Search results for "Generative AI Infrastructure"
Items we need to order for you
Unlocking Data with Generative AI and RAG
Design intelligent AI agents with retrieval-augmented generation, memory components, and graph-based context integrationFree with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesBuild next-gen AI systems using agent memory, semantic caches, and LangMemImplement graph-based retrieval pipelines with ontologies and vector searchCreate intelligent, self-improving AI agents with agentic memory architecturesBook DescriptionDeveloping AI agents that remember, adapt, and reason over complex knowledge isn’t a distant vision anymore; it’s happening now with Retrieval-Augmented Generation (RAG). This second edition of the bestselling guide leads you to the forefront of agentic system design, showing you how to build intelligent, explainable, and context-aware applications powered by RAG pipelines.You’ll master the building blocks of agentic memory, including semantic caches, procedural learning with LangMem, and the emerging CoALA framework for cognitive agents. You’ll also learn how to integrate GraphRAG with tools such as Neo4j to create deeply contextualized AI responses grounded in ontology-driven data.This book walks you through real implementations of working, episodic, semantic, and procedural memory using vector stores, prompting strategies, and feedback loops to create systems that continuously learn and refine their behavior. With hands-on code and production-ready patterns, you’ll be ready to build advanced AI systems that not only generate answers but also learn, recall, and evolve.Written by a seasoned AI educator and engineer, this book blends conceptual clarity with practical insight, offering both foundational knowledge and cutting-edge tools for modern AI development.*Email sign-up and proof of purchase requiredWhat you will learnArchitect graph-powered RAG agents with ontology-driven knowledge basesBuild semantic caches to improve response speed and reduce hallucinationsCode memory pipelines for working, episodic, semantic, and procedural recallImplement agentic learning using LangMem and prompt optimization strategiesIntegrate retrieval, generation, and consolidation for self-improving agentsDesign caching and memory schemas for scalable, adaptive AI systemsUse Neo4j, LangChain, and vector databases in production-ready RAG pipelinesWho this book is forIf you’re an AI engineer, data scientist, or developer building agent-based AI systems, this book will guide you with its deep coverage of retrieval-augmented generation, memory components, and intelligent prompting. With a basic understanding of Python and LLMs, you’ll be able to make the most of what this book offers.
R 1,628.00
Generative Artificial Intelligence Empowered Learning
This book explores the integration of Generative Artificial Intelligence (GenAI), such as ChatGPT, into educational practices and research methodologies. With the potential to transform traditional learning environments, this book addresses both the opportunities and challenges that come with leveraging GenAI for enhancing teaching and learning experiences.This comprehensive guide delves into multiple aspects of GenAI in education. It covers critical topics such as project-based learning (PBL), personalized learning through AI, AI-driven student assessment, and the role of AI in special education. Furthermore, it examines ethical considerations, the readiness of students and teachers for AI technologies, and case studies on AI’s impact on student outcomes. With insights from educators, researchers, and practitioners from around the globe, the book provides a balanced perspective on both theoretical frameworks and practical implementation of GenAI technologies.Ideal for educators, researchers, and policymakers, this book serves as a practical resource for those looking to effectively integrate AI into their pedagogical and research efforts. It is an essential reference for anyone interested in staying at the forefront of educational innovation and exploring the future possibilities of AI-empowered learning.
R 4,451.35
R 1,008.00
Designing Instruction with Generative AI
Designing Instruction with Generative AI offers a novel set of tools and strategies for leveraging generative AI to create engaging and personalized learning experiences. While instructional designers are a tremendous asset to higher education, not all colleges or universities have the robust staff needed to support all instructors on staff or large student populations. Drawing on a wealth of research, professional experience, and strategic insights, this book equips new and seasoned teaching faculty and trainers with step-by-step directions on how freely accessible artificial intelligence software can assist with all aspects of the course creation and instruction process and cater to the needs of diverse learners. Each chapter offers forward-thinking and empirically validated ways to help faculty create and improve instructional materials, course design, and learning environments while supporting their digital literacies. Rather than introduce AI as a means of outsourcing subject-area expertise, critical thinking, or cognitive processes, the author instead emphasizes its potential to build on traditionally honed knowledge and foundational instructional design practices. From optimizing course alignment and accessibility practices to fostering active learning, motivation, and engagement, educators will find new solutions to common teaching and learning challenges with greater efficiency in time and capacity.The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives (CC BY-NC-ND) 4.0 license.
