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R 489.00
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.
R 11,182.64
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
R 1,484.00
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 1,696.51
Kubernetes for Generative AI Solutions
Master the complete Generative AI project lifecycle on Kubernetes (K8s) from design and optimization to deployment using best practices, cost-effective strategies, and real-world examples.Key FeaturesBuild and deploy your first Generative AI workload on Kubernetes with confidenceLearn to optimize costly resources such as GPUs using fractional allocation, Spot Instances, and automationGain hands-on insights into observability, infrastructure automation, and scaling Generative AI workloadsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionGenerative AI (GenAI) is revolutionizing industries, from chatbots to recommendation engines to content creation, but deploying these systems at scale poses significant challenges in infrastructure, scalability, security, and cost management.This book is your practical guide to designing, optimizing, and deploying GenAI workloads with Kubernetes (K8s) the leading container orchestration platform trusted by AI pioneers. Whether you're working with large language models, transformer systems, or other GenAI applications, this book helps you confidently take projects from concept to production. You’ll get to grips with foundational concepts in machine learning and GenAI, understanding how to align projects with business goals and KPIs. From there, you'll set up Kubernetes clusters in the cloud, deploy your first workload, and build a solid infrastructure. But your learning doesn't stop at deployment. The chapters highlight essential strategies for scaling GenAI workloads in production, covering model optimization, workflow automation, scaling, GPU efficiency, observability, security, and resilience.By the end of this book, you’ll be fully equipped to confidently design and deploy scalable, secure, resilient, and cost-effective GenAI solutions on Kubernetes.What you will learnExplore GenAI deployment stack, agents, RAG, and model fine-tuningImplement HPA, VPA, and Karpenter for efficient autoscalingOptimize GPU usage with fractional allocation, MIG, and MPS setupsReduce cloud costs and monitor spending with Kubecost toolsSecure GenAI workloads with RBAC, encryption, and service meshesMonitor system health and performance using Prometheus and GrafanaEnsure high availability and disaster recovery for GenAI systemsAutomate GenAI pipelines for continuous integration and deliveryWho this book is forThis book is for solutions architects, product managers, engineering leads, DevOps teams, GenAI developers, and AI engineers. It's also suitable for students and academics learning about GenAI, Kubernetes, and cloud-native technologies. A basic understanding of cloud computing and AI concepts is needed, but no prior knowledge of Kubernetes is required.
R 1,628.00
Generative AI-Driven Sustainable Smart City
The rapid rise of Generative AI is increasingly reshaping the field of urban computing, marking the next frontier of sustainable smart city research and practice. This book takes readers on an exploratory journey into the transformative potential of Deep Generative Models (DGMs), hybrid architectures, and Foundation Models (FMs) to redefine the future urban landscape. These technologies contribute to enhancing environmental sustainability, climate resilience, infrastructure performance, operational efficiency, spatial organization, and functional livability through forward-thinking approaches to urban planning, design, and management—often implemented via digital twins.DGMs enable synthetic data generation, data augmentation, data imputation, scenario simulation, and predictive modeling, providing richer insights into complex urban systems. Hybrid architectures integrate generative and conventional models to improve robustness, scalability, and flexibility across diverse urban tasks. FMs extend these capabilities by facilitating knowledge generalization across datasets, supporting reasoning, and enabling adaptive decision-making under uncertainty. Strengthened by integrative frameworks and cross-disciplinary insights, these technologies offer powerful tools for developing sophisticated urban solutions. They advance how urban ecosystems are environmentally and strategically planned, efficiently and adaptively managed, aesthetically and functionally designed, and spatially and contextually organized.Rich in conceptual frameworks, theoretical insights, operational models, applied innovations, practical case studies, and policy guidance, this book equips researchers, planners, designers, technologists, and policymakers to harness generative intelligence in shaping adaptive, context-aware, and continuously refined urban environments that are ecologically responsible, climate-resilient, and future-ready. Essential for anyone exploring the frontier of GenAI-driven urban development, it offers actionable insights to guide the next generation of sustainable smart cities.
