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Platform and Model Design for Responsible AI
AI algorithms are ubiquitous and used for tasks, from recruiting to deciding who will get a loan. With such widespread use of AI in the decision-making process, it’s necessary to build an explainable, responsible, transparent, and trustworthy AI-enabled system. With Platform and Model Design for Responsible AI, you’ll be able to make existing black box models transparent.You’ll be able to identify and eliminate bias in your models, deal with uncertainty arising from both data and model limitations, and provide a responsible AI solution. You’ll start by designing ethical models for traditional and deep learning ML models, as well as deploying them in a sustainable production setup. After that, you’ll learn how to set up data pipelines, validate datasets, and set up component microservices in a secure and private way in any cloud-agnostic framework. You’ll then build a fair and private ML model with proper constraints, tune the hyperparameters, and evaluate the model metrics.By the end of this book, you’ll know the best practices to comply with data privacy and ethics laws, in addition to the techniques needed for data anonymization. You’ll be able to develop models with explainability, store them in feature stores, and handle uncertainty in model predictions.Key FeaturesLearn risk assessment for machine learning frameworks in a global landscapeDiscover patterns for next-generation AI ecosystems for successful product designMake explainable predictions for privacy and fairness-enabled ML trainingWhat you will learnUnderstand the threats and risks involved in ML modelsDiscover varying levels of risk mitigation strategies and risk tiering toolsApply traditional and deep learning optimization techniques efficientlyBuild auditable and interpretable ML models and feature storesUnderstand the concept of uncertainty and explore model explainability toolsDevelop models for different clouds including AWS, Azure, and GCPExplore ML orchestration tools such as Kubeflow and Vertex AIIncorporate privacy and fairness in ML models from design to deploymentWho this book is forThis book is for experienced machine learning professionals looking to understand the risks and leakages of ML models and frameworks, and learn to develop and use reusable components to reduce effort and cost in setting up and maintaining the AI ecosystem.
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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.
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System-2 Reasoning: from Semantic Anchoring to Causal Intelligence, Volume 2
Large language models can write poetry, pass bar exams, and generate fluent code. Yet they continue to fail in the domains where intelligence must be accountable: distinguishing causation from correlation, recognizing when evidence is insufficient, preserving commitments over time,and correcting their own reasoning failures. This book argues that the transition from pattern matching to genuine reasoning requires a System-2 layer grounded in coordinated diagnosis, audit, causal validation, memory, and meta-cognitive control. The volume develops this architecture from first principles through operational protocols. Semantic Anchoring (UCCT) demonstrates how contextual constraints can bind latent model representations into governed reasoning processes rather than prior-driven completion. Regulated Causal Anchoring (RCA) and RAudit diagnose sycophancy, pathological skepticism, and trace-output inconsistency without relying exclusively on ground-truth supervision. The Causal Abstraction Bridge and the CausalTSK benchmark reveal where models collapse from interventional and counterfactual reasoning back into associative prediction. Epistemic Regret Minimization (ERM) identifies causal shortcuts and failures of warranted inference, while Reinforcement Learning from Epistemic Regret (RLER) transforms those reasoning failures into a structured learning signal. Trivium introduces temporal accountability through a Causal Transaction Log, and Quadrivium integrates contextual, causal, temporal, and meta-cognitive regulation into a unified System-2 MACI architecture. The author's central thesis is that Artificial General Intelligence (AGI) will not emerge from scaling monolithic pattern-completion systems alone. AGI will require architectures capable of explaining why an answer is warranted, refusing conclusions when evidence remains indeterminate, and improving through epistemic failure. Written for researchers, advanced students, and practitioners, this book presents a framework for AI systems that are not merely impressive, but auditable, corrigible, and trustworthy. It is suitable for graduate-level courses in artificial intelligence, multi-agent systems, causal reasoning, and trustworthy AI.
