Allgemein
Generative AI Security
Up-to-date reference enabling readers to address the full spectrum of AI security challenges while maintaining model utility Generative AI Security: Defense, Threats, and Vulnerabilities delivers a technical framework for securing generative AI systems, building on established standards while focusing specifically on emerging threats to large language models and other generative AI systems. Moving beyond treating AI security as a dual-use technology, this book provides detailed technical analysis of three critical dimensions: implementing AI-powered security tools, defending against AI-enhanced attacks, and protecting AI systems from compromise through attacks like prompt injection, model poisoning, and data extraction. The book provides concrete technical implementations supported by real-world case studies of actual AI system compromises, examining documented cases like the DeepSeek breaches, Llama vulnerabilities, and Google’s CaMeL security defenses to demonstrate attack methodologies and defense strategies while emphasizing foundational security principles that remain relevant despite technological shifts. Each chapter progresses from theoretical foundations to practical applications. The book also includes an implementation guide and hands-on exercises focusing on specific vulnerabilities in generative AI architectures, security control implementation, and compliance frameworks. Generative AI Security: Defense, Threats, and Vulnerabilities discusses topics including: Machine learning fundamentals, including supervised, unsupervised, and reinforcement learning and feature engineering and selectionIntelligent Security Information and Event Management (SIEM), covering AI-enhanced log analysis, predictive vulnerability assessment, and automated patch generationDeepfakes and synthetic media, covering image and video manipulation, voice cloning, audio deepfakes, and AI’s greater impact on information integritySecurity attacks on generative AI, including jailbreaking, adversarial, backdoor, and data poisoning attacksPrivacy-preserving AI techniques including federated learning and homomorphic encryption Generative AI Security: Defense, Threats, and Vulnerabilities is an essential resource for cybersecurity professionals and architects, engineers, IT professionals, and organization leaders seeking integrated strategies that address the full spectrum of Generative AI security challenges while maintaining model utility. Shaila Rana, PhD, is a professor of Cybersecurity, co-founder of the ACT Research Institute, a cybersecurity, AI, and technology think tank, and serves as the Chair of the IEEE Standards Association initiative on Zero Trust Cybersecurity for Health Technology, Tools, Services, and Devices. Rhonda Chicone, PhD, is a retired professor and the co-founder of the ACT Research Institute. A former CSO, CTO, and Director of Software Development, she brings decades of experience in software product development and cybersecurity.
Das Rechtskonforme und effiziente KI-basierte Zeiterfassungssystem
In diesem Buch werden die Anforderungen an ein KI-gestütztes Zeitwirtschaftssystem am Beispiel einer internationalen Bank in Österreich untersucht. Die zentrale Herausforderung liegt darin, die Effizienzpotenziale durch Künstliche Intelligenz (KI) zu nutzen und gleichzeitig die strengen rechtlichen Rahmenbedingungen des Landes zu erfüllen. Die Relevanz des Themas ergibt sich aus der strategischen Bedeutung der Zeitwirtschaft, dem transformativen Potenzial von KI im Personalwesen und dem komplexen regulatorischen Umfeld in Österreich. Mithilfe eines qualitativen Forschungsansatzes und einer Single-Case-Study wurden semi-strukturierte Experteninterviews mit internen Stakeholdern aus den Bereichen HR, KI, Recht, Datenschutz und IT-Sicherheit geführt und durch eine Analyse interner Dokumente ergänzt. Die Ergebnisse zeigen, dass die strikte Einhaltung der rechtlichen Vorgaben – insbesondere des österreichischen Arbeitsrechts, der DSGVO-Grundsätze und der Mitbestimmungsrechte des Betriebsrats – eine unverhandelbare Grundlage darstellt. Ebenso entscheidend ist die proaktive Ausrichtung am EU AI Act für Hochrisiko-Systeme im HR-Bereich. Einleitung.- Literaturrecherche.- Methodik.- Fallstudienanalyse.- Diskussion.- Schlussfolgerung.- Erklärung über den Einsatz von KI.- Literaturverzeichnis.
Mastering Snowflake DataOps with DataOps.live
This practical, in-depth guide shows you how to build modern, sophisticated data processes using the Snowflake platform and DataOps.live—the only platform that enables seamless DataOps integration with Snowflake. Designed for data engineers, architects, and technical leaders, it bridges the gap between DataOps theory and real-world implementation, helping you take control of your data pipelines to deliver more efficient, automated solutions.. You’ll explore the core principles of DataOps and how they differ from traditional DevOps, while gaining a solid foundation in the tools and technologies that power modern data management—including Git, DBT, and Snowflake. Through hands-on examples and detailed walkthroughs, you’ll learn how to implement your own DataOps strategy within Snowflake and maximize the power of DataOps.live to scale and refine your DataOps processes. Whether you're just starting with DataOps or looking to refine and scale your existing strategies, this book—complete with practical code examples and starter projects—provides the knowledge and tools you need to streamline data operations, integrate DataOps into your Snowflake infrastructure, and stay ahead of the curve in the rapidly evolving world of data management. What You Will Learn Explore the fundamentals of DataOps, its differences from DevOps, and its significance in modern data managementUnderstand Git’s role in DataOps and how to use it effectivelyKnow why DBT is preferred for DataOps and how to apply itSet up and manage DataOps.live within the Snowflake ecosystemApply advanced techniques to scale and evolve your DataOps strategy Who This Book Is For Snowflake practitioners—including data engineers, platform architects, and technical managers—who are ready to implement DataOps principles and streamline complex data workflows using DataOps.live. 1. DataOps.- 2. Pillars of True DataOps.- 3. MLOps.- 4. DataOps Best Practices.- 5. Understanding Snowflake.- 6. Introduction to Git.- 7. Getting Started with Git.- 8. Advanced Git Topics.- 9. Introduction to DBT.- 10. Advanced DBT Techniques and Best Practices.- 11. Introduction to DataOps.live Platform.- 12. DataOps.live & DataOps: Better Together.- 13: Essential Elements of DataOps.live.- 14. Getting Started with DataOps.live.- 15. Managing Your Environments.- 16. Build Your First DataOps Pipeline.- 17. Getting Started with SOLE.- 18. Getting Started with MATE.- 19. Managing Multiple Databases with DataOps.Live.- 20. DataOps.live Orchestrators.- 21. Build Only Changed Models.- 22. DataOps.live REST API.- 23. Medallion Architecture.- 24. Kimball Architecture.- 25. DataVault 2.0 Architecture.- 26. Combining Medallion with DataVault 2.0 and Kimball.- 27. Entity-Relationship (ER) Modeling and Beyond.- 28. Event-Driven Data Models.- 29. Graph Data Modeling.
