Search results for "Generative AI Architecture"
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R 519.00
R 550.00
R 825.00
Transformation Enterprise Architecture with Agentic AI
This book is a practical guide to transforming enterprise architecture in the age of generative AI. As intelligent systems reshape how organizations build, scale, and govern technology, this book bridges the gap between traditional architectural frameworks and the emerging GenAI-driven landscape.The book begins with an overview of enterprise architecture principles, including TOGAF, Zachman, and Gartner frameworks, and dives into core architectural layers—application, data, technology, infrastructure, and security. It then introduces the fundamentals of Generative AI, its model types, and its rapid evolution within enterprise contexts. The core of the book is how GenAI disrupts and elevates architectural thinking—from conversational modeling and automated decision records to reimagining design lifecycles through intelligent automation, augmentation, and autonomy. Readers will learn how to modernize application portfolios, document and repair APIs, and embed LLMs as intelligent middleware. Dedicated chapters explore GenAI's transformative impact on platform engineering, DevOps, cloud/edge infrastructure, observability, and AI-augmented governance. Key use cases include auto-generating code and configurations, orchestrating deployment pipelines with non-human agents, enabling self-documenting platforms, and enhancing security through GenAI-driven threat modeling and compliance enforcement. The book concludes by examining responsible AI practices and human oversight in autonomous architectures, offering practical insights into creating secure, scalable, and resilient systems powered by GenAI.Whether you're an architect or developer, this book provides a modern framework for applying GenAI across the enterprise stack and designing the next generation of intelligent, adaptive, and self-governing enterprise systems. What you will learn:Understand core enterprise architecture principles and frameworks in the context of a generative AI-driven world.How GenAI is disrupting and enhancing every layer of enterprise architecture—from design to deployment.Explore practical methods for integrating GenAI into application architecture, API ecosystems, and DevOps pipelines.Discover how intelligent agents, automation, and orchestration are reshaping platform engineering and cloud infrastructure. Who this book is for:This book is for enterprise architects, AI practitioners and machine learning engineers seeking to integrate Generative AI into enterprise 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
Diffusions in Architecture: Artificial Intelligence and Image Generators
DIFFUSIONS IN ARCHITECTURE A guide to diffusion models and their impact on design, with insight on how this novel artificial intelligence technology may disrupt the industry Diffusions in Architecture: Artificial Intelligence and Image Generators delves into the impact of Generative AI models and their effect on architecture design and aesthetics. The book presents an in-depth analysis of how these new technologies are revolutionizing the field of architecture and changing the way architects approach their work. The architects presented in the book focus on the application of specific AI techniques and tools used in generative design, such as Diffusion models, Dall-E2, Stable Diffusion, and MidJourney. It discusses how these techniques can generate synthetic images that are both realistic and imaginative, creating new possibilities for architectural design and aesthetics. Twenty-two leading designers and theorists offer their insights, providing disciplinary depth by covering the full impact of these learning tools on architecture.
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R 764.00
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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Artificial Intelligent Architecture
The impact of artificial intelligence in the discipline of architecture is unavoidable and undeniable. The recent mass adoption of highly accessible machine learning tools including DALL-E, Stable Diffusion, and Midjourney has allowed designers to test their limits and assess their role as an author in the design of the built environment. This book includes speculations on the introduction of artificial intelligence bots/apps into architecture and features a collection of works from 18 architects and designers who are interrogating current AI applications. Within each chapter, authors put forth a position through a framework consisting of theory and application lenses. Additionally, interviews from leading practitioners will offer insights into the current curiosities fuelling investigation.This book will incite dialogue about the potential of AI as an ideation device and extension of the architect’s authorship. As a part of this work, curation plays an important role as the technology generates content at an incredible pace. Architectural design thinking will have to reconcile the injection of this new tool and this book will speculate on the current state in its infancy.
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R 2,134.00
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.
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R 1,069.00
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.
