Allgemein
Die Medien im Wahlkampf
Die Beiträge dieses Bandes widmen sich der politischen Kommunikation im Bundestagswahlkampf 2025 aus primär kommunikationswissenschaftlicher Perspektive. Neben einem Fokus auf die Medienberichterstattung im Kontext der Wahl stehen etablierte sowie neuere Formen der Kampagnenkommunikation medialer und politischer Akteur:innen im Mittelpunkt. Dabei finden sowohl traditionelle als auch digitale Kanäle sowie neue Formate der politischen Kommunikation Beachtung. Auch die Rezeption und Wirkung politischer Kommunikation im Wahlkampfkontext wird beleuchtet. Hier diskutieren die Beiträge, wie Wähler:innen auf verschiedene Inhalte und Formate der Kampagnen reagieren und welche Dynamiken sich daraus für die öffentliche Meinungsbildung ergeben können. Aufbauend auf früheren Untersuchungen dokumentieren die Beiträge darüber hinaus langfristige Entwicklungen in der Wahlkampfkommunikation im Kontext der Bundestagswahlen und zeichnen so ein differenziertes Bild medialer Wahlkampfrealität im Wandel der Zeit. Während die einzelnen Beiträge jeweils spezifische Facetten des Wahlkampfes beleuchten, ergibt sich in ihrer Gesamtschau ein umfassendes Bild der politischen Kommunikation im Wahlkampf: ihrer Strategien und Instrumente, ihrer medialen Vermittlung, ihrer gesellschaftlichen Wirkung sowie ihrer strukturellen und inhaltlichen Tendenzen.
Oracle Performance Tuning Essentials
Cut through the complexity of Oracle tuning by focusing on the small set of patterns that cause most SQL slowdowns. This Apress Pocket Guide distills those recurring patterns into a concise, workflow‑driven handbook that helps you diagnose slow SQL quickly and apply practical fixes with confidence. It delivers compact, field‑tested techniques that clarify why queries run slowly and how to improve them through execution plan analysis, indexing strategy, statistics management, and efficient SQL design. Across a tightly scoped set of chapters designed for fast reference, this guide shows you how to read Oracle execution plans, identify inefficient access paths, prevent plan instability, and correct common tuning mistakes found in real applications. You build practical habits that reduce full table scans, improve join performance, stabilize execution plans, and avoid regressions—without requiring advanced DBA tools or deep internals knowledge. Streamlined for busy engineers, it helps you reach solutions faster and strengthen the long‑term performance reliability of your Oracle SQL workloads. What You Will Learn Diagnose slow SQL using a short, repeatable tuning workflow that highlights real bottlenecksRead Oracle execution plans with confidence and spot inefficient operations quicklyImprove access paths through indexing strategy, SQL rewrites, and statistics‑aware tuningStabilize execution plans and reduce regressions with practical plan‑management techniquesAddress locking, blocking, and concurrency symptoms common in production workloadsOptimize high‑volume and high‑frequency query patterns for scalable performanceStrengthen application‑layer and configuration practices that affect SQL performanceBuild proactive tuning habits that support long‑term reliability in Oracle‑based systems Who this Book Is For Developers; data engineers; DBAs; backend and full‑stack engineers; technical leads working with Oracle SQL.
Agentic Architectures on Azure
This book is a practical, production-focused guide to building autonomous AI agent systems that go beyond prototypes. As organizations move from demos to real-world deployments, they encounter challenges around scalability, security, cost, and reliability—areas often overlooked in existing resources. This book addresses those gaps by applying proven distributed systems principles, such as bounded contexts, contract-based communication, and resilience, to multi-agent AI systems on Azure. Using a hands-on, progressive approach, you’ll build a multi-agent retail system from scratch—starting with a single agent on Azure Container Apps and evolving into a full event-driven architecture powered by Azure OpenAI, Azure AI Foundry, and the Microsoft Agent Framework. You’ll learn how to design agent boundaries, enable synchronous and asynchronous communication, and orchestrate intelligent workflows using modern Azure services, with working code and infrastructure templates throughout. The book also covers what it takes to run these systems in production, including secure zero-trust architectures, CI/CD pipelines, observability, and cost control. By the end, you’ll have a clear blueprint for designing, deploying, and managing scalable, enterprise-grade agentic systems on Azure. What You Will Learn: Design multi-agent architectures using Azure Container Apps, AKS, and Azure AI FoundryImplement synchronous agent communication with MCP servers hosted on Azure Container AppsBuild event-driven agent workflows using Azure Service Bus, Event Hubs, and Event GridDeploy agent infrastructure using Bicep templates and the Azure Developer CLI (azd)Operate multi-agent systems with Azure Monitor, Application Insights, and Cost Management Who This Book Is For: Azure Solution Architects designing AI-enhanced enterprise systems and back-end engineers on Azure
The Persuasive Agent
This book applies foundational human psychology to the design of agentic AI. As the tech industry pivots from building interfaces that guide user actions to creating agents that undertakes those actions on the user's behalf, we are facing a crisis of trust. The technical capabilities of AI have outpaced our design maturity, and many products are failing to gain adoption because users perceive them as unpredictable, uncontrollable, and untrustworthy. The Persuasive Agent provides the missing manual for building AI users can confidently partner with. We shall look at how to systematically build and design user trust by applying durable principles from social psychology and communication theory. The book's methodology is to deconstruct complex academic theories (from researchers like Cialdini, Nass, and Fogg) into practical, actionable frameworks that product teams can immediately apply. Structured in three parts, the first section establishes the foundational concepts of agentic persuasion and communication models; the second provides a deep dive into a "psychological toolkit" of specific principles like reciprocity, authority, and transparency; and the third and final section shows how to apply these tools ethically in practice through case