MC Microsoft Certified Azure AI Fundamentals Study Guide
Your comprehensive guide to preparing for the Azure AI Fundamentals certification exam The MC Microsoft Certified Azure AI Fundamentals Study Guide is your one-stop resource for complete coverage of the challenging AI-900 exam. This Sybex Study Guide covers the entirety of the AI-900 objectives. Prepare for the exam smarter and faster with Sybex thanks to efficient and accurate content, including an assessment test that validates and measures exam readiness, real-world examples and scenarios, and challenging chapter review questions. Reinforce and remember what you’ve learned with the Sybex online learning environment and test bank, accessible across multiple devices. Get prepared for the Azure AI Fundamentals exam with Sybex. Coverage of 100% of all exam objectives in this Study Guide means you’ll be ready for: Artificial Intelligence workloads and considerationsFundamental principles of machine learning on AzureComputer vision workloads on AzureNatural Language Processing (NLP) workloads on AzureGenerative AI workloads on Azure ABOUT THE AZURE AI FUNDAMENTALS CERTIFICATION The Azure AI Fundamentals certification demonstrates your knowledge of machine learning and AI concepts and related Microsoft Azure services. This exam is intended for you if you have both technical and non-technical backgrounds. Data science and software engineering experience are not required. However, you would benefit from having awareness of basic cloud concepts and client-server applications. You can use Azure AI Fundamentals to prepare for other Azure role-based certifications. Interactive learning environment Take your exam prep to the next level with Sybex’s superior interactive online study tools. To access our learning environment, simply visit www.wiley.com/ go/sybextestprep, register your book to receive your unique PIN, and instantly gain one year of FREE access after activation to: Interactive test bank with a practice exam to help you identify areas where further review is needed. Get more than 90% of the answers correct, and you’re ready to take the certification exam.100 electronic flashcards to reinforce learning and last-minute prep before the examComprehensive glossary in PDF format gives you instant access to the key terms so you are fully prepared A fast and efficient prep handbook for the Azure AI Fundamentals certification exam In MC Microsoft Certified Azure AI Fundamentals Study Guide: Exam AI-900, experienced software engineer Adora Nwodo walks you through every technical topic you need to understand to succeed on the AI-900 certification exam and build a fundamental understanding of Azure AI features. The Study Guide uses the proven and popular Sybex approach to help you use Azure AI in the real-world, whether you're in a technical or non-technical role. Nwodo offers clear explanations, step-by-step instructions, and visual aids to guide you through essential AI concepts and shows you how to use them in the Azure cloud. You'll learn about: Artificial intelligence concepts and workloadsResponsible AI use in AzureCore concepts in AI models and solutionsIntroduction to machine learning concepts, including data preprocessing, training and validation, algorithms, and moreThe application of machine learning concepts on the Azure cloud platformComputer vision in AzureNatural language processing conceptsGenerative AI conceptsThe Azure OpenAI ServiceExample AI use cases and industry-specific applications The MC Microsoft Certified Azure AI Fundamentals Study Guide highlights best practices for industry newcomers and veterans alike and builds the confidence you need to pass the AI-900 certification exam on your first attempt. Inside the book: Intuitively organized material and additional sample questions that allow you to selectively study the areas in which you need to improveCarefully explained content appropriate for non-technical professionals who interact with AI technologies occasionally or on a daily basisComplimentary access to the superior Sybex online learning environment and test bank, including practice test questions, digital flashcards, and a glossary of key terms Perfect for everyone preparing for the AI-900 certification exam, the Microsoft Certified Azure AI Fundamentals Study Guide is also a must-read for technical and non-technical professionals—especially those working in AI-impacted industries like sales and marketing—who wish to expand their AI skillset and improve their effectiveness at work. ABOUT THE AUTHOR ADORA NWODO is a software engineer, author, and speaker with extensive experience in building and managing large-scale distributed systems. She’s an expert in cloud computing, distributed systems, and DevOps tooling.
Attack Vectors
From early worms to AI powered deepfakes, Attack Vectors chronicles the relentless battle between hackers and defenders. This deep dive into cybersecurity’s evolution unpacks the rise of malware from the Morris Worm to Stuxnet and the cyber syndicates that turned hacking into a billion-dollar underground industry. It explores devastating exploits like CodeRed and HeartBleed, revealing how vulnerabilities become weapons. Meet the visionaries who built the internet’s defenses and the adversaries who found ways to break them. Governments, corporations, and rogue actors all play a role in this ongoing digital war, where data is power, and deception is an art. As cyber-attacks grow more sophisticated, understanding the past is crucial to securing the future. Attack Vectors is essential reading for anyone navigating today’s high-stakes cyber landscape to learn lessons from the past and how solutions today address the most attack vectors predicted in the future. You’ll Learn: Understand the history of cyber-security from the early 1950’s through today.Explore the history of terminology that defines the threat landscape.Examine the history of malware, exploits, breaches, syndicates, and people throughout the last 25 years.Learn how modern cyber-security solutions have been developed to address the evolution of attack vectors.Explore best practices for what to do after a breach and how to manage some of the biggest risks including human beings themselves. Who This Book Is For? New security management professionals, auditors, and information technology staff looking to understand the history of cyber-security. Chapter 1: The History of Attack Vectors.- Chapter 2: Business Justification.- Chapter 3: Definitions.- Chapter 4: Malware.- Chapter 5: Exploits.- Chapter 6: Breaches.- Chapter 7: Regulations.- Chapter 8: People.- Chapter 9: Syndicates.- Chapter 10: Social Engineering.- Chapter 11: Solutions.- Chapter 12: The Human Threat.- Chapter 13: Lateral Movement.- Chapter 14: Return on Investment.- Chapter 15: It’s Not If, But When.- Chapter 16: Supply Chain Attacks.- Chapter 17: Been Hacked?.- Chapter 18: History Lesson.- Chapter 19: Conclusion.
Künstliche Intelligenz in der Marketingabteilung
Das Buch untersucht den aktuellen Einsatz künstlicher Intelligenz in Marketingabteilungen. Basierend auf einem entwickelten Prozessmodell und einer quantitativen Befragung von Fachexpert*innen werden Nutzungshäufigkeit, Anwendungsfelder sowie Chancen und Herausforderungen analysiert. Abschließend werden konkrete Handlungsempfehlungen vorgestellt, um die Nutzung von KI im Marketing gezielt zu fördern. Einleitung.- Theoretische Grundlagen.- Zwischenfazit.- Empirische Untersuchung.- Auswertung der Forschungsergebnisse.- Diskussion.- Fazit.