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Generative AI and Generative AI of Things for Sustainable Smart Cities
This pioneering book invites readers on a compelling journey into Generative Artificial Intelligence (AI) and its groundbreaking convergence with AI of Things (AIoT), introducing GAIoT as a transformative frontier in urban computing and intelligence. GAIoT signals a paradigm shift towards more intelligent, self-learning, self-evolving, and context-aware systems—capable of generating adaptive, forward-looking solutions to the complex infrastructuraland environmental challenges confronting sustainable smart cities.With its combination of theoretical depth, applied innovation, and interdisciplinary scope, the book offers a comprehensive examination of deep generative models—namely Generative Adversarial Networks, Variational Autoencoders, Diffusion Models, Transformers, and hybrid architectures—and their applications in environmental sustainability, climate resilience, infrastructure optimization, dynamic decision-making, and data-driven urban management and planning. From synthetic data generation, data augmentation, and data imputation to predictive modeling and scenario simulation, GAIoT is driving the next wave of climate-responsive, environmentally conscious smart city innovation.What sets this book apart is its first-of-its-kind focus on GAIoT as a pathbreaking force in shaping the future of sustainable urban development. It delivers actionable insights, conceptual and operational frameworks, case studies, and policy guidance—equipping diverse stakeholders with the tools to build cities that not only respond to change but also anticipate and shape it. Targeting a broad and cross-disciplinary audience, the book shares state-of-the-art research, presents innovative solutions, and forecasts future trends in urban transformation. As both a seminal reference and a practical resource for researchers, technologists, practitioners, professionals, and policymakers, it provides essential guidance for those engaged in advancing the next frontier in urban computing and intelligence.
R 3,636.34
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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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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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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Generative AI 2.0 and Data Analytics
Data analytics and generative AI (Gen AI) are transformative technologies that play a critical role in modern decision-making and innovation. Data analytics enables organizations to extract actionable insights from vast amounts of structured and unstructured data, driving efficiency, improving customer experiences, and identifying trends. Generative AI, on the other hand, enhances creativity and problem-solving by producing new content, such as text, images, and designs, based on learned patterns. Together, they empower people and organizations to make data-driven decisions, automate complex processes, and unlock new opportunities for growth and innovation.Generative AI 2.0 and Data Analytics explores the intersection between Gen AI and data analytics and addresses its profound efects on industries and organizations across the globe. Highlights of the book include:Deep learning architectures for generative models in business data managementOptimizing human-AI collaboration for strategic decision-making in business practisesBenchmarking practices and evaluation metrics for Generative AI in business data analyticsNot only covering the fundamental concepts, and techniques of generative AI and their practical application, the book also investigates how these techniques foster innovation and improve quality of data in various business domains. It examines a broad range of topics from artificial data generation, security analytics, anomaly detection, reinforcement management, ethical consideration, challenges and future scenarios. The book also features expert opinions and case studies to provide practical direction and valuable insight.
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Prompt Engineering for Generative AI
Large language models (LLMs) and diffusion models such as ChatGPT and Stable Diffusion have unprecedented potential. Because they have been trained on all the public text and images on the internet, they can make useful contributions to a wide variety of tasks. And with the barrier to entry greatly reduced today, practically any developer can harness LLMs and diffusion models to tackle problems previously unsuitable for automation.With this book, you'll gain a solid foundation in generative AI, including how to apply these models in practice. When first integrating LLMs and diffusion models into their workflows, most developers struggle to coax reliable enough results from them to use in automated systems. Authors James Phoenix and Mike Taylor show you how a set of principles called prompt engineering can enable you to work effectively with AI.Learn how to empower AI to work for you. This book explains:The structure of the interaction chain of your program's AI model and the fine-grained steps in betweenHow AI model requests arise from transforming the application problem into a document completion problem in the model training domainThe influence of LLM and diffusion model architecture-and how to best interact with itHow these principles apply in practice in the domains of natural language processing, text and image generation, and code
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R 764.00
Generative Adversarial Networks in Practice