R 3,663.00
Generative AI-Driven Sustainable Smart City
The rapid rise of Generative AI is increasingly reshaping the field of urban computing, marking the next frontier of sustainable smart city research and practice. This book takes readers on an exploratory journey into the transformative potential of Deep Generative Models (DGMs), hybrid architectures, and Foundation Models (FMs) to redefine the future urban landscape. These technologies contribute to enhancing environmental sustainability, climate resilience, infrastructure performance, operational efficiency, spatial organization, and functional livability through forward-thinking approaches to urban planning, design, and management—often implemented via digital twins.DGMs enable synthetic data generation, data augmentation, data imputation, scenario simulation, and predictive modeling, providing richer insights into complex urban systems. Hybrid architectures integrate generative and conventional models to improve robustness, scalability, and flexibility across diverse urban tasks. FMs extend these capabilities by facilitating knowledge generalization across datasets, supporting reasoning, and enabling adaptive decision-making under uncertainty. Strengthened by integrative frameworks and cross-disciplinary insights, these technologies offer powerful tools for developing sophisticated urban solutions. They advance how urban ecosystems are environmentally and strategically planned, efficiently and adaptively managed, aesthetically and functionally designed, and spatially and contextually organized.Rich in conceptual frameworks, theoretical insights, operational models, applied innovations, practical case studies, and policy guidance, this book equips researchers, planners, designers, technologists, and policymakers to harness generative intelligence in shaping adaptive, context-aware, and continuously refined urban environments that are ecologically responsible, climate-resilient, and future-ready. Essential for anyone exploring the frontier of GenAI-driven urban development, it offers actionable insights to guide the next generation of sustainable smart cities.
R 10,257.00
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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R 626.00
Practical MLflow for Generative AI on Databricks
Bridge the gap between cutting-edge research and the practical operationalization of generative AI. This handy guide demystifies the complexities of managing GenAI applications using leading open source platform MLflow on Databricks.What sets this book apart is its focus on the requirements of managing and deploying GenAI applications. Authors Nuwan Ganganath, Julie Nguyen, and Chang Shi Lim—seasoned ML and AI specialists from Databricks—take you through each stage of the development of an agentic chatbot, sharing best practices, innovative techniques, and common challenges along the way. By combining deep technical insights with interactive exercises and actionable real-world code examples, they ensure that you're equipped to implement robust GenAI applications in modern enterprise environments.Understand MLflow fundamentals, including core components and the tool's application to GenAI on the Databricks platformBuild, track, and manage GenAI applications using MLflow, from prompts to the overall agentic chainOptimize and evaluate GenAI applications through a combination of automated LLM-assisted judges and human-in-the-loop reviewDeploy production-ready GenAI applications, leveraging Databricks' scalable infrastructure and monitoring capability
R 1,978.00
Modern Generative AI with ChatGPT and OpenAI Models
Harness the power of AI with innovative, real-world applications, and unprecedented productivity boosts, powered by the latest advancements in AI technology like ChatGPT and OpenAIPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesExplore the theory behind generative AI models and the road to GPT3 and GPT4Become familiar with ChatGPT's applications to boost everyday productivityLearn to embed OpenAI models into applications using lightweight frameworks like LangChainBook DescriptionGenerative AI models and AI language models are becoming increasingly popular due to their unparalleled capabilities. This book will provide you with insights into the inner workings of the LLMs and guide you through creating your own language models. You'll start with an introduction to the field of generative AI, helping you understand how these models are trained to generate new data. Next, you'll explore use cases where ChatGPT can boost productivity and enhance creativity. You'll learn how to get the best from your ChatGPT interactions by improving your prompt design and leveraging zero, one, and few-shots learning capabilities. The use cases are divided into clusters of marketers, researchers, and developers, which will help you apply what you learn in this book to your own challenges faster. You'll also discover enterprise-level scenarios that leverage OpenAI models' APIs available on Azure infrastructure; both generative models like GPT-3 and embedding models like Ada. For each scenario, you'll find an end-to-end implementation with Python, using Streamlit as the frontend and the LangChain SDK to facilitate models' integration into your applications. By the end of this book, you'll be well equipped to use the generative AI field and start using ChatGPT and OpenAI models' APIs in your own projects.What you will learnUnderstand generative AI concepts from basic to intermediate levelFocus on the GPT architecture for generative AI modelsMaximize ChatGPT's value with an effective prompt designExplore applications and use cases of ChatGPTUse OpenAI models and features via API callsBuild and deploy generative AI systems with PythonLeverage Azure infrastructure for enterprise-level use casesEnsure responsible AI and ethics in generative AI systemsWho this book is forThis book is for individuals interested in boosting their daily productivity; businesspersons looking to dive deeper into real-world applications to empower their organizations; data scientists and developers trying to identify ways to boost ML models and code; marketers and researchers seeking to leverage use cases in their domain – all by using Chat GPT and OpenAI Models.A basic understanding of Python is required; however, the book provides theoretical descriptions alongside sections with code so that the reader can learn the concrete use case application without running the scripts.