Cybersecurity Audit Essentials
In a world where cyber threats are more pervasive and sophisticated than ever, this book serves as a trusted companion for professionals charged with protecting critical digital assets. It bridges the gap between theoretical understanding and real-world application, equipping readers with the tools, techniques, and insights to conduct effective cybersecurity audits confidently. The guide takes readers through every stage of the audit process—from planning and scoping to execution, reporting, and follow-up—offering actionable advice at each step. It unpacks the core principles of cybersecurity auditing, such as risk assessment, compliance frameworks, and leveraging advanced tools and technologies. Readers will gain the skills to identify vulnerabilities, evaluate the effectiveness of security measures, and ensure compliance with regulatory requirements. What You Will Learn: Detailed explanations of various audit types, such as: internal, external, compliance, risk-based, and specialized, tailored to meet diverse organizational needs.Step-by-step instructions, checklists, and real-world examples to help readers avoid common pitfalls and adopt proven best practices.Insights into using cutting-edge technologies, including automated audit platforms, SIEM systems, and vulnerability scanners, to enhance audit effectiveness.Clear guidance on navigating key frameworks like GDPR, NIST, ISO 27001, and more, ensuring adherence to industry standards.Tips for prioritizing risk mitigation, crafting impactful audit reports, and fostering continuous improvement in cybersecurity practices. Who This Book Is for: IT auditors, cybersecurity auditors, cybersecurity professionals, and IT specialists who need a practical, hands-on guide to navigate the challenges of cybersecurity auditing in today’s complex digital landscape. Chapter 1: Introduction to Cybersecurity Audits.- Chapter 2: Planning the Cybersecurity Audit.- Chapter 3: Assessing Security Control.- Chapter 4: Compliance and Regulations.- Chapter 5 Introduction to Cyber Risk Management.- Chapter 6 Tools for Network and Cyber Security Audits.- Chapter 7 How to Write an Effective Cybersecurity Audit Report.- Chapter 8 Real-Life Scenarios and Case Studies.
Praxisbuch Large Language Models
Alle Werkzeuge und Techniken für die praktische Arbeit mit LLMsDas Handbuch für das intuitive Verständnis von LLMs: Mit zahlreichen Visualisierungen, die Konzepte schnell zugänglich machenThemen sind die Sprachverarbeitung – Textklassifikation, Suche oder Cluster – und die Sprachgenerierung – vom Prompt Engineering bis zur Retrieval Augmented Generation (RAG)Die Autoren haben mit ihren beliebten Blogs Millionen von Entwickler*innen geholfen, Machine Learning und KI zu verstehenDiese umfassende und anschauliche Einführung in die Welt der LLMs beschreibt sowohl konzeptionelle Grundlagen als auch konkrete Anwendungen und nützliche Tools. Tauchen Sie in das Innenleben von LLMs ein und erkunden Sie ihre Architekturen, Einsatzbereiche, Trainingsmethoden und Feintuning-Techniken. Mit seiner einzigartigen Mischung aus intuitiv verständlichen Illustrationen und praxisbezogenen Erläuterungen ist dieses Buch die ideale Ausgangsbasis für alle, die die Möglichkeiten von KI-Systemen voll ausschöpfen möchten.Sie lernen, vortrainierte Transformer-LLMs von Hugging Face für Anwendungsfälle wie das Verfassen von Texten oder für Inhaltszusammenfassungen einzusetzen. Sie erfahren außerdem, wie Sie Suchsysteme erstellen und vorhandene Bibliotheken und vortrainierte Modelle für Textklassifikation, Suche und Clustering nutzen.Verstehen Sie die Architektur von Transformer-basierten Sprachmodellen, die bei der Textgenerierung und -repräsentation hervorragende Ergebnisse liefernEntwerfen Sie fortgeschrittene LLM-Pipelines, um Textdokumente zu clustern und die darin enthaltenen Themen zu erforschenErstellen Sie semantische Suchmaschinen, die über den Abgleich von Schlagwörtern hinausgehen und auf Methoden wie Dense Retrieval und Reranking basierenLernen Sie, wie Sie generative Modelle optimal einsetzen – vom Prompt Engineering bis hin zur Retrieval Augmented Generation (RAG)Entwickeln Sie ein tieferes Verständnis dafür, wie LLMs trainiert und für spezifische Anwendungen optimiert werden, beispielsweise durch Feintuning generativer Modelle, Contrastive Fine-Tuning und In-Context-LearningJay Alammar ist Direktor und Engineering Fellow bei Cohere, dem Vorreiter bei der Bereitstellung von Large Language Models als API. In dieser Funktion berät und schult er Unternehmen und die Entwicklergemeinschaft bei der Verwendung von Sprachmodellen für praktische Anwendungsfälle. Durch seinen beliebten AI/ML-Blog hat Jay Millionen von Forscherinnen und Softwareengineers geholfen, Tools und Konzepte des Machine Learnings visuell zu verstehen - von den Grundlagen (die in der Dokumentation von Paketen wie NumPy und pandas auftauchen) bis hin zu den neuesten Entwicklungen (Transformers, BERT, GPT-3, Stable Diffusion). Jay ist außerdem Mitgestalter von beliebten Kursen zum Machine Learning und Natural Language Processing auf Deeplearning.ai und Udacity.Maarten Grootendorst ist Senior Clinical Data Scientist bei IKNL (Netherlands Comprehensive Cancer Organization). Er hat Master-Abschlüsse in Organisationspsychologie, klinischer Psychologie und Data Science, die er nutzt, um komplexe Konzepte des Machine Learning einem breiten Publikum zu vermitteln. Mit seinen beliebten Blogs hat er Millionen von Leserinnen und Lesern erreicht. Darin erklärt er die Grundlagen der künstlichen Intelligenz - oft aus psychologischer Sicht. Er ist Autor und Betreuer mehrerer Open-Source-Pakete, die sich auf die Stärke von Large Language Models stützen, wie BERTopic, PolyFuzz und KeyBERT. Seine Pakete werden millionenfach heruntergeladen und von Datenexperten und Organisationen weltweit genutzt.
Learn Model Context Protocol with Python
Learn Model Context Protocol with Python introduces developers, architects, and AI practitioners to the transformative capabilities of Model Context Protocol (MCP), an emerging protocol designed to standardize, distribute, and scale AI-driven applications. Through the lens of a practical project, the book tackles the modern challenges of resource management, client-server interaction, and deployment at scale. Drawing from Christoffer's expertise as a published author and tutor at the University of Oxford, you’ll explore the components of MCP and how they streamline server and client development. Next, you’ll progress from building robust backends and integrating LLMs into intelligent clients to interacting with servers via tools such as Claude for desktop and Visual Studio Code agents. The chapters help you understand how to describe the capabilities of hosts, clients, and servers, facilitating better interoperability, easier integration, and clearer communication between different components. The book also covers security best practices and building for the cloud, ensuring that you're ready to deploy your MCP-based apps. Each chapter enables you to develop hands-on skills for building and operating MCP-based agentic apps. The Python primer at the end rounds out the practical toolkit, making this book essential for any team building AI-native applications today.