R 2,929.00
Architecting a Framework for Edge AI Functional and Non-functional Requirements
This book presents a comprehensive exploration of the functional and non-functional requirements that define edge AI systems, including technical, ethical, legal, and regulatory dimensions. It offers a holistic perspective that spans hardware, software, the AI technology stack, and the data pipelines supporting applications across the micro-, deep-, and meta-edge continuum. Edge AI systems are evaluated through their key properties—functionality, performance, cost, dependability, and trustworthiness. The book closely interlinks these requirements with the concepts of system dependability and trust. Dependability is presented as the backbone of real-time edge AI performance, where services must be delivered reliably within strict timeframes. Trustworthiness is defined as the system’s ability to meet both functional and non-functional requirements in a verifiable manner—ensuring transparency, correctness, and alignment with human oversight. The chapters emphasize how building trust in edge AI is not merely a technical task, but a collaborative process across technical, ethical, and legal/regulatory domains. Establishing trustworthiness requires the careful definition of requirements, measurement of key performance indicators (KPIs), continuous monitoring, transparent processes, and alignment with broader societal values. Written for researchers, engineers, and students eager to understand the next frontier of edge intelligence, the book invites readers to engage with cutting-edge discussions on performance, accountability, and responsibility in AI at the edge. It is both an academic resource and a practical guide for those seeking to design, validate, and deploy edge AI systems that are not only high-performing but also dependable, trustworthy, and socially aligned.
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Generative AI for Cloud Solutions
Explore Generative AI, the engine behind ChatGPT, and delve into topics like LLM-infused frameworks, autonomous agents, and responsible innovation, to gain valuable insights into the future of AI Key FeaturesGain foundational GenAI knowledge and understand how to scale GenAI/ChatGPT in the cloudUnderstand advanced techniques for customizing LLMs for organizations via fine-tuning, prompt engineering, and responsible AIPeek into the future to explore emerging trends like multimodal AI and autonomous agentsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionGenerative artificial intelligence technologies and services, including ChatGPT, are transforming our work, life, and communication landscapes. To thrive in this new era, harnessing the full potential of these technologies is crucial. Generative AI for Cloud Solutions is a comprehensive guide to understanding and using Generative AI within cloud platforms.This book covers the basics of cloud computing and Generative AI/ChatGPT, addressing scaling strategies and security concerns. With its help, you’ll be able to apply responsible AI practices and other methods such as fine-tuning, RAG, autonomous agents, LLMOps, and Assistants APIs. As you progress, you’ll learn how to design and implement secure and scalable ChatGPT solutions on the cloud, while also gaining insights into the foundations of building conversational AI, such as chatbots. This process will help you customize your AI applications to suit your specific requirements.By the end of this book, you’ll have gained a solid understanding of the capabilities of Generative AI and cloud computing, empowering you to develop efficient and ethical AI solutions for a variety of applications and services.What you will learnGet started with the essentials of generative AI, LLMs, and ChatGPT, and understand how they function togetherUnderstand how we started applying NLP to concepts like transformersGrasp the process of fine-tuning and developing apps based on RAGExplore effective prompt engineering strategiesAcquire insights into the app development frameworks and lifecycles of LLMs, including important aspects of LLMOps, autonomous agents, and Assistants APIsDiscover how to scale and secure GenAI systems, while understanding the principles of responsible AIWho this book is forThis artificial intelligence book is for aspiring cloud architects, data analysts, cloud developers, data scientists, AI researchers, technical business leaders, and technology evangelists looking to understanding the interplay between GenAI and cloud computing. Some chapters provide a broad overview of GenAI, which are suitable for readers with basic to no prior AI experience, aspiring to harness AI's potential. Other chapters delve into technical concepts that require intermediate data and AI skills. A basic understanding of a cloud ecosystem is required to get the most out of this book.
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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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Hybrid Mind
The Hybrid Mind: The Human-AI Convergence explores the transformative potential of humans and AI systems collaborating to revolutionize risk management, oversight, and adaptive governance. Introducing Agentic AI—systems capable of autonomous decision-making while preserving human authority—the book outlines a hybrid inference architecture that combines Large and Small Language Models, machine learning, and probabilistic reasoning.Structured around the OODAF (Observe, Orient, Decide, Act, Feedback) loop, the book offers accessible explanations of intelligent system behavior and its implications. Use cases in finance, healthcare, cybersecurity, and SaaS environments demonstrate how hybrid intelligence enables scalable yet accountable operations, providing practical insights for the AI era.It introduces original frameworks, including Human ETHOS, the Experimentum Crucis Technology Matrix (ECTM), and the Hybrid Audit Model (HAM), that reimagine ethical design, system evaluation, and real-time auditability. Chapters explore topics such as feedback architecture, ethical interruption, decision traceability, and context framing, critical elements for governing AI with integrity.Designed for business leaders, technologists, and policymakers, The Hybrid Mind serves as both a strategic guide and a moral blueprint for the AI era. It equips readers to lead in a world where intelligence is shared, automation is consequential, and responsibility must be architected, not assumed.