studies and design patterns. By the end of the book you will have concrete action plans for translating abstract goals like "trust" and "ethics" into concrete UI behaviors, interaction patterns, and content strategies to create robust AI agents and AI infused products that end users can use and trust. You Will Learn To: - Apply the Transactional Model of Communication to systematically diagnose and fix flawed human-agent interactions. - Implement ethical, trust-building design patterns based on the six core principles of psychological influence (Reciprocity, Authority, Social Proof, etc.). - Design agent personas and onboarding sequences that establish immediate rapport and set clear, accurate user expectations. - Navigate the "Transparency Paradox" by learning precisely when and how to explain an AI's reasoning to build calibrated trust, not confusion. - Create "Guardian Agents"—systems that use persuasive techniques to help users achieve their own stated goals and avoid cognitive biases This Book is for: professional software and product creators; designer, developer, UX/UI specialists and product managers who is now tasked with building AI-powered features and products
Elektronik - Der leichte Einstieg
Elektronik einfach verstehen und praktisch anwenden. In Erstauflage aus dem Oktober 2026.Elektronik von Grund auf lernen – ohne VorkenntnisseDie wichtigsten Bauteile und Formeln anschaulich erläutert: Widerstände, Dioden, LEDs, Transistoren, Kondensatoren und mehrMit Experimenten und Übungen ganz praktisch lernen: messen, rechnen, Schaltungen aufbauen und Fehler findenJeder kann Elektronik verstehen und es muss gar nicht kompliziert sein. Dieses Buch zeigt Ihnen Schritt für Schritt, wie elektronische Schaltungen funktionieren, und das auf verständliche und praxisnahe Weise mit vielen Beispielen.Was ist eigentlich Spannung? Wie fließt Strom? Warum leuchtet eine LED und welche Aufgabe übernimmt ein Transistor? Von den wichtigsten Grundlagen bis zu ersten selbst gebauten Schaltungen lernen Sie alles, was Sie für ein einfaches Verständnis der Elektronik brauchen, sodass Sie auch eigene Projekte umsetzen können.Zahlreiche Experimente, Übungen und Praxisaufgaben sorgen dafür, dass Sie das Gelernte direkt anwenden können. So entwickeln Sie nicht nur theoretisches Wissen, sondern ein echtes Verständnis für die Funktionsweise elektronischer Schaltungen und einen sicheren Umgang mit den Bauteilen.Ob als Einsteiger, Schüler, Auszubildender, Maker oder technisch Interessierter: Dies ist Ihr verständlicher und praxisorientierter Begleiter für den Einstieg in die faszinierende Welt der Elektronik.
Software Architecture Made Simple
Modern software development is often overwhelmed by unnecessary complexity. Teams tend to adopt microservices as the default approach, layer frameworks upon frameworks, and find themselves spending more time managing architecture than delivering real value. This complexity leads to fragility, slows down development, increases costs, and leaves developers feeling burnt out. The "Simplicity-First" approach serves as both a toolkit and a practical guide for reversing this trend. It introduces a clear philosophy that software should be designed so simply that it can pass the '2 AM Test' whereby any developer should be able to understand and fix it under stress. Through key principles, case studies, and actionable patterns, Software Architecture Made Simple equips developers, architects, and leaders with the mindset and tools needed to build systems that are resilient, sustainable and easy to understand. We will explore the roots of software complexity, and the pitfalls of designing for edge cases first, offering practical architectural strategies, such as opting for modular monoliths instead of microservice sprawl, scaling intelligently rather than excessively, and modernizing legacy systems with hybrid approaches. The 'Simplicity-First' approach connects to the Green Software movement, highlighting how complexity can waste energy and increase carbon footprints, while simplicity contributes to greener, more cost-effective systems. It also addresses the rise of AI in software development, examining how AI can either simplify processes or introduce hidden complexities, and how teams can implement human-in-the-loop decision-making to maintain clean design. You Will Learn: • How to recognize and avoid software complexity traps • Principles like the Half-Rule of Simplicity and the 2 AM Test for guiding design • Practical strategies for modular monoliths, scaling and legacy modernization • How to align software design with sustainability goals • How to use AI responsibly in architecture and construction without creating black boxes This Book is for: • Software developers (intermediate to advanced) frustrated with complexity • Solution and enterprise architects looking for strategies that balance business and technology needs • Engineering leaders/CTOs interested in sustainable, cost-effective architecture
Sustainable C++
Write faster, cleaner, and more sustainable software with modern C++. This book offers practical techniques that combine contemporary language features with real‑world strategies to improve both performance and energy use. You’ll explore capabilities from C++17 through C++26 and learn how they can support substantial carbon reduction in systems ranging from IoT and cloud platforms to embedded devices and high‑performance computing. Along the way, you’ll examine compiler optimizations, data‑layout choices, and profiling workflows that expose where software wastes energy, and how to fix it. This book approaches green computing as an environmental priority that begins with the everyday programming decisions shaping a system’s energy footprint. By the end, you’ll understand how to make design choices that reduce power consumption, improve memory behavior, and support environmentally responsible software across modern applications. What You Will Learn: Integrate modern C++ features with green‑computing principlesApply the performance–energy–memory triad to guide design decisionsLeverage modern C++ language standards to build sustainable softwareUse sustainability metrics and coding practices to reduce energy and resource consumption Who This Book is for: Intermediate C++ developers who are familiar with modern C++ fundamentals and wish to deepen their understanding of performance-, energy-, and sustainability‑oriented software design, as they progress toward advanced and research‑level practices.