Thinking With Machines
Praise for THINKING WITH MACHINES “Clear, urgent, and deeply human. Dhar’s personal journey through AI makes Thinking with Machines feel less like science fiction and more like a mirror—helping us reflect on trust, resilience, and what it means to stay grounded in a world racing ahead.” —DAVID KO, CEO, Calm “At last, a book by one of the world’s leading AI researchers and practitioners that is both cutting edge and easy-to-read. Part history of the field, part handbook for the non-expert, part AI governance manifesto, and part author’s life journey, Thinking with Machines is indispensable reading for professionals, policymakers, and concerned citizens grappling with the paradigm shift of our times.” —PAUL SHEARD, Economist and Author of The Power of Money “In a world of wannabe AI experts, Vasant Dhar shines as someone who has played the long game, through successive AI hype cycles and winters. In this remarkable book, he provides a calm and reasoned way for us to decipher the world of AI and a playbook for dealing with the most important innovation of our time.” —NANDAN NILEKANI, Co-Founder and Chairman, Infosys, and Founding Chairman, UIDAI (Aadhaar) “In a world filled with AI experts who alternate between relentless hype and imminent doom, Vasant stands out as someone who sees shades of gray in this debate, and has the expertise and good sense to gauge AI’s strengths and limits. I strongly recommend his new book, Thinking with Machines, to everyone who is involved or interested in how AI will change the way we live and work, as I think it will.” —ASWATH DAMODARAN, New York University Professor and Valuation Guru “Vasant Dhar has a unique perspective: building trading algorithms that compound tiny edges into fortunes, then watching AI evolve from narrow applications to a general-purpose technology. In Thinking with Machines, he reveals when to trust algorithms, when to stay skeptical, and how to govern AI before it governs us. A must-read for our AI-powered future.” —ERIK BRYNJOLFSSON, Professor at Stanford University and Co-Founder of Workhelix, Inc. We are entering a brave new world, thanks to AI. We must shape this future to the advantage of everyone, and not just a select few. Thinking with Machines: The Brave New World of AI tells the story of AI from its very beginnings through the eyes of Vasant Dhar, currently Robert A Miller Professor at the Stern School of Business, and Professor of Data Science at New York University. Professor Dhar lived through the invention of AI algorithms and their various permutations until today. He brought AI to Wall Street in the 90s and was the first to teach AI at NYU Stern. Through his story and the lessons that it reveals, we learn about AI’s progress and reversals, its promises and dangers, and what we need to address before the machine gets away from us. Thinking with Machines is essential reading for AI enthusiasts and learners at all levels seeking knowledge on the greatest technological advancement of our time. VASANT DHAR is Robert A. Miller Professor at the Stern School of Business and Professor of Data Science at New York University. He is also the host of the Brave New World podcast and a frequent contributor to scientific journals and mainstream media about artificial intelligence.
Proactive Cyber Threat Intelligence
This book explores innovative methods to enhance cybersecurity by leveraging official and unofficial information sources on the web. While traditional approaches like Cyber Threat Intelligence (CTI) and Security Information and Event Management (SIEM) rely on past breaches, this work emphasizes a proactive stance, utilizing Open Source Intelligence (OSINT) to predict and prepare for emerging threats. Drawing from crisis informatics and data mining, the research introduces automated approaches for collecting, enriching, and analyzing cybersecurity information across diverse web sources, providing security teams with tools to identify emerging threats while reducing manual workload. For security professionals and researchers, this work demonstrates how automation can enhance human expertise in cybersecurity, paving the way for more robust and proactive threat detection. Introduction.- Research Background and Field.- Research Methodology and Context.- Findings.- Discussion.- The Notion of Relevance in Cybersecurity: A Categorization of Security Tools and Deduction of Relevance Notions.- A Domain-Adapted Language Model for the Cybersecurity Domain.- Bandit on the Hunt: Dynamic Crawling for Cyber Threat Intelligence.- Navigating the Shadows: Evaluating the Dark Web for Cyber Threat.- OVANA: An Approach to Analyze and Improve the Information Quality of Vulnerability Databases.- Common Vulnerability Scoring System Prediction based on Open Source Intelligence Information Sources.- Reducing Information Overload: Because Even Security Experts Need to Blink.
Level Up with Azure AI Foundry
Harness the power of AI through Microsoft’s Azure platform, AI Foundry. Azure AI Foundry includes a versatile and powerful suite of tools designed to cater to the needs of developers, data scientists, and organizations aiming to leverage AI for transformative outcomes. This book demystifies the Azure AI Foundry ecosystem by offering a comprehensive overview of its foundational concepts, tools, and services. The book begins with an overview of generative AI, detailing its concepts, architecture, and differences from traditional AI. You will then learn Azure AI Foundry Prompt Flow, covering its features, setup, and navigation. In addition, you will explore how to create, debug Prompt Flow, and build intelligent multi-modal AI applications. You will also understand responsible AI and governance with AI Foundry. After reading this book, you will understand how to utilize Azure’s AI tools to create impactful solutions that can drive efficiency, enhance decision making, and unlock new opportunities in your field. What You Will Learn: Get up to speed on the fundamentals of generative AIUnderstand the essential components of Azure AI Foundry, including Prompt Flow, multi-modal applicatios, and other key tools for building intellgent applicationsIntegrate AI capabilities into existing systems and workflowsKnow the latest trends and innovations in AI and how Azure AI Foundry can be used to drive cutting-edge solutions across various industriesManage security and governance of your AI application with AI Foundry Who This Book Is For Data professionals, AI enthusiasts, enterprise leaders, and aspiring data scientists keen on exploring Azure AI Foundry Chapter 1: Getting Started with Azure AI Foundry.- Chapter 2: Exploring Azure AI Foundry.- Chapter 3: Building with Prompt Flow.- Chapter 4: Bringing Your Own Data to AI Foundry.- Chapter 5: Exploring Multimodal AI Capabilities.- Chapter 6: Deploying, Monitoring and Ensuring AI Safety.