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Building Generative AI-Powered Apps
Generative AI has gone beyond the responsibility of researchers and data scientists and is being used by production engineers. However, there is a lot of confusion where to get started when building an end-to-end app with generative AI. This book consolidates core models, frameworks, and tools into a single source of knowledge. By providing hands-on examples, the book takes you through the generative AI ecosystem to build applications for production.The book starts with a brief and accessible introduction to transformer models before delving into some of the most popular large language models and diffusions models (image generation). These models are the foundations of both AI and your potential new apps. You will then go through various tools available to work with these models, starting with Langchain, a framework to develop foundational models, which is the next building block you should grasp after understanding generative AI models. The next chapters cover databases, caching, monitoring, etc., which are the topics necessary to build larger-scale applications. Real-world examples using these models and tools are included.By the end of this book, you should be able to build end-to-end apps that are powered by generative AI. You also should be able to apply the tools and techniques taught in this book to your use cases and business.What You Will LearnWhat is Generative AI?What is ChatGPT and GPT4?What are language models and diffusions models?How do we deploy LangChain and HuggingFace?Who This Book Is ForSoftware engineers with a few years of experience building applications in any language or infrastructure
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R 701.00
Generative AI for Cybersecurity and Privacy
Generative AI for Cybersecurity and Privacy offers a groundbreaking exploration of how generative artificial intelligence is reshaping the landscape of cybersecurity and privacy protection in an era of rapid digital transformation. As cyber threats grow in sophistication and scale, this book provides a timely and authoritative guide to harnessing generative AI to safeguard digital ecosystems, secure sensitive data, and address emerging challenges across diverse domains.Spanning a series of expertly curated chapters, this volume delves into cutting-edge advancements and practical applications of generative AI in cybersecurity. It covers critical areas such as AI-driven threat detection and response, privacy-preserving AI models, secure IoT and cloud computing frameworks, and robust defenses for cyber-physical systems, including Smart Cities and wireless networks. The book balances rigorous theoretical foundations with real-world case studies, making it an essential resource for researchers, security professionals, policymakers, and organizational leaders.The book offers comprehensive coverage of key topics, including:• Leveraging generative AI for proactive threat detection, risk analysis, and automated incident response• Innovative approaches to data privacy, compliance, and governance in AI-driven systems• Advanced methodologies for securing IoT, mobile applications, and cloud infrastructures• Practical frameworks for integrating generative AI into cybersecurity strategies for critical infrastructures• Emerging applications of generative AI in personalized, secure digital experiences, such as e-commerce and smart systemsAuthored by a global team of leading researchers and practitioners, this book stands out by not only addressing current cybersecurity and privacy challenges but also proposing forward-thinking, scalable solutions powered by generative AI. Unlike traditional resources, it emphasizes the transformative potential of AI in revolutionizing risk analysis, threat mitigation, and privacy preservation across multiple domains. Whether you’re navigating the complexities of IoT, cloud security, or emerging cyber threats, Generative AI for Cybersecurity and Privacy equips you with the knowledge and tools to build intelligent, secure, and future-ready strategies for a dynamic digital world.