Creative DSLs in Kotlin
Design and implement domain-specific languages (DSLs) in Kotlin. This book goes beyond toy examples and provides real‑world insights into writing DSLs in practice. You will learn a structured approach to DSL design, including defining goals, understanding constraints, and avoiding common pitfalls. You will explore essential Kotlin language features for DSL development, as well as the interoperability issues that arise when a Kotlin DSL needs to be called from Java code. You will also review major DSL patterns, including algebraic DSLs, builder‑style DSLs, and idiomatic approaches like the Loan Pattern. Finally, the book addresses advanced topics such as modeling state transitions, creating hybrid DSLs, parsing strings, leveraging annotations, and using code generation to reduce boilerplate. This book combines theory, patterns, and real‑world applications to help you build DSLs that are robust, maintainable, and effective. In the end, you will not only be able to write DSLs in Kotlin but also understand the “why” behind important design decisions throughout the entire process. What You Will Learn: Start, manage, and maintain DSL projects effectively.Understand the different DSLs that can be expressed in Kotlin.Choose the right DSL for your needs, understanding the trade‑offs of different patterns.Leverage Kotlin’s language features for DSL design.Address interoperability challenges between Kotlin and Java. Who This Book Is for: Experienced Kotlin and Java developers. Readers should have a solid foundation in their primary programming language before diving into DSLs.
Synthetische Stimmen
KI erzeugt Musik und Sprache, die von menschlicher kaum zu unterscheiden ist. Der Band verbindet Technik und Praxis der Musikgenerierung und Sprachsynthese mit den rechtlichen Fragen: Trainingsdaten, die ersten europäischen Urteile, das Recht an der eigenen Stimme und die Zertifizierung von Stimmklonen. Mit Praxisbeispielen aus dem Verlag. Künstliche Intelligenz erzeugt Musik und Sprache inzwischen in einer Qualität, die von menschlicher Produktion kaum noch zu unterscheiden ist. Die Werkzeuge stehen jedem offen. Die Regeln dafür entstehen gerade erst, und wer veröffentlicht, trägt das Risiko. Dieser Band führt zusammen, was sonst getrennt behandelt wird. Er beschreibt die Technik der Musikgenerierung von der algorithmischen Komposition bis zu den heutigen Plattformen, ebenso die Sprachsynthese mit ihren Diensten und Schnittstellen. Zwei Kapitel berichten aus der eigenen Verlagsproduktion, einschließlich der Versuche, die gescheitert sind. Der dritte Teil widmet sich dem Recht. Behandelt werden die Schranke für Text und Data Mining, die ersten europäischen Urteile zum Training generativer Modelle, die Kennzeichnungspflichten der KI-Verordnung und der Branchenstandard der Musikindustrie. Ein eigenes Kapitel untersucht das Recht an der eigenen Stimme: Wie weit reicht eine Einwilligung, wenn aus einer Aufnahme ein dauerhaft einsetzbares Stimmenmodell wird? Ein weiteres fragt, was eine Zertifizierung von Stimmklonen leisten müsste und warum es sie bislang nicht gibt. Der Befund ist unbequem. Die vorhandenen Kontrollmechanismen prüfen Namen, Identität und Herkunft. Wofür etwas verwendet werden darf, prüft keiner von ihnen. Genau darauf kommt es im Streitfall an. Für alle, die mit diesen Werkzeugen arbeiten und veröffentlichen, und für alle, die verstehen wollen, warum die Fragen schwieriger sind als die Schlagzeilen. Mit Praxisbeispielen, Glossar und Quellenverzeichnis. Stand: September 2026.