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Data-Driven Decision Support System in Intelligent HealthCare
Machine Intelligence with Generative AI is one of the most trending topics with applications in almost all fields of life. In healthcare, it is not only accelerating the development of new products, but also automating the generation of new and synthetic content making it easier to train and improve machine learning models.Some of the biggest achievements of Generative AI in healthcare have been drug discovery, personalized care, differentially private synthetic data generation, operational efficiency, and many more. Generative AI models like Generative Adversarial Networks, and Variational Autoencoders are employed to generate synthetic medical images, aiding in data augmentation, facilitating disease diagnosis, and enabling advanced medical imaging research. Additionally, Generative AI techniques are being utilized for creating realistic electronic health records (EHRs) and simulated patient data, supporting privacy-preserving data sharing, and empowering innovative studies for personalized medicine and drug development. NLP models like ClinicalBERT use transformer-based deep learning architecture to understand and represent contextual information in large clinical text datasets, such as electronic health records (EHRs) and medical literature, and can better grasp medical terminologies, domain-specific language, and contextual nuances that are unique to the healthcare field.This volume delves into the realm of Machine Intelligence with Generative AI and explores its impact on the healthcare industry.
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DeepSeek in Action
From fundamental concepts to advanced implementations, this book thoroughly explores the DeepSeek-V3 model, focusing on its Transformer-based architecture, technological innovations, and applications.The book begins with a thorough examination of theoretical foundations, including self-attention, positional encoding, the Mixture of Experts mechanism, and distributed training strategies. It then explores DeepSeek-V3’s technical advancements, including sparse attention mechanisms, FP8 mixed-precision training, and hierarchical load balancing, which optimize memory and energy efficiency. Through case studies and API integration techniques, the model's high-performance capabilities in text generation, mathematical reasoning, and code completion are examined. The book highlights DeepSeek’s open platform and covers secure API authentication, concurrency strategies, and real-time data processing for scalable AI applications. Additionally, the book addresses industry applications, such as chat client development, utilizing DeepSeek’s context caching and callback functions for automation and predictive maintenance.This book is aimed primarily at AI researchers and developers working on large-scale AI models. It is an invaluable resource for professionals seeking to understand the theoretical underpinnings and practical implementation of advanced AI systems, particularly those interested in efficient, scalable applications.
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Physics-Generated AIs of Robust Nonlinear Filter and Control Designs for Complicated Man-Made Machines
This book introduces a robust H∞ physics-generated AI-driven filter and controller, along with a nonlinear Luenberger observer model and a state estimation error dynamic model, to effectively address HJIEs for robust H∞ state estimation (filtering) and reference trajectory tracking control in nonlinear stochastic systems. Additionally, it presents a method for training deep neural networks (DNNs) using these models, alongside a physics-generated AI-driven observer-based reference tracking control scheme, with applications in the guidance and control of relevant systems.Key features: Provides theoretical analysis and detailed design procedure for physics-generated AI-driven H∞ or mixed H2/H∞ filter Applies physics-generated AI-driven robust H∞ or mixed H2/H∞ filter and reference tracking control schemes to the trajectory estimation and reference tracking control of man-made machines Introduces physics-generated AI-driven decentralized H∞ observer-based team formation tracking control of large-scale quadrotor UAVs, biped robots or LEO satellites Promulgates the idea of the forthcoming age of physics-generated AI in robot Describes robust physics-generated AI-driven filter and control schemes for complex man-made machinesThis book is aimed at graduate students and researchers in control science, signal processing and artificial intelligence.