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Generative and Open AI in Industry 5.0
This book provides an up-to-date and easy-to-read introduction to Generative AI and its revolutionary contribution to building Industry 5.0. Written for both practitioners and interested readers, it is a clear and compelling overview of the technologies at the heart of the next industrial revolution.Part one introduces the conceptual notions behind generative models, large language models (LLMs), and the neural networks that drive them. It delves into the mathematical and conceptual foundations of Generative AI, transformer architecture, and the practical applications of language and vision models. The second half links the innovations to Industry 5.0, discussing digital twins, predictive maintenance, DevOps, and cybersecurity in intelligent systems. It encompasses real-world applications of AI in areas such as finance, manufacturing, and smart infrastructure. Case studies, like the application of generative models in FinTech and blockchain adoption, underscore how AI is already transforming business and society around the world.The book is perfect for students, researchers, and practitioners of AI, data science, and industrial systems interested in comprehending the theory and practical applications of Generative AI. It also benefits educators and policy makers who wish to lead in cutting-edge technological advancements, with emphasis on responsible, secure, and human-oriented AI applications for a brighter future.
R 5,165.00
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.
R 1,368.00
Architecting Generative AI Applications
Take generative AI from prototype to production with confidence, master core LLM architectures, rigorous evaluation (offline and A/B testing), LLMOps and deployment pipelines, and the reliability practices that keep systems stable, secure, and scalable in the real world.Key FeaturesTurn generative AI prototypes into production-ready applicationsMaster LLM evaluation, observability, and reliability engineeringDeploy and scale AI systems using LLMOps and modern DevOps toolsBook DescriptionVibe-coding tools & coding assistants make it easy to spin up generative AI prototypes. Getting those prototypes into production is where most teams stall. This book is a practical guide to building production-ready generative AI applications that are reliable, scalable, and secure, and to understanding where traditional software best practices can clash with the realities of operating LLM-based systems.Written by a Staff AI Engineer at Google, it takes you through the full AI product lifecycle: scoping and building effective prototypes, aligning them with business goals, and scaling enterprise-wide generative AI adoption. You will learn how to evaluate LLMs with offline metrics, human-in-the-loop methods, and statistical testing. Next, you will design core architectures such as RAG, vector databases, agents, and memory systems. Next, operationalize these systems with production-grade code, robust testing, DevOps, MLOps, and LLMOps workflows, including deployment and scaling on modern LLMOps platforms. The book also covers security, Responsible AI, and modern observability and reliability for generative AI systems. By the end you’ll learn how to run post-launch A/B tests, maintain systems over time, and measure business impact. The focus is on durable engineering principles, so your products succeed beyond the prototype stage.What you will learnDesign offline and online evaluation strategies (including statistical A/B testing) and collect the right dataConvert AI prototypes into production-ready applications that are stable, scalable, & secureReduce maintenance effort with best practices in testing, configuration, and code readabilityImplement DevOps, MLOps, and LLMOps—what's common and what differs across these approaches for AI systemsBuild platform teams to scale enterprise-wide generative AI adoptionDefine reliability targets using SRE principles and statistical A/B testingWho this book is forThis book is for technical leaders, AI engineers, data scientists, software engineers, and architects building generative AI applications. It is also ideal for engineering managers, product leaders, and technical decision-makers who need to understand how to deploy, scale, and maintain production-grade AI systems.
R 1,628.00
Demystifying Generative AI
Demystifying Generative AI: A Practical and Intuitive Introduction In an era where artificial intelligence is rapidly reshaping the world and redefining the way we work, Demystifying Generative AI: A Practical and Intuitive Introduction emerges as a key resource for professionals and enthusiasts seeking to leverage the transformative power of AI. Authored by AI experts Robert Barton and Jerome Henry, this book is a unique entry into the world of AI. Unlike traditional references that are either too technical or overly simplistic, this book strikes a balance by providing clear explanations and practical examples, all supported by real-world case studies. It is designed as an intuitive guide through the inner workings of AI, from foundational principles to deployment and security best practices. It is designed to make generative AI accessible to anyone interested in learning more about AI, including IT professionals, software developers, business analysts, tech managers, educators, and decision-makers. Rob and Jerome address the surging demand for AI literacy as organizations invest heavily in AI-driven solutions, aiming to boost productivity and maintain a competitive edge. Key Topics: Foundations of AI: A historical and conceptual overview of artificial intelligence, including essential terminology and the broader AI landscape.How Generative AI Actually Works: An in-depth and intuitive analysis of LLMs, from the origins of language modeling into the modern world of Transformers and their applications.Unique Approach: Balances depth and accessibility, focusing on intuitive understanding with examples, adding practical application rather than dense theory or superficial summaries.Practical Applications: Features how GenAI and LLMs can be deployed in practices, using applications like RAG, fine-tuning techniques, and how to security LLMs from attack.Comprehensive Coverage: Covers foundational AI concepts, machine learning (classic and advanced), deep learning, large language models, Transformers, AI infrastructure, agentic AI systems, ethical considerations, security for LLMs, and deployment strategies. Demystifying Generative AI emphasizes the growing necessity of AI literacy in a technology-driven world. By demystifying generative AI and equipping readers with both theoretical grounding and practical tools, the book aims to empower individuals and organizations to succeed in the era of intelligent automation. With its expert authorship and accessible format, this book is an essential resource for navigating the next wave of innovation.