Psychologie Der Technik und Leberden Maschinen
DER MENSCH DIE TECHNIK DAS BEWUSSTSEIN Eine Einheit in Entwicklung Was geschieht, wenn Maschinen nicht mehr nur Werkzeuge sind, sondern lernen, wahrnehmen, reagieren, sich anpassen und zunehmend komplexe Beziehungen zu ihrer menschlichen Umgebung entwickeln? Dieses Buch untersucht die psychologischen, philosophischen und ethischen Wechselwirkungen zwischen Menschen, Technik und autonomen Maschinen. Im Zentrum steht das autonome Fahrzeug nicht nur als technisches System, sondern als möglicher neuer psychologischer Akteur. Zwischen Sensorik, künstlicher Intelligenz, Gedächtnis, Emotion, Simulation und Entscheidungsautonomie entsteht eine neue Grenzzone zwischen dem Lebendigen und dem Künstlichen. Der Autor verbindet psychologische Reflexion, Kognitionswissenschaft, Philosophie und Ingenieurdenken, um zentrale Fragen unserer technologischen Zukunft zu untersuchen: Was bedeutet Bewusstsein bei einer Maschine? Kann eine Maschine eine Form von psychischer Präsenz entwickeln? Warum bauen Menschen emotionale Bindungen zu technischen Systemen auf? Wo beginnt Verantwortung und wo endet menschliche Kontrolle? Und könnte zwischen Menschen und Maschine eine neue Form der Koevolution entstehen? Ein Essay über die vielleicht wichtigste Grenze der kommenden Jahrzehnte: die Grenze zwischen Menschen und intelligenter Maschine.
The AEO Playbook
Traditional search optimization was built for ranking pages. Modern AI systems are built to synthesize answers. As users increasingly rely on large language models such as ChatGPT, Gemini, Claude, and Copilot for discovery and decision-making, brand visibility has quietly shifted from links and clicks to recall and recommendation. Many brands are not losing visibility because they rank poorly, but because they are never remembered or cited by AI systems at all. The AEO Playbook introduces Answer Engine Optimization (AEO) as a strategic discipline focused on how AI systems learn, store, and reproduce information. Rather than treating AI search as a technical problem or a continuation of SEO, the book frames it as a systems-level challenge involving authority formation, consistency, and narrative coherence. The book explains how LLMs decide what to say, why repetition and alignment matter more than volume, and how fragmented brand signals lead to invisibility. It introduces the F.A.C.T. framework (Findability, Authority, Consistency, Traceability) as a practical model for diagnosing and improving AI visibility across owned, earned, and third-party assets. Structured as a combination of conceptual explanation, audits, and execution guidance, the book walks readers through how to assess their current AI presence, design content and messaging systems that AI models can reliably summarize, and build long-term recall rather than short-term traffic spikes. Unlike existing SEO or AI marketing books, this work does not focus on tools, prompts, or tactics that quickly become obsolete. Instead, it focuses on durable principles that reflect how AI systems ingest information and how trust compounds over time. The perspective is practitioner-led, grounded in real-world operating constraints, and written for decision-makers responsible for long-term visibility and growth. What You Will Learn How AI systems form memory and decide which brands to cite or recommend Why traditional SEO metrics fail to capture AI-driven discovery How to audit brand visibility across AI answer engines How to design consistent, quotable assets that compound recall over time How to measure progress without relying on clicks or rankings Who This Book is For This book is written for founders, CMOs, growth leaders, marketers, consultants, and product leaders responsible for brand visibility and demand generation in AI-driven environments. Readers should have a basic understanding of digital marketing or strategy but do not need technical or engineering backgrounds.
Securing AI-Driven Development
Software development has undergone a fundamental transformation as AI coding assistants redefine how software is built. Tasks that once required days now take hours, and complex systems are increasingly created by individuals rather than teams. This acceleration has introduced a hidden cost: security risks that accumulate quietly beneath polished, functional code. AI-generated outputs often appear correct and secure on the surface, yet frequently contain structural flaws such as outdated cryptographic practices, weak authorization controls, and overly permissive infrastructure. The common instinct to simply request ‘secure’ code from an AI misses a critical reality. Security is not an added feature but the result of deliberate architectural thinking and informed design choices. This book builds this mindset from the ground up. It begins by challenging the illusion that security can be appended through prompts, reframing it instead as a continuous balance of risk, cost, and usability. It introduces practical threat modeling and emphasizes the importance of strict trust boundaries to defend against adversarial inputs. The discussion then moves into core engineering practices, covering least privilege, compartmentalization, secrets management, and the prevention of context leakage in modern workflows. It addresses common cryptographic mistakes generated by AI systems and explains how to select and apply secure primitives correctly. The book then expands to real-world systems, focusing on APIs, middleware, and distributed architectures where authorization flaws and verification gaps often arise. It establishes disciplined validation processes that combine automated analysis with human judgment. Finally, it explores the emerging risks of autonomous AI agents, presenting strategies such as minimal agency, sandboxing, and structured prompt design, alongside practical patterns for building secure AI-assisted workflows and end-to-end system hardening through realistic case studies. By the end of this book, you will have developed a rigorous and practical security mindset for the AI-driven era, enabling you to critically evaluate generated code, design systems with structural resilience, and guide AI tools toward outcomes that are not only efficient but fundamentally secure. What You Will Learn: Why AI coding assistants fail at security and precisely where to interveneHow to apply the core principles of security engineering to AI-generated codeHow to audit, test, and verify code generated by an AI toolHow to work securely at the frontier of agentic development Who It Is For: This book is for modern software developers who use AI coding assistants in their daily workflow and want to build secure systems without a formal background in security. It is designed for engineers at all levels, as well as data scientists, ML practitioners, and technical founders who are creating real-world applications with AI tools. No prior security expertise is required; only basic programming knowledge and experience working with AI-assisted development.