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ARCHITECTING HUMAN-CENTRIC AI SYSTEMS: AUGMENTED INTELLIGENCE
This comprehensive book provides the practical AI-enabled human-centric adaptive intelligent enterprise architecture (AIEA) framework for architecting human-centric AI systems for scalable augmented intelligence. The AIEA framework includes several contemporary concepts and practices such as trimodal thinking, human-centric intelligence design, ArcOps and cognition architecture.This volume also unpacks the critical cross-layer domains of human-AI integration, trustworthiness, resiliency and observability. It discusses the topics of digital twin, intelligence design canvas, intelligence design reference model, and intelligence design technology reference model.The useful reference text benefits professionals, academics, researchers, and postgraduate students in the fields of AI/ML, software engineering and databases.
R 3,590.00
System Design for the LLM Era
STOP building fragile AI wrappers; START designing resilient AI systems.Key FeaturesFrom LLM fundamentals to real-world practicalitiesPatterns and principles for architecting LLM-based systemsLearn from in-depth case studiesDecouple from premium models using tiered fallbackEvent-driven architectures for decoupling high-latency agentic workflowsCost management approachesSecurity strategies for LLM systemsGlossary of LLM and AI systems design terminology includedBook DescriptionMany companies are trying to turn their small AI experiments into big products, but they lack a good plan.Engineers need a practical guide to building these new AI systems the right way, so that they can handle scale, won't cost too much to build or operate, and perform reliably. This book is that guide, combining technical depth with breadth and practicality. Starting from LLM fundamentals, the book details the architectural patterns and design principles needed to build production-grade AI systems. In-depth case studies then show you how to apply them to a range of real-world application scenarios, including AI-native IDEs, adaptive learning platforms, and intelligent search solutions.The book provides a deep, practical look at the real-world challenges and solutions for building systems with LLMs at their core.What you will learnArchitect a complete, production-grade AI-powered system from scratchDesign and mitigate the unique challenges of LLM APIs, like high latency and costImplement key software engineering patterns like circuit breakers and rate limiting for AI systemsChoose the right databases and data models for AI applications, including vector search enginesBuild a scalable and resilient system that can handle high load and ensure user privacyWho this book is forThis book will be an invaluable learning resource for engineers, architects and leads working with LLMs or looking to integrate LLMs into their existing systems.
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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.
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Smarter Healthcare through Generative Intelligence
Generative artificial intelligence (AI) has potential to improve healthcare because it can enable smarter, more efficient solutions across diagnostics, treatment, and patient care. By analyzing vast amounts of medical data, generative AI systems can identify patterns, predict disease progression, and assist in early diagnosis with remarkable accuracy. It enhances personalized medicine by generating tailored treatment plans based on individual patient profiles, including genetic, lifestyle, and clinical data.Smarter Healthcare through Generative Intelligence examines the convergence of healthcare and intelligent computation and how it is reshaping the global healthcare landscape. Focusing on applications and management issues, it looks at generative models, privacy-aware distributed computing, smart sensing technologies, and policy frameworks. From data augmentation to drug discovery and from mental health assessment to intelligent patientcommunication, the book explores the ways generative AI can transform healthcare.Highlights include:· Fog computing for smart healthcare· Leveraging generative intelligence to overcome dataset limitations in emotional well-being analysis· Ethical considerations in AI-driven smart healthcare· Designing intelligent chatbots for healthcareGenerative AI is transforming healthcare into a more proactive, precise, and patient-centered industry. Written for healthcare practitioners, researchers, policymakers, and technologists, this book is a guide on how they can design,adopt, and govern generative technologies for smarter healthcare.