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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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Generative AI
Generative AI (GenAI) is the hottest new area of AI, accelerating innovations affecting all industries and showcasing AI’s ability to present human-like language capabilities with large language models (LLMs). This book builds a solid foundation for the technology underlying GenAI Models, LLM, and foundation and reasoning models for the No-Code audience by explaining the models' workings in simple English. It then takes the reader on a journey to find out where there are gaps to innovate. Each chapter outlines the state of research in each topic and invites learners to build on this research to think about ethical implications and balance it with innovation to benefit everyone. The author covers the technological, product, and societal impact of Generative AI to give a comprehensive guide for this important technology. This book will help the data scientist, product manager, and business leader alike because it covers the API economy and critical industries impacted and offers a framework to think about automation and jobs impacted and what competencies are needed to excel in GenAI and offers an in-depth guide to build GenAI products and applications.
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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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Designing the Conceptual Landscape for a XAIR Validation Infrastructure
This book focuses on explainable-AI-ready (XAIR) data and models, offering a comprehensive perspective on the foundations needed for transparency, interpretability, and trust in AI systems. It introduces novel strategies for metadata structuring, conceptual analysis, and validation frameworks, addressing critical challenges in regulation, ethics, and responsible machine learning.Furthermore, it highlights the importance of standardized documentation and conceptual clarity in AI validation, ensuring that systems remain transparent and accountable.Aimed at researchers, industry professionals, and policymakers, this resource provides insights into AI governance and reliability. By integrating perspectives from applied ontology, epistemology, and AI assessment, it establishes a structured framework for developing robust, trustworthy, and explainable AI technologies.
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ML and Generative AI in the Data Lakehouse
In today's race to harness generative AI, many teams struggle to integrate these advanced tools into their business systems. While platforms like GPT-4 and Google's Gemini are powerful, they aren't always tailored to specific business needs. This book offers a practical guide to building scalable, customized AI solutions using the full potential of data lakehouse architecture.Author Bennie Haelen covers everything from deploying ML and GenAI models in Databricks to optimizing performance with best practices. In this must-read for data professionals, you'll gain the tools to unlock the power of large language models (LLMs) by seamlessly combining data engineering and data science to create impactful solutions.Learn to build, deploy, and monitor ML and GenAI models on a data lakehouse architecture using DatabricksLeverage LLMs to extract deeper, actionable insights from your business data residing in lakehousesDiscover how to integrate traditional ML and GenAI models for customized, scalable solutionsUtilize open source models to control costs while maintaining model performance and efficiencyImplement best practices for optimizing ML and GenAI models within the Databricks platform
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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 611.00
Building Generative AI Solutions for Industry with AWS
Building Generative AI Solutions for Industry with AWS is a comprehensive guide for developers, and data scientists aiming to harness the power of Generative AI across a range of industrial domains using Amazon Web Services. This hands-on book provides readers with the knowledge and practical skills to design, secure, govern, and deploy GenAI solutions effectively in real-world environments. The book begins with an introduction to the fundamentals of Generative AI and the AI/ML services offered by AWS. Readers will explore industry use cases and learn how to kick-start their journey building GenAI applications on the AWS cloud. It focuses on security and governance, covering essential practices for data privacy, access control, encryption, compliance, and ethical AI deployment. Each chapter then dives deep into specific industry applications--automotive, manufacturing, healthcare, financial services, and media & entertainment. From autonomous driving and predictive maintenance to fraud detection and content creation, readers will learn how GenAI is transforming operations and unlocking new opportunities in these fields. The book also addresses cost management strategies for GenAI on AWS, offering guidance on optimizing resources and budgeting efficiently. Concluding with future possibilities and implementation best practices, this book equips professionals with the tools and insights to build scalable, secure, and compliant GenAI systems tailored to their domain. What you will learn: Understand the fundamentals of Generative AI and AWS AI/ML services.How to build and deploy secure, compliant GenAI applications on AWS.Explore real-life GenAI use cases across industries like automotive, healthcare, and finance.Gain insights into managing GenAI costs and optimizing AWS resources. Who this book is for: Software developers, machine learning engineers, data scientists, and cloud architects who want to build real-life Generative AI solutions using AWS.