Securing Cyber-Physical Systems
Protect critical infrastructure from emerging threats with this essential guide, providing an in-depth exploration of innovative defense strategies and practical solutions for securing cyber-physical systems. As industries increasingly rely on the convergence of digital and physical infrastructures, the need for robust cybersecurity solutions has grown. This book addresses the key challenges posed by integrating digital technologies into critical physical systems across various sectors, including energy, healthcare, and manufacturing. Focusing on innovative defence strategies and practical solutions, this book provides an in-depth exploration of the vulnerabilities and defence mechanisms essential to securing cyber-physical systems. The book is designed to equip researchers, cybersecurity professionals, and industry leaders with the knowledge to protect critical infrastructure from emerging digital threats. From understanding complex vulnerabilities to implementing secure system designs, this volume offers a comprehensive guide to fortifying and securing the systems that shape our modern, interconnected world. Readers will find the volume: Explores the evolving threat landscape, encompassing potential attacks on critical infrastructure, industrial systems, and interconnected devices;Examines vulnerabilities inherent in cyber-physical systems, such as weak access controls, insecure communication channels, and the susceptibility of physical components to digital manipulation;Uses real-world case studies to introduce strategies for assessing and quantifying the cybersecurity risks associated with cyber-physical systems, considering the potential consequences of system breaches;Provides an overview of cybersecurity measures and defense mechanisms designed to fortify cyber-physical systems against digital threats, including intrusion detection systems, encryption, and security best practices;Discusses existing and emerging regulatory frameworks aimed at enhancing cybersecurity in critical infrastructure and physical systems. Audience Researchers, cybersecurity professionals, information technologists and industry leaders innovating infrastructure to protect against digital threats. K. Ananthajothi, PhD is a Professor in the Department of Computer Science and Engineering at Rajalakshmi Engineering College, Chennai, India. He has published one book, two patents, and several research papers in international journals and conferences. His research focuses on machine learning and deep learning. S. N. Sangeethaa, PhD is a Professor in the Department of Computer Science and Engineering at the Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu, India. She has published seven books, more than 25 research articles in reputable journals, and more than 50 papers in national and international conferences. Her research interests include artificial intelligence, machine learning, and image processing. D. Divya, PhD is an Assistant Professor in the Department of Computer Science and Engineering at Misrimal Navajee Munoth Jain Engineering College, Chennai, India. She has published several papers in international journals. Her research focuses on data mining and machine learning. S. Balamurugan, PhD is the Director of Albert Einstein Engineering and Research Labs and the Vice-Chairman of the Renewable Energy Society of India. He has published more than 60 books, 300 articles in national and international journals and conferences, and 200 patents. His research interests include artificial intelligence, augmented reality, Internet of Things, big data analytics, cloud computing, and wearable computing. Sheng-Lung Peng, PhD is a Professor and the Director of the Department of Creative Technologies and Product Design at the National Taipei University of Business, Taiwan. He has published more than 100 research papers in addition to his role as a visiting professor and board member for several international universities and academic groups. His research interests include designing and analyzing algorithms for bioinformatics, combinatorics, data mining, and networks.
Digital Immune System
Equip yourself with the knowledge to build a resilient digital infrastructure by understanding how the digital immune system leverages advanced technologies to proactively defend against cyber threats. The concept of the digital immune system represents a significant advancement in cybersecurity, reflecting the industry’s shift toward more intelligent and adaptive defense mechanisms. Drawing inspiration from biological immune systems, the digital immune system offers a solution that naturally adapts and responds to evolving threats. This book explores this evolving landscape, focusing on the integration of advanced technologies like artificial intelligence, machine learning, and automation to build resilient digital infrastructures. It delves into how these technologies can create a self-sustaining ecosystem that detects, responds to, and mitigates cyber threats in real-time and highlights the significance of predictive analytics and behavioral analysis in identifying potential threats before they materialize. Through case studies and real-world examples, the book demonstrates how organizations have successfully implemented digital immune systems to protect their assets and maintain operational integrity in an increasingly hostile digital environment. Additionally, the book addresses the challenges and ethical considerations involved in deploying a digital immune system. It discusses the balance between security and privacy, the potential for false positives, and the need for transparency in automated decision-making processes. By providing a comprehensive overview of the current state and prospects of digital immunity, the book serves as a crucial resource for cybersecurity professionals, IT leaders, and anyone interested in understanding the next-generation of digital defense mechanisms. Readers will find the book: Introduces the emergence of the digital immune system; Discusses different applications of the digital immune system across various industries; Comprehensively covers the fundamentals of the digital immune system for different domains, presenting state-of the-art analysis and real-world case studies; Examines the importance of a proactive approach to cybersecurity, emphasizing the need for organizations to move beyond traditional reactive measures. Audience Research scholars in computer science and AI, IT professionals, network administrators, cybersecurity and blockchain technology experts, engineering students and government research agencies looking to the future of cybersecurity. Sujata Priyambada Dash, PhD is an Assistant Professor in the Department of Management at the Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India. She has published one edited book, several book chapters, and numerous articles in international journals and conferences. Vaibhav Mishra, PhD is an Associate Professor at the ICFAI Business School Hyderabad, Telangana, India. He has published research articles in international journals of repute and edited books on blockchain, AI, and machine learning. Bijeta Shaw, PhD is an Assistant Professor in the Operations and IT Department at ICFAI Business School Hyderabad, Telangana, India. She has authored numerous research articles in international journals and coordinated conferences. Sandeep Kumar Panda, PhD is a Professor and the Associate Dean in the School of Science and Technology at the ICFAI Foundation for Higher Education Hyderabad, Telangana, India. He has published six edited books, several book chapters, and more than 80 articles in international journals and conferences. S. Balamurugan, PhD is the Director of Research and Development at Intelligent Research Consultancy Services. He has published about 50 books, more than 300 articles in international journals and conferences, and 55 patents.
Industrial Control System (ICS) and Operational Technology (OT) Security
Operational Technology (OT) refers to hardware and software used to monitor, control, and manage industrial processes, infrastructure, and assets across sectors like manufacturing, energy, transportation, and utilities. It includes systems such as Industrial Control Systems (ICS), SCADA, PLCs, and DCS. ICS Security is the protection of industrial automation and control systems that manage critical operations like power generation, water treatment, and oil and gas production. Its goal is to safeguard confidentiality, integrity, and availability while mitigating risks from cyber threats, disruptions, and unauthorized access. With this book, our goal is to help readers understand how to secure complex industrial environments by providing a clear introduction to ICS and OT security. We will begin by explaining what ICS and OT are, how they differ from traditional IT, and why those differences matter. From there, the discussion will focus on the increasing importance of ICS/OT security—particularly in the context of IT and OT convergence, where systems and data are becoming more interconnected. You will also gain an understanding of the key components that make up Industrial Control Systems and how they work together to monitor and control industrial operations. These components—including SCADA, PLC, HMI, and DCS—are essential for ensuring operational efficiency, safety, and security in industries like manufacturing, energy, and critical infrastructure. What you Will Learn · The major differences between IT and OT security · Key components of Industrial Control Systems · The major challenges in ICS/OT security · Why ICS/OT security is very important in the digitalization era · The most common and widely used ICS/OT standards, tools, and frameworks Who This Book is for: Beginners of OT Security environment. The book assumes you have an understanding of the IT Security environment and will be a great book for those who wish to learn the major differences and key elements of ICS and OT Security. Chapter 1: Introduction of Industrial Control System (ICS) and Operational Technology (OT) Security.- Chapter 2: Key Components of Industrial Control Systems.- Chapter 3: Challenges in ICS/OT Security.- Chapter 4: ICS and OT Security tools and framework.
IAM and PAM Cybersecurity
In today’s digital age, organizations face growing cyber threats targeting user identities and access credentials. Identity and Access Management (IAM) helps secure identities, manage privileges, and enforce security policies—making it essential for zero-trust security, compliance, and efficiency. Privileged Access Management (PAM), a specialized branch of IAM, focuses on protecting and monitoring privileged accounts such as administrators, root users, and service accounts. Because these accounts have elevated access, they are prime targets for cyberattacks. Together, IAM and PAM safeguard access to systems, applications, and data, playing a vital role in cloud security, IoT, and enterprise environments by preventing unauthorized access and mitigating insider threats. Readers will learn: · Why securing identities is critical today · Key differences between IAM and PAM · Major access management risks and challenges · Common IAM and PAM solutions (e.g., Azure AD, Okta, CyberArk, BeyondTrust, SSH Communications Security) Chapter 1: IAM, Securing Identities in the Digitalization Era.- Chapter 2: PAM, Protecting Privileged Accounts and Access Management.- Chapter 3: IAM and PAM risks, impacts, and challenges.- Chapter 4: IAM and PAM tools and frameworks.