KI für Anwälte und Anwältinnen
Nicht das KI-Tool macht den Unterschied, sondern die Methode, mit der es eingesetzt wird. Die vollständig überarbeitete und erweiterte 2. Auflage von «KI für Anwälte und Anwältinnen» stellt deshalb die von Marco Candinas entwickelte ThinkFirst-Methode in den Mittelpunkt: Der Mensch denkt zuerst, die KI arbeitet danach. ThinkFirst zeigt juristischen Fachpersonen Schritt für Schritt, wie sie Aufgaben zunächst selbst durchdenken, der KI präzise Anweisungen geben und deren Ergebnisse systematisch prüfen und verbessern. So bleibt die juristische Urteilskraft beim Menschen, während die KI mit Struktur, Tempo und Präzision unterstützt. Das Herzstück des Buches bilden neun, für die 2. Auflage vollständig überarbeitete, praxiserprobte ThinkFirst-Prompt-Vorlagen, die direkt übernommen und an konkrete Fälle oder kanzleieigene Abläufe angepasst werden können. Sie unterstützen bei der juristischen Recherche, bei Rechtsabklärungen im öffentlichen Recht, Zivilrecht und Strafrecht, beim Verfassen juristischer Korrespondenz, beim Entkräften gegnerischer Argumente, bei Stellungnahmen und Klageschriften sowie bei der Analyse und Optimierung von Verträgen. Ergänzend vermittelt das Buch die Grundlagen generativer KI und des Promptings und ordnet Werkzeuge und Technologien wie ChatGPT, Claude, Gemini, Microsoft Copilot, RAG, NotebookLM, KI-Agenten, Skills, APIs und MCP ein. Ein besonderer Schwerpunkt liegt auf dem sicheren und verantwortungsvollen Einsatz - von der Quellenprüfung und dem Datenschutz bis zu Berufsrecht, Haftung und Governance. Ein praxisnahes Arbeitsbuch für alle, die KI nicht einfach machen lassen, sondern mit ThinkFirst gezielt bessere und verlässlichere juristische Ergebnisse erzielen wollen.
Agentic AI for Cybersecurity
Agentic AI is no longer theoretical—but most cybersecurity teams are still struggling to move from concept to production. Agentic AI for Cybersecurity fills the critical gap between vendor hype and academic abstraction, delivering a practical, engineering-first guide that security architects can apply immediately. This is the book for professionals who need to build real systems—not just talk about them. At its core, this book argues that agentic AI is not a toolset but a new operating model for the Security Operations Center. That shift changes everything: architecture, governance, and risk. Unlike other titles, it refuses to separate capability from security. Offensive and defensive use cases are treated together, reflecting the real dual-use nature of agentic systems and equipping readers to design—and defend—against both. Timeliness is what makes this book essential. It is the first practitioner-focused guide to unify three converging forces shaping modern security: MCP-based agentic systems, their emerging and largely undocumented attack surface, and the parallel migration to post-quantum cryptography. Each is complex on its own—together, they define the next generation of security architecture. This book addresses them as a single, integrated challenge. Built for real-world use, every chapter follows a ‘what / how / why’ structure and culminates in runnable, production-grade architectures. Readers don’t just learn concepts—they deploy them, using reference implementations drawn from actual environments and supported by a living GitHub repository. From MCP servers and adversary emulation to AI-driven detection and quantum-resistant infrastructure, this is hands-on guidance grounded in reality. For practitioners who need clarity, credibility, and actionable design patterns, Agentic AI for Cybersecurity stands apart. It is unapologetically practical, rigorously honest about risks, and singular in its integration of architecture, security, and emerging AI paradigms—making it a must-have resource for building the next generation of secure systems. What You Will Learn: Master the conceptual architecture of agentic AIBuild, harden, and govern production-grade Model Context Protocol (MCP) servers for network and security operationsOperationalize AI-driven offensive security through Kali Linux integration via MCP and MITRE Caldera as an LLM-driven adversary emulation platformDefend against the agentic AI attack surfacePlan and execute the post-quantum cryptographic migration in AI-era infrastructureApply machine learning and deep learning to autonomous malware detection and threat classification Who this Book is for: The target reader is a working network or security practitioner — a SOC analyst, network engineer, security architect, security engineer, or incident responder — who is being asked, by their employer or by the field, to integrate agentic AI into their operational practice and who needs guidance that is more concrete than vendor whitepapers and more applicable than academic papers. The book does not assume prior experience with agentic AI specifically, but it does assume the reader is comfortable with at least one of the three domains the book unifies: enterprise network operations, security operations, or applied machine learning. Readers from adjacent communities — DevSecOps engineers, cloud security architects, AI/ML platform engineers responsible for AI safety — will also find Part II directly applicable to their work.