Generative Adversarial Networks (GANs) in Practice is an all-inclusive resource that provides a solid foundation on GAN methodologies, their application to real-world projects, and their underlying mathematical and theoretical concepts. Key features: Guides you through the complex world of GANs, demystifying their intricacies. Accompanies your learning journey with real-world examples and practical applications. Navigates the theory behind GANs, presenting it in an accessible and comprehensive way. Simplifies the implementation of GANs using popular deep learning platforms. Introduces various GAN architectures, giving readers a broad view of their applications. Nurture your knowledge of AI with our comprehensive yet accessible content. Practice your skills with numerous case studies and coding examples. Reviews advanced GANs such as DCGAN, CGAN, CycleGAN, and more, with clear explanations and practical examples. Adapts to both beginners and experienced practitioners, with content organized to cater to varying levels of familiarity with GANs. Connects the dots between GAN theory and practice, providing a well-rounded understanding of the subject. Takes you through GAN applications across different data types, highlighting their versatility. Inspires the reader to explore beyond the book, fostering an environment conducive to independent learning and research. Closes the gap between complex GAN methodologies and their practical implementation, allowing readers to directly apply their knowledge. Empowers you with the skills and knowledge needed to confidently use GANs in your projects.Prepare to deep dive into the captivating realm of GANs and experience the power of AI like never before with Generative Adversarial Networks (GANs) in Practice. This book brings together the theory and practical aspects of GANs in a cohesive and accessible manner, making it an essential resource for both beginners and experienced practitioners.
R 4,313.79
Agentic GraphRAG
What if your AI systems could retrieve information, reason over complex knowledge, plan actions, and continuously learn-all while maintaining enterprise-grade security and compliance? Agentic Graph RAG guides technical leaders, engineers, and architects through the next evolution of generative AI. Combining retrieval-augmented generation (RAG) with graph-based reasoning and agentic capabilities, this guide provides a practical blueprint for building scalable, auditable, and intelligent AI systems.Written by Anthony Alcaraz and Sam Julien, this book demystifies knowledge graphs, graph memory, neural-symbolic reasoning, and agent orchestration through real-world case studies, hands-on design patterns, and production-ready architectures. Readers will learn how to construct graph-native retrieval systems, integrate advanced reasoning into agent workflows, and address enterprise challenges around governance, scalability, and transparency.Design graph-augmented architectures that surpass traditional RAGImplement agents with dynamic memory, planning, and decision-making capabilitiesIntegrate knowledge graphs with large language models for robust, explainable AIDeploy scalable, governable multiagent systems ready for production environments
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Generative Adversarial Learning: Architectures and Applications
This book provides a collection of recent research works addressing theoretical issues on improving the learning process and the generalization of GANs as well as state-of-the-art applications of GANs to various domains of real life. Adversarial learning fascinates the attention of machine learning communities across the world in recent years. Generative adversarial networks (GANs), as the main method of adversarial learning, achieve great success and popularity by exploiting a minimax learning concept, in which two networks compete with each other during the learning process. Their key capability is to generate new data and replicate available data distributions, which are needed in many practical applications, particularly in computer vision and signal processing. The book is intended for academics, practitioners, and research students in artificial intelligence looking to stay up to date with the latest advancements on GANs’ theoretical developments and their applications.
R 6,261.60
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
R 1,824.00
Retrieval Augmented Generation In Production: Architecture, Patterns, And Runbooks
This book is a practical, end-to-end guide to building product-implementation-ready Retrieval-Augmented Generation (RAG) systems for high-stakes domains such as healthcare, finance, education, legal services, and customer support. While Artificial Intelligence (AI) advances rapidly, Large Language Models (LLMs) continue to face challenges with factual consistency and domain-specific accuracy. LLM-based RAG systems address these limitations by integrating live, external knowledge sources to produce grounded, current, and trustworthy outputs.Readers are taken through the full RAG pipeline with MLOps/LLMOps — while tackling 30+ real-world implementation challenges, including data parsing & chunking, prompt rephrasing, retrieval quality, response synthesis, hallucination mitigation, evaluation frameworks, serving & monitoring enhancement, orchestration optimization, and graph-, tabular-, and agentic-RAG patterns. Clear architectures, case studies, and runnable code illustrate how to design, implement, validate, monitor, and scale robust RAG systems.The book also provides a balanced perspective on the current limitations of RAG approaches and their future potential as part of emerging agentic AI ecosystems. Whether you are an engineer, product leader, or researcher, this book equips you to deliver reliable, business-ready AI solutions while staying ahead of rapidly evolving technologies.All supplementary material (i.e. sample codes) are available at https://github.com/junxu-ai/RAGCodes.