Suchmaschinen-Optimierung für Dummies
Mit SEO bei Google ganz oben landen und für mehr Traffic auf Ihrer Website sorgen Ihre Website ist gerüstet für zahlreiche Besucher, und Sie hoffen auf hohe Umsätze oder viele Interaktionen? Dann ist es Zeit für SEO. Dieses Buch zeigt Ihnen, wie Ihre Website von Suchmaschinen besser gefunden wird und wie Sie bei Google ganz oben landen. Finden Sie die richtigen Suchbegriffe und stimmen Sie Ihre Inhalte darauf ab, optimieren Sie Ihre Website, die Ladezeiten, Texte und Snippets und unterstützen Sie den Google-Crawler beim Erfassen der Seiten. Zahlreiche Beispiele und Anekdoten aus der Praxis sorgen nicht nur für ein gutes Ranking, sondern auch für Spaß beim Lesen. Sie erfahren Wie Suchmaschinen-Optimierung funktioniertWie Sie Ihre Website für SEO optimierenWelche hilfreichen Tipps und Tricks es rund um das Thema SEO gibtWie Künstliche Intelligenz Sie bei SEO unterstützen kann Julian Dziki beschäftigt sich seit 2008 mit Suchmaschinen-Optimierung. Er ist Gründer und Geschäftsführer von Seokratie, einer Agenturgruppe für SEO und Online-Marketing (München, Kitzbühel). Er hält Vorträge, postet in seinem SEO-Blog, organisiert Meetups und zertifiziert beim BVDW Fachkräfte für SEO.
KI und Recht für Dummies
Rechtssicherheit bei der Entwicklung und Nutzung von KI gewinnen Lassen auch Sie sich von KI unterstützen – Content von ChatGPT erstellen, Grafiken von Midjourney gestalten und Fragen von CoPilot beantworten? Oder nutzen Sie schon KI-Modelle in Ihren eigenen Systemen und bieten Kundenservice via Chatbot mithilfe künstlicher Intelligenz an? Haben Sie dabei Datenschutz, Urheberrecht, die Vorgaben der KI-Verordnung und Haftungsfragen im Blick? Dieses Buch erklärt Ihnen verständlich und praxisnah die rechtlichen Aspekte der Nutzung künstlicher Intelligenz und hilft Ihnen dabei, rechtliche Fallstricke zu erkennen und Risiken zu minimieren – damit Sie die Effizienz und Innovationskraft von künstlicher Intelligenz rechtssicher nutzen können! Sie erfahren Was die KI-Verordnung für Ihre Nutzung von KI bedeutetWelche Rolle das Urheberrecht bei der Contenterstellung mit KI spieltWas für das Training von KI-Systemen giltWie Sie Chatbots rechtssicher einsetzen Dr. Kristina Schreiber ist Rechtsanwältin und Partnerin bei Loschelder Rechtsanwälte in Köln, spezialisiert auf die Beratung zu KI, ITK und Daten. Marlene Schreiber ist Rechtsanwältin und Partnerin bei HÄRTING Rechtsanwälte in Berlin, spezialisiert auf die Beratung zu KI, IT und digitalen Geschäftsmodellen.
AI-Driven Software Testing
AI-Driven Software Testing explores how Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing quality engineering (QE), making testing more intelligent, efficient, and adaptive. The book begins by examining the critical role of QE in modern software development and the paradigm shift introduced by AI/ML. It traces the evolution of software testing, from manual approaches to AI-powered automation, highlighting key innovations that enhance accuracy, speed, and scalability. Readers will gain a deep understanding of quality engineering in the age of AI, comparing traditional and AI-driven testing methodologies to uncover their advantages and challenges. Moving into practical applications, the book delves into AI-enhanced test planning, execution, and defect management. It explores AI-driven test case development, intelligent test environments, and real-time monitoring techniques that streamline the testing lifecycle. Additionally, it covers AI’s impact on continuous integration and delivery (CI/CD), predictive analytics for failure prevention, and strategies for scaling AI-driven testing across cloud platforms. Finally, it looks ahead to the future of AI in software testing, discussing emerging trends, ethical considerations, and the evolving role of QE professionals in an AI-first world. With real-world case studies and actionable insights, AI-Driven Software Testing is an essential guide for QE engineers, developers, and tech leaders looking to harness AI for smarter, faster, and more reliable software testing. What you will learn: • What are the key principles of AI/ML-driven quality engineering • What is intelligent test case generation and adaptive test automation • Explore predictive analytics for defect prevention and risk assessment • Understand integration of AI/ML tools in CI/CD pipelines Who this book is for: Quality Engineers looking to enhance software testing with AI-driven techniques. Data Scientists exploring AI applications in software quality assurance and engineering. Software Developers – Engineers seeking to integrate AI/ML into testing and automation workflows. Part 1.- Chapter 1: The Role of AI and ML in Modern Software Testing.- Chapter 2: Software Testing from Manual to AI-Driven Automation.- Chapter 3: Quality Engineering in the Age of AI.- Chapter 4: Comparing Traditional and AI-Driven Testing.- Chapter 5: SDLC vs STLC Understanding the Basics.- Chapter 6: The Testing Pyramid in Traditional and AI-Driven Testing.- Part 2.- Chapter 7: Revolutionizing Test Planning and Execution with AI/ML.- Chapter 8: Intelligent Test Case Development with AI/ML.- Chapter 9: AI/ML-Driven Test Setup and Management.- Chapter 10: AI/ML in Smart Defect Management and Resolution.- Chapter 11: Test Closure with AI/ML Reporting and Continuous Feedback.- Chapter 12: Eliminating Testing Gaps with AI/ML Precision.- Part 3.- Chapter 13: Scaling Software Testing with AI/ML.- Chapter 14: Enhancing CI/CD Pipelines with AI/ML Driven Testing.- Chapter 15: AI/ML for Real-Time Test Execution Monitoring.- Chapter 16: Predicting Failures with AI/ML Analytics.- Chapter 17: The Future of QE with AI-Driven Testing.- Chapter 18. Next Steps to Implementing AI-Driven QE.