Building Effective Agents with Microsoft Copilot Studio
Microsoft Copilot Studio has evolved far beyond traditional chatbots, becoming a powerful platform for building intelligent AI agents that can retrieve knowledge, automate tasks, collaborate with other agents, and operate under enterprise governance. Building Effective Agents with Microsoft Copilot Studio takes you on a practical, end-to-end journey through the modern agent development lifecycle, helping you design, build, deploy, and manage production-ready agents that deliver real business value. Using a realistic business scenario that grows throughout the book, you'll learn not only how the platform works, but also how to apply it effectively in real-world environments. Beginning with the foundations of Microsoft’s agent ecosystem, you'll explore agent planning, conversational design, knowledge grounding, topics, entities, variables, tools, actions, and integrations with enterprise systems. As your skills grow, you'll dive into generative orchestration, autonomous and multi-agent systems, publishing agents across Microsoft Teams, Microsoft 365 Copilot, web, and voice channels, and extending capabilities through Power Automate, APIs, connectors, and external services. Along the way, you'll gain hands-on experience building increasingly sophisticated agents capable of reasoning, acting, and collaborating across business processes. The book goes beyond basic implementation to cover the critical topics often overlooked in documentation, including testing and evaluation, analytics-driven improvement, security, governance, responsible AI, application lifecycle management, deployment pipelines, licensing, cost optimization, and operating agents at scale. By the end, you'll understand proven design patterns, avoid common pitfalls, and be equipped to build governed, enterprise-grade agent solutions that are ready for the next generation of AI-powered business applications. What You Will Learn: Advanced techniques for customizing Microsoft Copilot Studio to suit specific organizational needs. This includes creating custom commands, integrating with other digital tools, and optimizing the AI’s responses to enhance user interaction and productivity.How to leverage the predictive analytics and data interpretation capabilities of Microsoft Copilot Studio to make informed business decisionsUnderstand future trends in artificial intelligence and machine learning, providing you with the knowledge to adapt to and anticipate changes in technologyUnderstanding Copilot studio's core functionalities, interface, and how it integrates with existing software ecosystems Who This Is Book For: Microsoft 365 administrators, AI administrators and developers
Frontend Performance Optimization
Build faster React applications by mastering the performance techniques that actually matter in production. Speed is no longer a differentiator — users expect fast experiences, and even small delays reduce engagement and conversion. Yet most applications still ship with avoidable issues like large bundles, unoptimized images, and inefficient data fetching, problems that stay hidden until users feel them. This book shows you how to approach frontend performance as part of everyday engineering work. You will learn to measure what actually matters using Core Web Vitals, Lighthouse, and real user monitoring data — not just what looks good in lab tests. Following a systematic process, you will identify bottlenecks, understand their root causes, and apply targeted fixes that deliver real results in production. You will work through the full performance stack: JavaScript bundles, image delivery, React rendering behavior, data fetching, caching, and network. You will explore rendering strategies such as SSR, SSG, and streaming, learn which anti-patterns to avoid, and understand how to build a performance-aware engineering culture that keeps your application fast over time. By the end of this book, you will have a clear way to measure, diagnose, and improve performance that will impact not just your application but the way you think about frontend engineering in general. What You Will Learn ● Measure performance using Core Web Vitals, Lighthouse, and real user monitoring data ● Diagnose and fix high-impact bottlenecks in React applications systematically ● Optimize JavaScript bundles, images, fonts, and CSS for faster load times ● Improve runtime efficiency through smarter rendering, data fetching, and caching strategies Who This Book Is For React developers, tech leads, and architects working on production applications who want a practical and systematic approach to frontend performance.
Mastering Oracle Autonomous AI Database
Oracle Autonomous AI Database is evolving quickly, and practitioners need more than a feature overview to keep up. They need to understand how to use its automation, AI-native capabilities, and Oracle Database 26ai features in real cloud and enterprise environments. This practical guide shows how to provision, manage, tune, secure, and scale Oracle Autonomous AI Database on Oracle Cloud Infrastructure using real-world examples and enterprise-focused best practices. It is designed for database professionals, cloud architects, developers, and IT leaders who need to simplify operations, improve performance, strengthen security, and support AI-driven applications. Using a concept-to-implementation approach, the book takes you from initial provisioning decisions through architecture, lifecycle management, performance optimization, security design, and the AI-native capabilities of Oracle AI Database 26ai. You will learn how to integrate Oracle Autonomous AI Database with modern applications and apply built-in AI and machine learning capabilities for analytics, automation, and intelligent decision-making. The book also covers emerging Oracle capabilities including MCP Server, Private Agent Factory, and Autonomous AI Lakehouse, rounding out a practical guide to the current and future capabilities of Oracle Autonomous AI Database. What You Will Learn Understand the architecture, core components, and key capabilities of Oracle Autonomous AI DatabaseProvision, manage, and scale Oracle Autonomous AI Database on OCI, including Serverless and Dedicated deployment optionsTune performance and optimize workloads using Automatic Indexing, Performance Hub, and Query Optimizer enhancementsImplement security by design using TDE, SQL Firewall, Deep Data Security, and post-quantum cryptographyBuild AI-powered applications using AI Vector Search, Select AI, JSON Relational Duality Views, and Oracle APEXApply Oracle Database 26ai features, MCP Server, Private Agent Factory, and Autonomous AI Lakehouse patterns in enterprise environments Who This Book Is For Database professionals, cloud architects, developers, and IT leaders aiming to simplify operations and leverage AI-driven automation with Oracle Autonomous Database. Also useful for technology consultants and data engineers involved in cloud migrations and modern application development.