R 9,299.98
R 1,008.00
R 619.00
AI-Based Forecasting of Solar Photovoltaics Power Generation
The widespread deployment of photovoltaics (PV) systems has emerged as a key element in the global shift toward a carbon-neutral and sustainable energy systems. Driven by a combination of supportive regulatory frameworks, government incentive programs, technical developments, and increasing environmental awareness, the adoption of PV technologies has witnessed remarkable growth in recent years. However, the rapid integration of distributed PV systems into existing electricity grid infrastructure introduces new challenges, particularly concerning voltage regulation, reverse power flow, and congestion within the electricity grid. These issues are intensified when PV systems are integrated without proper strategy. In this context, solar PV power forecasting has become an essential tool for ensuring the reliable and efficient integration of solar PV systems into power systems. Artificial intelligence (AI) and machine learning (ML) offer means to forecast PV power and energy generation based on historical data of PV generation, meteorological data, and/or weather forecasts. AI-Based Forecasting of Solar Photovoltaics Power Generation blends theoretical knowledge with practical case studies, which serves as a comprehensive and timely contribution to the rapidly evolving field of solar PV forecasting. It covers topics such as data collection and processing, solar forecasting based on statistical time-series, machine and deep learning, hybrid and probabilistic approaches, model optimization, hyperparameter tuning, and solar PV forecasting for energy system integration and control. As solar PV systems become increasingly integrated into energy systems, a dedicated book on PV generation forecasting is incredibly useful, making this book an important resource for energy system operators, policymakers, researchers, and students seeking to improve the reliability, resiliency, and efficiency of solar PV systems and the broader systems into which they are integrated.
R 4,274.00
Generative AI Application Integration Patterns
Unleash the transformative potential of GenAI with this comprehensive guide that serves as an indispensable roadmap for integrating large language models into real-world applications. Gain invaluable insights into identifying compelling use cases, leveraging state-of-the-art models effectively, deploying these models into your applications at scale, and navigating ethical considerations.Key FeaturesGet familiar with the most important tools and concepts used in real scenarios to design GenAI appsInteract with GenAI models to tailor model behavior to minimize hallucinationsGet acquainted with a variety of strategies and an easy to follow 4 step frameworks for integrating GenAI into applicationsBook DescriptionExplore the transformative potential of GenAI in the application development lifecycle. Through concrete examples, you will go through the process of ideation and integration, understanding the tradeoffs and the decision points when integrating GenAI.With recent advances in models like Google Gemini, Anthropic Claude, DALL-E and GPT-4o, this timely resource will help you harness these technologies through proven design patterns.We then delve into the practical applications of GenAI, identifying common use cases and applying design patterns to address real-world challenges. From summarization and metadata extraction to intent classification and question answering, each chapter offers practical examples and blueprints for leveraging GenAI across diverse domains and tasks. You will learn how to fine-tune models for specific applications, progressing from basic prompting to sophisticated strategies such as retrieval augmented generation (RAG) and chain of thought.Additionally, we provide end-to-end guidance on operationalizing models, including data prep, training, deployment, and monitoring. We also focus on responsible and ethical development techniques for transparency, auditing, and governance as crucial design patterns.What you will learnConcepts of GenAI: pre-training, fine-tuning, prompt engineering, and RAGFramework for integrating AI: entry points, prompt pre-processing, inference, post-processing, and presentationPatterns for batch and real-time integrationCode samples for metadata extraction, summarization, intent classification, question-answering with RAG, and moreEthical use: bias mitigation, data privacy, and monitoringDeployment and hosting options for GenAI modelsWho this book is forThis book is not an introduction to AI/ML or Python. It offers practical guides for designing, building, and deploying GenAI applications in production. While all readers are welcome, those who benefit most include:Developer engineers with foundational tech knowledgeSoftware architects seeking best practices and design patternsProfessionals using ML for data science, research, etc., who want a deeper understanding of Generative AITechnical product managers with a software development backgroundThis concise focus ensures practical, actionable insights
R 1,693.00
R 703.00
Hybrid Intelligence