Taking Testing Seriously
Explore and implement the Rapid Software Testing approach with expert guidance from industry leaders Discover the secrets to mastering software testing in Taking Testing Seriously: The Rapid Software Testing Approach. This comprehensive guide offers an in-depth exploration of the Rapid Software Testing (RST) methodology, providing practical insights into implementing it on your projects. Led by the innovators who crafted RST over more than 30 years, this is the first comprehensive description of RST ever put in writing. It is your ticket to enhancing your testing skills, finding bugs that matter, and building your reputation as a tester. Taking Testing Seriously dives deep into the scientific foundations of testing, challenging conventional assumptions. With contributions from seasoned software testing professionals, expert interviews, and practical case studies and examples, the book covers essential topics such as how to think like a tester, what it means to be a responsible tester, how to develop testing skills, and how to apply AI to the testing process. It also addresses common pitfalls like "automation bias" and the industry's odd obsession with test cases. Whether you're a tester, programmer, or a technical manager, Taking Testing Seriously is an invaluable resource that will elevate your grasp of the fundamentals and timeless dynamics in software testing. This essential guide will help you learn to appreciate and develop your tacit knowledge while finding opportunities to use tools to help you succeed across all the seasons and contexts of testing. Elevate your software testing approach with a methodology from industry leaders who dedicated their careers to studying, practicing, and teaching the craft of testing. Dive into the world of expert software testing with Taking Testing Seriously: The Rapid Software Testing Approach. This book arms software professionals with the knowledge required to master the Rapid Software Testing (RST) methodology. Written by two co-creators of the RST approach and supplemented by material from respected testers who offer valuable insights, it is an essential read for anyone seeking excellence in the craft of testing. Taking Testing Seriously offers a rich exploration of the RST methodology through insightful interviews, expert discussions, practical case studies, and real-world examples. It thoroughly covers key topics such as the psychology of testing, the science behind it, the fundamental processes and heuristics of test design, and much more. This book provides concrete strategies for addressing common software testing challenges and integrating new solutions with existing systems. You will: Gain insights from experienced software testers through in-depth interviews and expert adviceLearn how to the skills of testing are needed more than ever in an AI-powered IT industryDiscover strategies to leverage the latest automation technologies to refine and expedite your testing processesEscape from the echo chamber of “best practices” and learn to think critically about testing Focusing on the mindset and skillset of excellent testing, Taking Testing Seriously is a must-have resource for software engineers and technical leaders eager to improve their testing proficiency. Whether you are looking to advance your career or simply want to ensure your next project meets the highest standards of quality, this book provides the tools you need. Order your copy today and start transforming the way you and your team approach software testing. JAMES BACH is the creator of the Rapid Software Testing methodology, founder and CEO of software testing and training company Satisfice, and the co-author of the critically acclaimed bestseller Lessons Learned in Software Testing (Wiley 2001). MICHAEL BOLTON has over 30 years of experience testing, developing, managing, and writing about software. For over 20 years, he has led DevelopSense, a Toronto-based testing and development consultancy. In 2006, he became co-creator (with creator James Bach) of Rapid Software Testing (RST).
Data Engineering for Beginners
A first-hand, expert guide to the technical skills and industry insights you need to launch your data engineering career In Data Engineering for Beginners, big-data expert Chisom Nwokwu delivers a comprehensive, beginner-friendly guide for early-career engineers, analysts, and industry professionals eager to master the fundamentals of data engineering. As AI continues to transform industries, this book clearly shows how data engineering sits at the heart of building intelligent systems. Through real-world scenarios and hands-on insights, you’ll gain a head start in a future-proof career built on data. Nwokwu offers a clear and accessible intro to the core pillars of data engineering, including database fundamentals, data warehouses, data lakes, data pipelines, and the crucial topics of data quality, security, and governance. In addition to easy-to-follow explanations of key technical concepts, she also offers practical insights into data engineering career paths, breaking down the essential skills and tools you’ll need to ace an interview and land your first role. Data Engineering for Beginners is an essential resource for anyone looking to build or expand their technical skillset in data engineering. Whether you’re launching a new career as a data engineer, data analyst, or data scientist, or you’re a marketing specialist, financial analyst, or AI practitioner. This book serves as a practical and accessible starting point. It’s not just for tech professionals; it’s a valuable tool for anyone working with data or aiming to contribute to modern, data-driven systems. A hands-on technical and industry roadmap for aspiring data engineers In Data Engineering for Beginners, big data expert Chisom Nwokwu delivers a beginner-friendly handbook for everyone interested in the fundamentals of data engineering. Whether you're interested in starting a rewarding, new career as a data analyst, data engineer, or data scientist, or seeking to expand your skillset in an existing engineering role, Nwokwu offers the technical and industry knowledge you need to succeed. The book explains: Database fundamentals, including relational and noSQL databasesData warehouses and data lakesData pipelines, including info about batch and stream processingData quality dimensionsData security principles, including data encryptionData governance principles and data frameworkBig data and distributed systems conceptsData engineering on the cloudEssential skills and tools for data engineering interviews and jobs Data Engineering for Beginners offers an easy-to-read roadmap on a seemingly complicated and intimidating subject. It addresses the topics most likely to cause a beginning data engineer to stumble, clearly explaining key concepts in an accessible way. You'll also find: A comprehensive glossary of data engineering termsCommon and practical career paths in the data engineering industryAn introduction to key cloud technologies and services you may encounter early in your data engineering career Perfect for practicing and aspiring data analysts, data scientists, and data engineers, Data Engineering for Beginners is an effective and reliable starting point for learning an in-demand skill. It's a powerful resource for everyone hoping to expand their data engineering Skillset and upskill in the big data era. CHISOM NWOKWU, is a Big-Data Engineer, Multi-Published Author, and Creator specialising in the design and development of scalable data platforms for teams. She’s an Azure Certified Data Engineer Associate who has worked with large international firms, including Microsoft and Bank of America.