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Enterprise Guide for Implementing Generative AI and Agentic AI
Generative AI and Agentic AI together are revolutionizing the technology landscape, with profound and far-reaching impacts across industries. Organizations are increasingly adopting these technologies to drive innovation, enhance unstructured content management, and improve problem-solving capabilities. With Agentic AI, enterprises are moving towards the development of intelligent systems that can plan, reason, and act with autonomy. While early proof-of-concepts (POCs) demonstrated the potential of these technologies, the current shift is toward responsible and scalable production implementations that leverage both generative and agentic capabilities.This book begins by guiding you through the technological evolution of AI, from early machine learning to today's large language models (LLMs) and agentic systems. It then explores a wide range of use cases across industries, highlighting how LLMs can support decision-making, and how Agentic AI enables dynamic, collaborative systems that act with autonomy and intent. This is followed by Design Patterns across the lifecycle of AI solution development, deployment and monitoring. Readers will then gain insights into the methodologies for developing and deploying Generative and Agentic AI solutions at an enterprise level. A featured implementation demonstrates how Agentic AI can be effectively put into action.The book also introduces essential concepts such as MLOps, LLMOps, and Responsible AI principles which are critical for transitioning the AI solutions from experimentation to production. These principles ensure that AI deployments are scalable, secure, ethical and compliant. The book concludes with key takeaways and best practices for developing, evaluating, deploying and scaling AI applications responsibly and effectively within enterprise settings. You Will: Understand key design patterns to develop, deploy and monitor a Generative AI solution effectively.Learn how to develop and implement a production-ready Agentic AI use case.Discover best practices for building scalable, secure, and enterprise-grade AI solutions.Understand how to assess and mitigate risks using Responsible AI principles and LLMOps best practices. This book is for: Enterprise Software Engineers and Architects
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Building Trust in the Generative Artificial Intelligence Era
In an era where generative artificial intelligence (AI) is reshaping industries and daily life, trust has become a cornerstone for its successful adoption and application. Building Trust in the Generative Artificial Intelligence Era: Technology Challenges and Innovations explores how trust can be built, maintained, and evaluated in a world increasingly reliant on AI technologies. Designed to be accessible to a broad audience, thi book blends theoretical insights with practical approaches, offering readers a comprehensive understanding of the topic.This book is divided into three parts. The first part examines the foundations of trust in generative AI, highlighting trends and ethical challenges such as "greenwashing" and remote work dynamics. The second part provides actionable frameworks and tools for assessing and enhancing trust, focusing on topics like cybersecurity, transparency, and explainability. The final section presents global case studies exploring university students' perceptions of ChatGPT, generative AI's applications in European agriculture, and its transformative impact on financial systems.By addressing both the opportunities and risks of generative AI, this book delivers groundbreaking insights for academics, professionals, and policymakers worldwide. It emphasizes practical solutions, ensuring readers gain the knowledge needed to navigate the evolving technological landscape and foster trust in transformative AI systems.
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AI-Enabled Electronic Circuit and System Design
As our world becomes increasingly digital, electronics underpin nearly every industry. Understanding how AI enhances this foundational technology can unlock innovations, from smarter homes to more powerful gadgets, offering vast opportunities for businesses and consumers alike. This book demystifies how AI streamlines the creation of electronic systems, making them smarter and more efficient. With AI’s transformative impact on various engineering fields, this resource provides an up-to-date exploration of these advancements, authored by experts actively engaged in this dynamic field. Stay ahead in the rapidly evolving landscape of AI in engineering with “AI-Enabled Electronic Circuit and System Design: From Ideation to Utilization,” your essential guide to the future of electronic systems. A transformative guide describing how revolutionizes electronic design through AI integration. Highlighting trends, challenges and opportunities;Demystifies complex AI applications in electronic design for practical use;Leading insights, authored by top experts actively engaged in the field;Offers a current, relevant exploration of significant topics in AI’s role in electronic circuit and system design.Editor’s bios. Dr. Ali A. Iranmanesh is the founder and CEO of Silicon Valley Polytechnic Institute. He has received his Bachelor of Science in Electrical Engineering from Sharif University of Technology (SUT), Tehran, Iran, and both his master’s and Ph.D. degrees in Electrical Engineering and Physics from Stanford University in Stanford, CA. He additionally holds a master’s degree in business administration (MBA) from San Jose State University in San Jose, CA. Dr. Iranmanesh is the founder and chairman of the International Society for Quality Electronic Design (ISQED). Currently, he serves as the CEO of Innovotek. Dr. Iranmanesh has been instrumental in advancing semiconductor technologies, innovative design methodologies, and engineering education. He holds nearly 100 US and international