R 2,491.00
Cloud-native Architecture (CNA) and Artificial Intelligence (AI) for the Future of Software Engineering: The Principles, Patterns, Platforms and Practices
Cloud-native Architecture (CNA) and Artificial Intelligence (AI) for the Future of Software Engineering: The Principles, Patterns, Platforms and Practices, Volume 141 in the Advances in Computers series, explores the convergence of artificial intelligence, machine learning, and modern software engineering practices. Chapters in this new release include Demystifying the Cloud-native Artificial Intelligence (CNAI) Paradigm, Articulating Machine and Deep Learning Models for Next-Generation Software Development, Delineating Artificial Intelligence (AI) and Its Potentials for Automated Software Engineering, Leveraging Machine and Deep Learning (ML/DL) Algorithms towards AI Models for Automating Software Development, and more.Other sections cover Artificial Intelligence (AI) Technologies and Tools for Accelerated Software Development, Demystifying the Agentic AI Paradigm for Accelerated Software Engineering, Detailing AI Techniques and Tools for Software Engineering Acceleration and Automation, Generative AI Tools for Accelerated Software Engineering, Empowering Software Engineering Automation through Explainable AI, and much more.
R 5,355.00
Hands-On Generative Adversarial Networks with Keras
Develop generative models for a variety of real-world use-cases and deploy them to productionKey FeaturesDiscover various GAN architectures using Python and Keras libraryUnderstand how GAN models function with the help of theoretical and practical examplesApply your learnings to become an active contributor to open source GAN applicationsBook DescriptionGenerative Adversarial Networks (GANs) have revolutionized the fields of machine learning and deep learning. This book will be your first step towards understanding GAN architectures and tackling the challenges involved in training them.This book opens with an introduction to deep learning and generative models, and their applications in artificial intelligence (AI). You will then learn how to build, evaluate, and improve your first GAN with the help of easy-to-follow examples. The next few chapters will guide you through training a GAN model to produce and improve high-resolution images. You will also learn how to implement conditional GANs that give you the ability to control characteristics of GAN outputs. You will build on your knowledge further by exploring a new training methodology for progressive growing of GANs. Moving on, you'll gain insights into state-of-the-art models in image synthesis, speech enhancement, and natural language generation using GANs. In addition to this, you'll be able to identify GAN samples with TequilaGAN.By the end of this book, you will be well-versed with the latest advancements in the GAN framework using various examples and datasets, and you will have the skills you need to implement GAN architectures for several tasks and domains, including computer vision, natural language processing (NLP), and audio processing.Foreword by Ting-Chun Wang, Senior Research Scientist, NVIDIAWhat you will learnLearn how GANs work and the advantages and challenges of working with themControl the output of GANs with the help of conditional GANs, using embedding and space manipulationApply GANs to computer vision, NLP, and audio processingUnderstand how to implement progressive growing of GANsUse GANs for image synthesis and speech enhancementExplore the future of GANs in visual and sonic artsImplement pix2pixHD to turn semantic label maps into photorealistic imagesWho this book is forThis book is for machine learning practitioners, deep learning researchers, and AI enthusiasts who are looking for a perfect mix of theory and hands-on content in order to implement GANs using Keras. Working knowledge of Python is expected.