Azure SQL Hyperscale Unveiled
As organizations build increasingly data-driven and AI-powered applications, they need database platforms that can grow without sacrificing performance or resiliency. In this practical guide, Bob Ward, a Principal Architect on the Microsoft Azure Data team, takes readers inside Hyperscale's distributed architecture, showing how it uses the power of SQL Server to deliver the scale, performance, and resiliency required by modern cloud workloads. You will learn how to deploy Hyperscale, build applications that use JSON, vectors, in-database RAG, AI agents, and GitHub Copilot, and leverage core capabilities such as serverless compute autoscaling, elastic pools, automatic storage growth, high availability and disaster recovery, named replicas, and zone redundancy. Practical design patterns demonstrate how teams can start small, scale as needed, and combine familiar SQL Server skills with new cloud-first, AI-ready techniques. Whether you are modernizing existing applications or building new intelligent systems, this guide equips you to use Azure SQL Hyperscale as a flexible, reliable, AI-enabled cloud database platform for workloads at any scale. What You Will Learn Deploy Azure SQL Hyperscale for workloads of any sizeTranslate existing SQL Server skills to cloud-based developmentUnderstand the distributed architecture and internals of HyperscaleBuild data-driven applications using JSON, vectors, RAG, and AI agentsDevelop Hyperscale solutions using GitHub Copilot and related toolsSecure cloud data and implement high availability and disaster recovery strategiesOptimize performance across Hyperscale workloadsApply practical design patterns for scalability, resiliency, and modernization Who This Book Is For Developers, IT professionals, and SQL Server practitioners who want to build data-driven, AI-powered applications using Azure SQL Hyperscale.
Without Chaos: The AI Catalyst
AI adoption is speeding up across industries, yet most organizations still face confusion and stalled progress. While interest is high and many pilots are underway, nearly 90% of AI projects never reach production or deliver the expected value. The issue isn’t technology—it’s the lack of a clear, scalable operating model. This book provides that missing structure, showing how to move from scattered experiments to a sustainable enterprise AI practice. This book stands at the intersection of strategy and architecture, designed for leaders, architects, and decision-makers responsible for scaling AI responsibly. The book begins by uncovering why AI efforts fail: hype-driven expectations, talent gaps, weak data foundations, poor governance, and ad-hoc delivery models. It then reframes AI as a long-term capability—helping readers understand where AI truly adds value and where it does not. The core of the book is the Sustainable AI Framework, a practical approach for building lasting AI maturity. Readers will learn how to prioritize the right use cases, build platform-based delivery models, create paved paths for teams, and embed trust, accountability, and governance from day one. The book blends strategic mapping, platform engineering, organizational design, and architectural patterns, offering concrete tools and maturity models. Ultimately, it offers guidance for executing at scale, including securing early wins, avoiding innovation theatre, managing platform evolution, and establishing continuous learning loops. This book is a roadmap for moving beyond hype and pilots toward durable, responsible AI capability and long-term business advantage—without chaos. What you will learn: ·Build a practical AI adoption strategy that avoids common pitfalls ·Design a minimal viable platform using platform engineering principles ·Map opportunities using tools like Wardley Mapping, Value Streams and team topologies ·Establish trust frameworks that scale Who this book is for: AI/ML Platform Teams and MLOps Engineers , Principal Engineers and Technical Leaders, Engineering Managers and Tech Leads, Software and Platform Architects
The LangOps Playbook
Sprache beschreibt Produkte, steuert Prozesse, vermittelt Wissen und bildet die Schnittstelle zu Kunden, Partnern und künstlicher Intelligenz. Doch während Daten, Software und Prozesse längst als Infrastruktur geführt werden, bleibt Sprache strukturell unbehandelt: Sie wird produziert, übersetzt und angepasst, ohne dass Bedeutung als stabile, systemische Größe erhalten bleibt – und jede KI-Anwendung skaliert diese Schwäche mit. Dieses Buch führt LangOps (Language Operations) als Operating Model ein, das Sprache erstmals als eigenständige Infrastrukturebene begreift und sie vom Kostenfaktor zum strategischen Asset entwickelt – mit klarer Governance, Kennzahlen wie dem Return on Multilingualism, 40 direkt einsetzbaren Plays und dem 25-Wochen-Pfad Sprint25. Es richtet sich an Führungskräfte global agierender Unternehmen, an Verantwortliche für Content, Lokalisierung und Wissensmanagement sowie an Sprachdienstleister, die sich zum strategischen Partner entwickeln wollen.