patents, reflecting his signifi cant contributions to the field. Dr. Iranmanesh is the Senior life members of EEE, senior member of the American Society for Quality, co-founder and Chair Emeritus of the IEEE Education Society of Silicon Valley, Vice Chair Emeritus of the IEEE PV chapter, and recipient of IEEE Outstanding Educator Award. Dr. Hossein Sayadi is a Tenure-Track Assistant Professor and Associate Chair in the Department of Computer Engineering and Computer Science at California State University, Long Beach (CSULB). He earned his Ph.D. in Electrical and Computer Engineering from George Mason University in Fairfax, Virginia, and an M.Sc. in Computer Engineering from Sharif University of Technology in Tehran, Iran. As a recognized researcher with over 14 years of research experience, Dr. Sayadi is the founder and director of the Intelligent, Secure, and Energy-Efficient Computing (iSEC) Lab at CSULB. His research focuses on advancing hardware security and trust, AI and
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Fairness in Generative AI
Build responsible AI systems with practical fairness benchmarks. Turn fairness principles into measurable requirements using metrics, human review, AI governance, CI gates, and drift monitoring across text, image, and multimodal AI.Key FeaturesTurn generative AI fairness principles into testable responsible AI requirementsBuild benchmarks and metrics for LLMs, image models, and multimodal generative AIOperationalize AI governance with human review, CI gates, drift monitoring, and scorecardsApply AI ethics to identify harms, evaluate trade-offs, and guide model releasesBook DescriptionA child asks an image model for a picture of a soccer player and gets a boy, every time. Across hiring, lending, education, and healthcare, fairness in generative AI can affect opportunity, dignity, and trust. This practical guide shows you how to measure fairness and build responsible AI practices across LLMs, image models, and multimodal generative AI systems.Move from AI ethics and fairness principles to engineering artifacts you can test and defend. Define harms and sensitive attributes, decide whose experience is in scope, and translate fairness trade-offs into measurable requirements. Then build a modular benchmarking framework for your ML and MLOps pipelines using versioned scenario and prompt libraries, quantitative metrics, qualitative rubrics, and human review.Put AI governance into practice by publishing actionable scorecards and dashboards, adding fairness checks to CI and release gates, monitoring drift, and preparing incident-response playbooks. Keep evaluation meaningful as models, data, and expectations change.A running soccer image-generator case connects the concepts to implementation, showing how responsible AI engineering moves from principles to measurable production controls.What you will learnTurn fairness principles into testable requirements for generative AIMap harms to representational, allocative, and procedural checksDesign versioned scenario and prompt libraries that protect real dataImplement fairness metrics for text, image, and multimodal AI outputsApply human review with rater training and inter-rater agreementPublish scorecards and dashboards stakeholders can act onGate model releases on fairness and monitor drift in productionRun incident-response playbooks that keep benchmarks credibleWho this book is forThis book is for people who build, ship, evaluate, and govern generative AI. ML and data engineers, application developers shipping LLM features, and responsible AI, trust-and-safety, and security engineers will gain hands-on value. Product managers and engineering leaders will develop a shared language for AI governance, while policymakers, regulators, researchers, standards contributors, and civil-society advocates will find structured ways to scrutinize deployed systems. A working grasp of ML evaluation and Python is all you need.
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Generative AI for Next-Gen Defence Strategies
This book aims to explore the manifold role of Generative AI in enhancing defence capabilities and its potential to transform future warfare. It is divided into three sections. The first discusses the fundamentals of Generative AI and its strategic significance in modern defence sector. This section sets the foundation for understanding how these technologies are shaping current defence strategies and providing a competitive edge. The second explores the application of Generative AI across military, aerospace and naval domain. This section starts by exploring the use of Generative AI for Strategic and Operational Planning, Battlefield Simulation and Training, and development of Autonomous Vehicles and Exoskeletons. The section also covers the use of Generative AI in Drone Swarms, Autonomous Navigation, and Mission Control, as well as its role in maintaining and monitoring Aerospace Infrastructure. Advanced applications such as Radar Systems, Underwater Drones, and Naval Robotics are also discussed in the section. Additionally, the predictive capabilities of Generative AI, including Trajectory Prediction and Failure Prediction for ships and aircraft, and its significance in Naval Warfare and Underwater Sensing have been explored. It also examines the critical role of Generative AI in Cyber Security and Network Defence. The last section of the book addresses the challenges of Generative AI in Defence and the ethical considerations that need to be addressed while using Generative AI in Defence sector. This section also critically assesses the potential threats posed by Generative AI in Cognitive Warfare, highlighting the need for robust ethical frameworks and policies to mitigate risks.The book will be useful for defence professionals, researchers, and technologists who want to understand the transformative potential of Generative AI in defence and attempts to inspire innovative thinking and foster a deeper understanding of the strategic value of Generative AI.