Uniquely aimed at teams that think together to solve problems and make decisions, this book explains how to enhance the collective intelligence of a team-size group and combine it with the artificial intelligence (AI) of generative AI to create a hybrid intelligence that is smarter than either one on its own.Boards, committees, and other team-size groups of 5-20 people are the primary problem-solving and decision-making units within organizations, and they form the bridges between organizations, industries, and nations that collaborate on projects. So how can leaders exponentially improve their teams’ capabilities? Assemble the right people, arm them with the right processes, and execute those processes on the right platforms—then add generative AI to enhance those practices. This book describes and synthesizes various ways of increasing a group’s collective intelligence through people, process, and platform practices, and goes on to explain how to augment the practices with generative AI, including how to ask it questions and what questions to ask to obtain superior answers. The resulting upsurge in team capability enables organizations to survive and thrive in a world that is growing progressively more competitive and complex.This book will become the definitive resource for leaders and managers of commercial, government, and nonprofit organizations who want to learn how to significantly improve their teams’ problem-solving and decision-making ability by increasing their collective intelligence and combining it with the artificial intelligence of generative AI.
R 9,324.22
R 489.00
R 570.00
Understanding AI, IoT, 6G and The Infrastructure Revolution
This book offers a comprehensive analysis of the essential technologies that that enable the smooth functioning of the Internet on a global scale, It explains how these technologies work together to enable a variety of online experiences, including sports and entertainment, intelligent shopping, and financial transactions.This invaluable reference provides the reader with a solid understanding of the innovations and advancements in technologies like Artificial Intelligence, 6G, fiber optics, cloud computing, and more! Instead of investigating each technology in isolation, this book shows how they all fit together to reinforce and enhance a particular network capability.This hands-on guide is a unique jargon-free examination of current technologies for general audiences with only a basic knowledge of terminology: it is a foundational text, and no specialized knowledge is needed to understand the technologies discussed.
R 1,368.00
R 1,527.00
R 489.00
R 763.00
Data Lakehouse Revolution
We are racing toward a new kind of AI--faster, smarter, and more connected than ever. At the heart of it is the Data Lakehouse, and Databricks is the engine powering the transformation. Whether you're a data scientist training models, an engineer scaling pipelines, or an architect modernizing your stack, this book gives you what you need to stay ahead. Inside, you'll understand how to unlock the full potential of Machine Learning and Generative AI (GenAI) using Databricks--no fluff, just real tools, real strategies, and real results. From MLFlow and AutoML to Unity Catalog, Retrieval Augment Generation (RAG), and Vector Search, you'll get a complete blueprint for building intelligent systems that actually work in production. With step-by-step labs, industry case studies, and expert tips from someone who's lived through the entire evolution of enterprise AI, this book is your guide to mastering what's next. If you're serious regarding building AI that matters, this is where your journey begins. What You'll Learn Build full-stack ML and GenAI solutions on Databricks Train and track models with MLFlow, AutoML, and tuning strategies Secure and govern data with Unity Catalog Apply explainable, ethical AI techniques Deploy and monitor ML models in real-world pipelines Use RAG and vector search to power GenAI applications Gain confidence with hands-on labs and real enterprise use cases Who This Book Is For Azure administrators, data architects, and data engineers
R 1,368.00
R 428.00
Multimodal Generative AI
This book stands at the forefront of AI research, offering a comprehensive examination of multimodal generative technologies. Readers are taken on a journey through the evolution of generative models, from early neural networks to contemporary marvels like GANs and VAEs, and their transformative application in synthesizing realistic images and videos. In parallel, the text delves into the intricacies of language models, with a particular on revolutionary transformer-based designs. A core highlight of this work is its detailed discourse on integrating visual and textual models, laying out state-of-the-art techniques for creating cohesive, multimodal AI systems. “Multimodal Generative AI” is more than a mere academic text; it’s a visionary piece that speculates on the future of AI, weaving through case studies in autonomous systems, content creation, and human-computer interaction. The book also fosters a dialogue on responsible innovation in this dynamic field. Tailored for postgraduates, researchers, and professionals, this book is a must-read for anyone vested in the future of AI. It empowers its readers with the knowledge to harness the potential of multimodal systems in solving complex problems, merging visual understanding with linguistic prowess. This book can be used as a reference for postgraduates and researchers in related areas.