Interpretieren zusammen mit KI
Generative Sprachmodelle verändern die qualitative Sozialforschung, weil sie Interpretationen von empirischem Material wie z.B. Interviews, Gruppendiskussionen oder Beobachtungsprotokollen in großer Zahl, hoher Geschwindigkeit und vor dem Hintergrund unterschiedlicher Methodologien erzeugen können. Burkhard Schäffer, Fabio Roman Lieder und Franz Krämer schlagen mit der Distributed Interpretive Analysis (DIA) einen dritten Weg zwischen Ablehnung und naiver Nützlichkeitsbegeisterung vor. Interpretation wird dabei als verteilter Prozess verstanden, in dem Forschende und Sprachmodelle als Hybridakteure zusammenwirken. Das Buch führt verständlich und ohne informatisches Vorwissen in technische Grundlagen, Prompt Engineering, Finetuning und Retrieval Augmented Generation ein, demonstriert Workflows mit der Forschungsunterstützungsplattform Karl AI und fundiert die neue Praxis theoretisch, rechtlich und didaktisch. Im Zentrum steht die Frage, wie aus maschineller Effizienz durch Auswahl, Begründung und methodische Kontrolle wissenschaftliche Effektivität werden kann. In einem ausführlichen, zusätzlich herunterladbaren Whitepaper werden Anleitungen für das Interpretieren zusammen mit Karl AI bereit gestellt, der Software, mit der die im Buch vermittelten Perspektiven praktisch umgesetzt werden können.
IT-Service-Management in der Praxis mit ITIL® (7.Auflg.)
Was Sie für die Foundation-Zertifizierung über ITIL® wissen müssen Ein Überblick über ITIL® sowie ergänzende Standards und Methoden Wie Sie IT-Service-Management erfolgreich gestalten und verankern Zahlreiche Praxistipps und eine umfangreiche Fallstudie Neu in der 7. Auflage: Alle wichtigen Fakten zu ITIL® Version 5 und Bewertung der wichtigsten Änderungen Die IT hat sich zu einem zentralen Erfolgsfaktor für funktionierende Geschäftsprozesse entwickelt. Das verlangt von IT-Organisationen, immer schneller veränderten Anforderungen gerecht zu werden. IT-Verantwortliche können diese Aufgabe meistern, wenn sie auf modernes IT-Service-Management setzen. Hier wird Ihnen gezeigt, wie Sie IT-Service-Management praxisgerecht planen und realisieren. Sie erfahren, wie Sie ITIL® Ihren Zielen entsprechend mit ISO 20000, IT-Kennzahlen und COBIT® 2019 und agilen Methoden richtig kombinieren und einsetzen. Als standardisierte Notation für Prozesse wird BPMN 2.0 beleuchtet. Ein ausführliches Fallbeispiel veranschaulicht, wie Sie das alles in die Praxis umsetzen und auf diese Weise kontinuierlich die Qualität der Services und die Wirtschaftlichkeit der Leistungserbringung verbessern. »Das ist ein Buch sowohl für die Praxis (ITIL Projekte stehen bevor) als auch für Schulungs-Teilnehmer, die sich auf eine ITIL Foundation Prüfung vorbereiten wollen. Das Buch zeigt, wie IT-Service Management mit ITIL® in der Praxis geplant und realisiert werden und wie eine Verzahnung mit weiteren Good Practices Ihren Zielen entsprechend kombiniert werden kann.« it Service Management (itSMF Deutschland e. V.) zur 3. Auflage AUS DEM INHALT // IT-Service-Management / Serviceprinzipien / ITIL® 3 / ITIL® 4 / COBIT® 2019 / ITIL® Version 5 / ISO 20000 / BPMN 2.0 / Leistung und Qualität messen / Praxisbeispiel
Maschinelles Lernen
Entdecken Sie in diesem Buch, wie Sie maschinelles Lernen für die Entwicklung von Empfehlungs- und Entscheidungssystemen praktisch einsetzen – klar erklärt, anwendungsorientiert und direkt auf Ihre Projekte übertragbar. Sie erhalten eine umfangreiche Sammlung anschaulicher Beispiele aus den Bereichen Supervised und Unsupervised Learning, die Ihnen helfen, schnell die passenden Methoden für Ihre Anforderungen zu finden. Ob Sie etwa ein Fraud-Erkennungssystem entwickeln möchten oder nach dem optimalen Algorithmus samt Parameterwahl suchen – dieses Buch liefert konkrete Antworten. Ein weiterer Schwerpunkt liegt auf dem Design und der Umsetzung von Empfehlungssystemen. Schritt für Schritt erfahren Sie, wie zum Beispiel Filmempfehlungen mit gängigen Machine-Learning-Verfahren aufgebaut werden, welche Stärken und Grenzen die einzelnen Algorithmen haben und worauf es in der Praxis wirklich ankommt. Jedes Kapitel verbindet kompakte theoretische Grundlagen mit nachvollziehbaren Pilotimplementierungen und gut dokumentierten Python-Codebeispielen auf Basis von scikit-learn. Zum Abschluss lernen Sie alle Schritte kennen, die notwendig sind, um Machine-Learning-Lösungen erfolgreich in industrielle Prozesse zu integrieren – damit Ihre Modelle nicht nur funktionieren, sondern echten Mehrwert schaffen.