R 1,498.00
Agentic Architect
Architectural education and the profession are at a crossroads, grappling with the climate crisis, housing shortages, migration, and the omnipresent rise of AI. While AI excels at generating visuals, its true value for architects lies not in replicating existing strengths, but in solving practical, time‑consuming problems. This frees architects to tackle larger societal challenges and leverage their core competencies for the public good.The Agentic Architect explores how current and future architects can harness AI to become indispensable. The book addresses the critical gap between AI’s current (mis)use in academia and practice and how to pivot towards its full, effective adoption. Central to this pivot is the concept of agency. Agentic AI autonomously completes tasks; an agentic architect acts with purpose, meaning, and fulfillment, making informed decisions and contributing unique insights. Many architects, however, are experiencing a crisis of agency, feeling disempowered and immobilized. This book reveals how agency is lost—from school through professional life—and provides concrete solutions for rebuilding it for career growth, workforce relevance, and societal impact. Beyond technical skills, The Agentic Architect emphasizes educating the “whole architect” to prevent demoralization in the face of powerful AI. It focuses on identifying and cultivating agency, showing how to work agentically with AI throughout one’s career. This isn’t a doom‑and‑gloom narrative, but an optimistic, practical guide on “who we need to be” to thrive alongside AI.The Agentic Architect is a high‑level, tool‑agnostic resource for students, faculty, design professionals, and firm leaders.
R 8,572.00
Retrieval Augmented Generation In Production: Architecture, Patterns, And Runbooks
This book is a practical, end-to-end guide to building product-implementation-ready Retrieval-Augmented Generation (RAG) systems for high-stakes domains such as healthcare, finance, education, legal services, and customer support. While Artificial Intelligence (AI) advances rapidly, Large Language Models (LLMs) continue to face challenges with factual consistency and domain-specific accuracy. LLM-based RAG systems address these limitations by integrating live, external knowledge sources to produce grounded, current, and trustworthy outputs.Readers are taken through the full RAG pipeline with MLOps/LLMOps — while tackling 30+ real-world implementation challenges, including data parsing & chunking, prompt rephrasing, retrieval quality, response synthesis, hallucination mitigation, evaluation frameworks, serving & monitoring enhancement, orchestration optimization, and graph-, tabular-, and agentic-RAG patterns. Clear architectures, case studies, and runnable code illustrate how to design, implement, validate, monitor, and scale robust RAG systems.The book also provides a balanced perspective on the current limitations of RAG approaches and their future potential as part of emerging agentic AI ecosystems. Whether you are an engineer, product leader, or researcher, this book equips you to deliver reliable, business-ready AI solutions while staying ahead of rapidly evolving technologies.All supplementary material (i.e. sample codes) are available at https://github.com/junxu-ai/RAGCodes.
R 5,788.00
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.
R 10,257.00
Generative AI for Connected and Autonomous Vehicles
Harness generative AI for connected and autonomous vehicle systems As generative AI reshapes intelligent transportation, engineers and researchers need rigorous frameworks for deploying these technologies in safety-critical systems. This book offers a comprehensive treatment of the models, protocols, and adaptation techniques required to bring large language models, vision-language models, diffusion models, and agentic systems into modern CAV architectures. The book progresses from generative foundations through multimodal representation, language-based interaction, agentic coordination, communication protocols, model adaptation, and validation and safety. Mathematical, probabilistic, and reinforcement-learning prerequisites are consolidated into dedicated appendices, allowing the main chapters to focus on system design and CAV-specific applications. Written by an award-winning researcher with more than 100 patents across connected vehicles and AI, readers will also find: Practical treatment of prompt engineering, retrieval-augmented generation, function calling, and tool use for grounding GenAI in vehicular contextChapters on agent architectures, multi-agent systems, orchestration frameworks, and MCP and A2A protocols for collaborative CAV intelligenceAdaptation and deployment techniques including supervised fine-tuning, reinforcement learning fine-tuning, and knowledge distillation for resource-constrained edge platformsDedicated chapters on simulation platforms, evaluation methodologies, and ethical and safety considerations for deploying generative AI in autonomous vehicles Automotive engineers, AI researchers, and graduate students at the intersection of machine learning and intelligent transportation will find this book indispensable. Readers from OEMs, mobility startups, or academic labs gain the foundations and practices needed to design, adapt, and validate generative AI in connected mobility.
R 4,559.00
R 815.00