R 9,347.44
R 1,069.00
R 458.00
Machine Learning in Civil Engineering and Infrastructure Development
Machine Learning in Civil Engineering and Infrastructure Development: A Practitioner's Handbook is a practitioner-oriented handbook that demonstrates, through diverse real-world examples, how civil engineers can integrate machine learning into projects while remaining grounded in engineering judgment, physical understanding, and professional responsibility.The book is organised in three parts, guiding readers from foundational principles to advanced applications. Part I introduces core machine learning concepts and workflows to establish the modelling philosophy that underpins later chapters. Part II explores applications at the material and structural level, including damage detection, durability under extreme conditions, and optimisation of emerging technologies such as 3D printing. Part III expands to system-level challenges and professional practice by covering topics like condition assessment using computer vision, embodied carbon estimation, flood risk management through human–AI collaboration, and critical reflections on ethics, AI tools and the modernisation of the profession.Bridging the gap between complex machine learning methodologies and practical implementation, this book equips civil engineering professionals with the knowledge and skills to stay at the forefront of their industry. Educators will also find case studies for teaching, while researchers can draw inspiration for new datasets, hybrid models, and integration into codes and standards.
R 4,200.58
Role of Generative AI in the Communication Classroom
In an era marked by the rapid integration of Artificial Intelligence (AI) into our lives, the discourse surrounding its implications has intensified. The Role of Generative AI in the Communication Classroom is a pioneering book that delves into the multifaceted dimensions of AI, specifically focusing on OpenAI's revolutionary Chat Generative Pre-Trained Transformer (Chat GPT) and its profound influence on the landscape of communication education. This book navigates the intersection of technology, education, and ethics, shedding light on the imperative need for a collaborative approach to shape AI's evolution. AI's potential to reshape industries and human roles is undeniable. Rooted in the intricate workings of AI and its hallmark, Chat GPT, this book meticulously dissects the dynamic relationship between humans and machines. The discourse extends beyond technology and into the realm of education, asserting that the power to mold AI's trajectory cannot rest solely in the hands of developers. Collaboration involving educators and policymakers is fundamental to ensure AI's ethical and inclusive deployment in education. With an eye on the future, this book advocates for curriculum adaptations that cater to the digital generation, whose lives are intertwined with AI. Addressing the symbiotic interaction of AI and communication, it underscores the significance of communication studies engaging with AI to leverage its potential to foster societal benefits. While revealing AI's transformative potential in the communication classroom, the book conscientiously explores ethical concerns and biases, fostering a balanced approach to its integration. This book is instrumental to the ongoing discourse on AI's role in education. The call for ethical considerations, inclusivity, and regulation serves as a guiding compass for educators, students, developers, and policymakers alike. The book ensures a holistic perspective on AI's integration by addressing privacy, citation, voice ownership, and overall digital ethics. Targeting academic communities and organizations focused on communication, media studies, and AI in education, The Role of Generative AI in the Communication Classroom is an essential read. It aligns seamlessly with the agendas of educational departments across universities, presenting an insightful synthesis of computer science, education, and communication studies. By engaging with relevant conferences and journals, this book invigorates discussions and steers education toward a balanced and responsible AI-driven future.