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Illustrated Guide to AI Agents
Artificial intelligence is entering a new phase. No longer limited to answering prompts or completing simple writing tasks, AI agents can now reason, plan, and act with increasing independence. From accelerating scientific breakthroughs to supporting creative work, these systems are quickly reshaping industries and everyday life. This book provides the conceptual foundation and practical insights you need to understand—and effectively work with—this emerging technology.Through hundreds of clear graphic illustrations, Maarten Grootendorst and Jay Alammar explain how AI agents are built, how they think, and where they're heading. Designed for professionals, students, and curious learners alike, this guide goes beyond the buzz to reveal what's actually happening inside these systems, why it matters, and how to apply the knowledge in real-world contexts. With its visual storytelling and accessible explanations, An Illustrated Guide to AI Agents is your essential reference for navigating the next frontier of artificial intelligence.Explore the core architecture of AI agents: tools, memory, and planningUnderstand reasoning LLMs, multimodal models, and multi-agent collaborationLearn advanced methods, including distillation, quantization, and reinforcement learningEvaluate real-world applications, strengths, and limitations of AI agents
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Smarter Healthcare through Generative Intelligence
Generative artificial intelligence (AI) has potential to improve healthcare because it can enable smarter, more efficient solutions across diagnostics, treatment, and patient care. By analyzing vast amounts of medical data, generative AI systems can identify patterns, predict disease progression, and assist in early diagnosis with remarkable accuracy. It enhances personalized medicine by generating tailored treatment plans based on individual patient profiles, including genetic, lifestyle, and clinical data.Smarter Healthcare through Generative Intelligence examines the convergence of healthcare and intelligent computation and how it is reshaping the global healthcare landscape. Focusing on applications and management issues, it looks at generative models, privacy-aware distributed computing, smart sensing technologies, and policy frameworks. From data augmentation to drug discovery and from mental health assessment to intelligent patientcommunication, the book explores the ways generative AI can transform healthcare.Highlights include:· Fog computing for smart healthcare· Leveraging generative intelligence to overcome dataset limitations in emotional well-being analysis· Ethical considerations in AI-driven smart healthcare· Designing intelligent chatbots for healthcareGenerative AI is transforming healthcare into a more proactive, precise, and patient-centered industry. Written for healthcare practitioners, researchers, policymakers, and technologists, this book is a guide on how they can design,adopt, and govern generative technologies for smarter healthcare.
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Practical Guide to Generative AI Using Amazon Bedrock
This comprehensive guide gives you the knowledge and skills you need to excel in Generative AI. From understanding the fundamentals to mastering techniques, this book offers a step-by-step approach to leverage Amazon Bedrock to build, deploy, and secure Generative AI applications. The book presents structured chapters and practical examples to delve into key concepts such as prompt engineering, retrieval-augmented generation, and model evaluation. You will gain profound insights into the Amazon Bedrock platform. The book covers setup, life cycle management, and integration with Amazon SageMaker. The book emphasizes real-world applications, and provides use cases and best practices across industries on topics such as text summarization, image generation, and conversational AI bots. The book tackles vital topics including data privacy, security, responsible AI practices, and guidance on navigating governance and monitoring challenges while ensuring adherence to ethical standards and regulations. The book provides the tools and knowledge needed to excel in the rapidly evolving field of Generative AI. Whether you're a data scientist, AI engineer, or business professional, this book will empower you to harness the full potential of Generative AI and drive innovation in your organization. What You Will Learn Understand the fundamentals of Generative AI and Amazon BedrockBuild Responsible Generative AI applications leveraging Amazon BedrockKnow techniques and best practicesSee real-world applicationsIntegrate and manage platformsHandle securty and governance issuesEvaluate and optimze modelsGain future-ready insightsUnderstand the project life cycle when building Generative AI Applications Who This Book Is For Data scientistys, AI/ML engineers and architects, software developers plus AI enthusiasts and studenta and educators, and leaders who want to evangelize within organizatios
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R 698.00
Art and Ethics of Generative AI
Generative AI technologies are reshaping the way we create, communicate, and compute, presenting both unprecedented opportunities and profound challenges. As these systems increasingly influence decisions that shape our lives, it becomes clear that performance metrics alone cannot define their success. A deeper understanding of the intersection between technical innovation and human values is essential to navigate this transformative era.The Art and Ethics of Generative AI: Fairness, Transparency, and Human Values provides a balanced exploration of generative AI, combining technical insights with ethical considerations. It explains generative model architectures and learning paradigms in an accessible way, while addressing critical issues such as algorithmic bias, explainability challenges, and emerging regulatory approaches. Practical frameworks for responsible AI development are offered, alongside multidisciplinary perspectives that bridge technical and humanistic viewpoints. Abstract concepts are brought to life through practical illustrations, demonstrating how ethical considerations play out in real-world AI applications.Designed for a wide audience, the book serves AI practitioners seeking actionable guidance for building responsible systems, researchers exploring nuanced ethical dimensions, educators and students looking for comprehensive learning materials, and policymakers needing governance insights. It is an essential resource for anyone engaged in the rapidly evolving AI ecosystem.