R 2,417.79
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.
R 7,584.84
R 340.00
RAG-Driven Generative AI
Building MAS-RAG (multi-agent AI systems for RAG) that reason over real-world data using hybrid retrieval and scalable architectures for production use.Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesMaster DualRAG by combining vector search with SQL filtering over structured enterprise dataImplement GraphRAG, Spatial-RAG, and vector search natively in Oracle Database 23aiBuild multimodal video pipelines with human-feedback loops and fine-tuned modelsBook DescriptionStop moving your data to the AI. This second edition defines a revolutionary architectural shift: bringing the AI to the data. By using Oracle Database 23ai as a converged engine in this book, you will architect Sovereign AI systems that eliminate the fragmentation, latency, and massive security risks inherent in traditional data extraction.You’ll work with DualRAG, synchronizing unstructured vector semantics with the deterministic truth of structured SQL, Graph, and Spatial retrieval. This allows your systems to reason over verified corporate data rather than probabilistic guesses, reducing hallucinations at the source. Moving beyond simple pipelines, you’ll also build MAS-RAG (multi-agent systems for RAG), where autonomous agents coordinate across hybrid retrieval workflows, multimodal video pipelines, and graph-based knowledge structures.Designed for developers and architects, these blueprints transform disconnected data silos into a unified engine to architect autonomous enterprise intelligence that scales with RLHF and model fine-tuning. By the end of the book, you’ll be able to design and deploy enterprise AI systems that combine retrieval, reasoning, and structured data to build reliable generative AI applications.*Email sign-up and proof of purchase requiredWhat you will learnBring intelligence directly to the data within Oracle Database 23aiDefeat hallucinations and data poisoning with DualRAG, synchronizing vector semantics with structured SQLBuild MAS-RAG pipelines with Planner, Agent Registry, and MCP-standardized sovereign agentsEngineer an inference-time router using hybrid adaptive RAG to switch between reasoning, retrieval, and human feedbackFuse vector similarity, Oracle Spatial, and SQL Property Graph traversal into a converged hyper-queryMultimodal video RAG with version-controlled schema registry and semantic vector search over visual assetsWho this book is forThis book is for AI engineers, ML engineers, data scientists, and MLOps professionals who want to build production-ready generative AI systems grounded in enterprise data. It will also benefit solutions architects, database engineers, and software developers looking to integrate large language models with structured and unstructured data sources using modern retrieval architectures. Readers should be comfortable with Python and have a basic understanding of machine learning concepts. Prior experience with generative AI or vector databases will help you get the most ou
R 1,791.00
R 878.00
R 1,079.00
Combating Cyberattacks Targeting the AI Ecosystem
This book explores in detail the AI-driven cyber threat landscape, including inherent AI threats and risks that exist in Large Language Models (LLMs), Generative AI applications, and the AI infrastructure. The book highlights hands-on technical approaches to detect security flaws in AI systems and applications utilizing the intelligence gathered from real-world case studies. Lastly, the book presents a very detailed discussion of the defense mechanisms and practical solutions to secure LLMs, GenAI applications, and the AI infrastructure. The chapters are structured with a granular framework, starting with AI concepts, followed by practical assessment techniques based on real-world intelligence, and concluding with required security defenses. Artificial Intelligence (AI) and cybersecurity are deeply intertwined and increasingly essential to modern digital defense strategies. The book is a comprehensive resource for IT professionals, business leaders, and cybersecurity experts for understanding and defending against AI-driven cyberattacks.
R 1,186.00