R 8,919.71
R 519.00
R 641.00
R 397.00
Utilizing Generative AI for Cyber Defense Strategies
As cyber threats become increasingly sophisticated, the need for innovative defense strategies becomes urgent. Generative artificial intelligence (AI) offers a revolutionary approach to enhance cybersecurity. By utilizing advanced algorithms, data analysis, and machine learning, generative AI can simulate complex attack scenarios, identify vulnerabilities, and develop proactive defense mechanisms while adapting to modern-day cyber-attacks. AI strengthens current organizational security while offering quick, effective responses to emerging threats. Decisive strategies are needed to integrate generative AI into businesses defense strategies and protect organizations from attacks, secure digital data, and ensure safe business processes. Utilizing Generative AI for Cyber Defense Strategies explores the utilization of generative AI tools in organizational cyber security and defense. Strategies for effective threat detection and mitigation are presented, with an emphasis on deep learning, artificial intelligence, and Internet of Things (IoT) technology. This book covers topics such as cyber security, threat intelligence, and behavior analysis, and is a useful resource for computer engineers, security professionals, business owners, government officials, data analysts, academicians, scientists, and researchers.
R 12,374.29
R 1,625.99
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
R 702.00
R 2,469.00
R 1,793.30
R 489.00
R 641.00
Generative Artificial Intelligence for Biomedical and Smart Health Informatics
Enables readers to understand the future of medical applications with generative AI and related applications Generative Artificial Intelligence for Biomedical and Smart Health Informatics delivers a comprehensive overview of the most recent generative AI-driven medical applications based on deep learning and machine learning in which biomedical data is gathered, processed, and analyzed using data augmentation techniques. This book covers many applications of generative models for medical image data, including volumetric medical image segmentation, data augmentation, MRI reconstruction, and modeling of spatiotemporal medical data. The book explores findings obtained by explainable AI techniques, with coverage of various techniques rarely reported in literature. Throughout, feedback and user experiences from physicians and medical staff, as well as use cases, are included to provide important context. The book discusses topics including privacy and security challenges in AI-enabled health informatics, biosensor-guided AI interventions in personalized medicine, regulatory frameworks and guidelines for AI-based medical devices, education and training for building responsible AI solutions in healthcare, and challenges and opportunities in integrating generative AI with wearable devices. Topics covered include: Treatment of neurological disorders using intelligent techniques and image-guided and tomography interventions for neuromuscular disordersBio-inspired smart healthcare service frameworks with AI, machine learning, and deep learning, integration of IoT devices, and edge computing in industrial and clinical systemsTraffic management and optimization in distributed environments, patient data management, disease surveillance and prediction, and telemedicine and remote monitoringEducation-driven, peer-to-peer, and service-oriented architectures and transparency and accountability in medical decision-making Generative Artificial Intelligence for Biomedical and Smart Health Informatics is an essential reference for computer science researchers, medical professionals, healthcare informatics, and medical imaging researchers interested in understanding the potential of artificial intelligence and other related technologies in healthcare.
R 5,373.00
Generative AI for Software Development
As the field of Artificial Intelligence continues to evolve, Generative AI has emerged as a powerful tool in the realm of software development. AI models capable of generating high-quality, human-like code can significantly accelerate the software development process, assist in troubleshooting, and even automate tasks traditionally performed by human developers. Generative AI models, such as OpenAI, DeepMind, and others, are already demonstrating the ability to write functional code, generate documentation, and assist in code review. These models are not only reducing the time it takes to develop applications but are also making software development more accessible to a broader audience, including non-technical users. This book is intended to serve as a comprehensive guide for developers, technology leaders, and anyone interested in understanding how to leverage Generative AI in software development. It will cover the underlying technology behind these models, practical use cases, integration into development workflows, and ethical considerations. Additionally, it explores how the democratization of coding through AI will shape the future of software engineering. By providing insights into real-world applications and best practices for integrating generative AI into the software lifecycle, this book empowers readers to stay ahead of the technological curve in a rapidly evolving industry.
R 6,380.00
R 367.00
R 641.00