R 5,165.00
Gen AI Manufacturing Revolution
Transformative insights and advice to reduce downtime, navigate disruptions, and improve efficiency The Gen AI Manufacturing Revolution highlights how generative and agentic artificial intelligence are transforming manufacturing processes, driving innovation, and reshaping the way we work. Implementing AI in manufacturing comes with unique challenges, such as integrating AI with physical machinery, managing intricate supply chains, and ensuring regulatory compliance. This book addresses these challenges, helping you harness AI's potential while navigating its complexities. With tailored strategies and use cases, the book demonstrates how to unlock the power of Gen AI, from automating routine tasks to enabling better decision-making. You'll learn to build an AI-ready workforce, overcome common challenges in adoption, drive value across the organization, and maintain accountability. Leverage generative AI to streamline product design, boost creativity, and accelerate innovationDeploy agentic AI systems to autonomously manage workflows, monitor performance, and support complex decision-makingBuild an AI-ready workforce capable of collaborating with intelligent systemsAddress ethical, operational, and cultural challenges in AI adoptionDrive enterprise-wide value while maintaining transparency and accountability Whether you're looking to reduce downtime, enhance efficiency, or stay ahead of disruptive change, this book is your essential guide to leading the next wave of industrial transformation through AI.
R 730.00
Transforming Financial Services with Generative AI
Generative AI (GenAI) is revolutionizing the financial services sector (FSS), offering new ways to enhance efficiency, improve customer experiences, and streamline operations. This book is your comprehensive guide to understanding and implementing GenAI within FSS. Grounded in real-world use cases, this book moves from fundamentals to boardroom-ready execution. You'll start with the core concepts behind AI, machine learning, and GenAI, then pivot into the challenges of FSS: intense regulatory scrutiny, evolving fraud threats, operational complexity, and rising expectations for personalized, always-on service. You'll learn a practical blueprint for GenAI adoption, including strategy, governance, risk controls, data readiness, and culture--through the lens of executives tasked with delivering measurable value responsibly. From there, you'll get hands-on experience building GenAI systems: prompt design, evaluation, Retrieval-Augmented Generation (RAG), fine-tuning, and agentic workflows. You'll see how these capabilities power mission-critical functions across the enterprise: Finally, you'll operationalize all of it with modern FMOps/LLMOps practices--security, privacy, performance, cost management, monitoring, and continuous improvement--so pilots become production systems that scale. The closing chapter distills emerging trends, from autonomous agents to domain-specialized models, and lays out next steps so your organization can adopt GenAI with confidence, compliance, and a clear return on investment. Whether you're a CIO crafting a roadmap, a product leader shipping AI features, a data scientist building RAG pipelines, or a risk and compliance executive seeking control and clarity, this book is your end-to-end guide to deploying GenAI that is safe, explainable, and enterprise-grade. What You Will Learn Develop a strategic blueprint for GenAI adoption, including implementation methodologies, risk mitigation, and best practices for financial institutions Master the core components of building GenAI applications, from prompt engineering and model evaluation to RAG and Agentic AI Discover how GenAI transforms risk and compliance management, including applications in Anti-Money Laundering (AML), trade surveillance, and regulatory reporting Explore practical GenAI use cases across retail banking, investment banking, and wealth management, and learn about successful operational deployment Who This Book Is For Data scientists, AI professionals, and financial services experts interested in leveraging generative AI within the financial sector.
R 1,368.00
R 1,130.00