AWS Certified AI Practitioner Study Guide
Your complete guide to succeeding on the AWS Certified AI Practitioner exam The AWS® Certified AI Practitioner Study Guide is a comprehensive resource for complete coverage of the AIF-C01 exam. This Sybex Study Guide covers all of the AIF-C01 objectives. You’ll prepare for the exam smarter and faster with Sybex thanks to accurate content including, assessment tests that validate and measure exam readiness, real-world examples and scenarios, practical exercises, and chapter review questions. Retain what you’ve learned with the Sybex online learning environment and test bank, accessible across multiple devices. Get prepared for the AWS Certified AI Practitioner exam with Sybex. Coverage of 100% of all exam objectives in this Study Guide means you’ll be ready for: Fundamentals of AI and MLFundamentals of Generative AIApplications of Foundation ModelsGuidelines for Responsible AISecurity, Compliance, and Governance for AI Solutions ABOUT THE AWS CERTIFIED AI PRACTITIONER CERTIFICATION The AWS Certified AI Practitioner certification validates your expertise in in artificial intelligence, machine learning, and generative AI. The qualification demonstrates your familiarity with foundational AI and ML principles, responsible AI practices, and AWS services and tools. Interactive learning environment Take your exam prep to the next level with Sybex’s superior interactive online study tools. To access our learning environment, simply visit www.wiley.com/go/sybextestprep, register your book to receive your unique PIN, and instantly gain one year of FREE access after activation to: Interactive test bank with 2 practice exams to help you identify areas where further review is needed. Get more than 90% of the answers correct, and you’re ready to take the certification exam.100 electronic flashcards to reinforce learning and last-minute prep before the examComprehensive glossary in PDF format gives you instant access to the key terms so you are fully prepared Quickly and intelligently prepare for the AIF-C01 exam and succeed in your first role as an AWS AI practitioner In AWS Certified AI Practitioner Study Guide: Foundational (AIF-C01) Exam, a team of veteran AWS and AI specialists walks you through an efficient and effective path to success on the challenging AIF-C01 exam. You'll demonstrate your knowledge and effectiveness with artificial intelligence (AI) and machine learning (ML), generative AI technologies, and their associated AWS services and tools, independent of any specific job role or industry title. You'll discover how to understand the appropriate uses of AI, ML, and generative AI technologies, when to use the various products and tools available, and how to ask relevant questions within your organization. The book covers the fundamentals of AI, ML, and generative AI, applications of foundation models, guidelines for responsible AI use, and security, compliance, and governance for AI solutions. Inside the book: Complimentary access to the online Sybex learning environment, including practice tests and exams, chapter review questions, flashcards, and a searchable key term glossaryMaterial to help you become familiar with Amazon Web Services tools, including EC2, S3, Lambda, and SageMakerExplanations of Amazon Web Services infrastructure, including discussions of AWS regions, availability zones, and edge locations Perfect for anyone interested in building AI/ML solutions on Amazon Web Services, AWS Certified AI Practitioner Study Guide is a must-read resource for everyone planning to take the AIF-C01 exam, as well as those interested in working—or already working—in this dynamic and exciting field. ABOUT THE AUTHORS VIKRAM ELANGO is a Senior Generative AI Specialist Solutions Architect at AWS. VIVEK GANGASANI is a Sr. Generative AI Specialist Architect and Tech Lead for Inference. SHREYAS SUBRAMANIAN is a Principal Data Scientist at AWS, inventor with 20+ AI patents, and author of three AI books and 50+ technical publications.
Using Amazon Bedrock
A start-to-finish playbook for building generative AI solutions on AWS with Amazon Bedrock In Using Amazon Bedrock: Learn to Architect, Secure and Optimize Generative AI Applications on AWS, AWS Community Builder and artificial intelligence expert Renaldi Gondosubroto delivers a hands-on walkthrough of Amazon Bedrock, Amazon Web Services’ central, fully managed service for building generative-AI apps. The author dives deep into the techniques you can use to create generative AI solutions, including prompt engineering with foundational models, guidance for working with multimodal models, fine-tuning, and interacting with the API. You’ll find optimized code samples, best practices, and step-by-step guides for creating reliable, secure, and cost-effective generative AI tools. You’ll also discover repeatable frameworks for iterating and optimizing production models and explanations of the latest capabilities including guardrails and utilizing Claude 4. Using Amazon Bedrock is a must-read guide for cloud architects, AI engineers, developers, and programmers interested in building efficient, working solutions using generative AI technology. It covers every useful feature offered in Bedrock and explains exactly how to use them as you develop sustainable and scalable software that solves real-world problems. A from-scratch roadmap to building generative AI solutions on AWS with Amazon Bedrock In Using Amazon Bedrock: Learn to Architect, Secure and Optimize Generative AI Applications on AWS, accomplished Software Engineer, developer advocate, and AWS Community Builder, Renaldi Gondosubroto, delivers an in-depth walkthrough of Amazon Bedrock, the keystone generative AI service on the Amazon Web Services cloud. Gondosubroto offers a start-to-finish guide of the service and its capabilities, from prompt engineering with foundational models to building applications using the API, working with multimodal models, and fine-tuning. This book provides hands-on instruction on Amazon Bedrock from an experienced developer and AI specialist. It’s packed with real-world code samples, proven best practices, and techniques that result in reliable, secure, and cost-effective generative AI solutions. You’ll also find: Demonstrations of robust governance guardrails and strategies for reducing development timeRepeatable, industry-grade frameworks for continuously iterating and optimizing models in productionContemporary updates that incorporate the latest announcements made by Amazon at the re:Invent conference in December 2024 and beyond Perfect for cloud architects, artificial intelligence engineers, and software engineers, Using Amazon Bedrock is an insightful, original, and practical roadmap to generative AI on AWS that explains the AI fundamentals you need to understand to get started in AWS generative AI development and the hands-on techniques you’ll use every day to transform those concepts into efficient, working solutions. RENALDI GONDOSUBROTO is an AWS Community Builder, an accomplished software engineer with over a decade of architecting solutions on AWS, and a developer advocate in the tech community. He currently holds all 14 AWS certifications. He has extensive experience developing AI solutions for small and large enterprises.
AI in Early Education
An original and up-to-date examination of how to use AI to improve pre-school and early childhood education In AI in Early Education: Integrating Artificial Intelligence for Inclusive and Effective Learning, examines how to use artificial intelligence technology to enhance teaching and learning with personalized learning pathways, inclusive instructional practices, and increased student engagement. The book explores key themes, like AI literacy for young learners, ethical and pedagogical considerations, and the professional development that educators need to effectively integrate these tools into their classrooms. You’ll also find: Ways to use AI to support diverse learners, including those with special educational needs Hands-on strategies that offer educators clear, practical ways to bring AI tools into the classroom A careful emphasis on inclusion strategies, demonstrating how AI can help tailor learning experiences to meet the needs of all students Perfect for educators, teacher trainers, researchers, regulators, and policymakers interested in how artificial intelligence might contribute to the improvement of early childhood and primary education, AI in Early Education is also a must-read for pre-service and in-service teachers and professionals involved in curriculum development and inclusive education. An original and up-to-date examination of how to use AI to improve pre-school and early childhood education In AI in Early Education: Integrating Artificial Intelligence for Inclusive and Effective Learning, examines how to use artificial intelligence technology to enhance teaching and learning with personalized learning pathways, inclusive instructional practices, and increased student engagement. The book explores key themes, like AI literacy for young learners, ethical and pedagogical considerations, and the professional development that educators need to effectively integrate these tools into their classrooms. You'll also find: Ways to use AI to support diverse learners, including those with special educational needsHands-on strategies that offer educators clear, practical ways to bring AI tools into the classroomA careful emphasis on inclusion strategies, demonstrating how AI can help tailor learning experiences to meet the needs of all students Perfect for educators, teacher trainers, researchers, regulators, and policymakers interested in how artificial intelligence might contribute to the improvement of early childhood and primary education, AI in Early Education is also a must-read for pre-service and in-service teachers and professionals involved in curriculum development and inclusive education. Dr. Stamatios Papadakis is an Assistant Professor in Educational Technology at the Department of Preschool Education, University of Crete, Greece. His research focuses on the integration of digital technologies, computational thinking, and educational robotics in early childhood and primary education. He has authored and edited numerous books and articles on mobile learning, STEM education, and AI in education. He also serves as Editor and Editorial Board Member in several international journals and is actively involved in major European initiatives in digital education.