of generative models, from the black-box problem to the structure of latent space. These dynamics show why existing copyright exceptions, designed for an earlier technological era, often struggle to keep pace with generative AI. The book concludes with a framework for rethinking copyright exceptions that integrates legal, economic, normative, and technological considerations.By bringing together insights across jurisdictions and disciplines, this book offers a clear and timely account of how legal systems are trying to adapt copyright rules to the rise of generative AI without stifling innovation. It will appeal to scholars of intellectual property and artificial intelligence, as well as policymakers, regulators, and AI developers navigating this fast-changing landscape.
R 8,572.00
R 764.00
GenAI Security
This book is an essential guide for anyone looking to build secure and resilient generative AI applications. It provides actionable insights into identifying vulnerabilities and preventing attacks.Authored by security expert Paul Zenker, the book explores the critical aspects of securing GenAI systems across their lifecycle - design, development, and operation. Through numerous practical examples, detailed illustrations of threat scenarios, and application architectures, readers will gain a comprehensive understanding of potential risks. Step-by-step instructions cover techniques such as prompt injections, jailbreaks, and other attack vectors, equipping readers with the skills to anticipate and mitigate threats effectively.This resource is ideal for developers, security professionals, AI researchers, and organizations working with generative AI technologies. This book offers the tools and knowledge needed to create systems that can withstand sophisticated attacks in today’s high-stakes digital landscape.
R 6,118.00
