Computer und IT
Exploring Azure Container Apps
Equip yourself with the knowledge and tools needed to harness the full potential of Azure Container Apps, Microsoft's cutting-edge platform for orchestrating and scaling containerized applications effortlessly.Begin your journey with an introduction to Azure Container Apps (ACA), uncovering its unique features, benefits, and use cases. Further, you will dive into setting up and using Azure Container Registry (ACR) to efficiently store and manage container images, while mastering best practices for access control and security. You will also explore the intricacies along with comprehensive guides on deployment, scaling, and troubleshooting. You will learn how to integrate the Distributed Application Runtime (Dapr) with Azure Container Apps, explore best practices for seamless integration, and leverage its powerful building blocks for state management and pub/sub mechanisms. In conclusion, you will navigate the complexities of communication between microservices within Azure Container Apps environments, exploring various communication patterns and techniques.After reading this book, you will be able to work with ACA and implement the modern application deployment and scaling with Azure Container Apps with KEDA.WHAT YOU WILL LEARN:· Building and Deploying Backend API and Frontend UI to ACA· Communication between different Microservices inside ACA· Integration with Distributed Application Runtime (Dapr)· State management inside ACA· Scheduling and managing backend jobs· Monitoring with Application insights· Kubernetes Event Driven AutoScaler (KEDA)WHO THIS BOOK IS FOR:Application developers, DevOps Engineers and Tech EnthusiastsChapter 1: Introduction to Azure Container Apps.- Chapter 2: Deploying First Containerized App to Azure Container Apps.- Chapter 3: Creating and Deploying Frontend Blazor Web Application.- Chapter 4: Integrating Dapr with Azure Container Apps.- Chapter 5: Async Communication with Dapr Pub/Sub API.- Chapter 6: ACA Scheduled Jobs with Dapr .- Chapter 7: Monitoring and Observability.- Chapter 8: Auto Scaling with KEDA.
Creational Design Patterns in C#
Unlock the power of design patterns to elevate your software architecture. This pocket book provides an in-depth guide to five essential creational design patterns, crucial for building robust, scalable, and maintainable applications in C#.With step-by-step implementation in C# and a focus on practical applications, this book will empower you to enhance your software solutions and sharpen your design skills. The book starts by covering the simple factory pattern first where you will learn object creation with clear, single-responsibility classes followed by the factory method pattern. Next, you will go through the prototype, singleton, and builder patterns. In the end, you will demonstrate dependency injection with real-life examples.The book breaks down complex concepts into practical examples and concise explanations, making it an invaluable resource at every level of experience.WHAT YOU WILL LEARN:* Gain insights into the concepts of design patterns.* Will be familiar with real-world applications of design patterns* Gets a hands-on experience for each of the patterns using the latest version of C#.* Some of the alternatives to these patterns with their pros and cons.WHO THIS BOOK IS FOR:OOP, C# developers who want to learn and practice design patterns to upgrade their development skills.Chapter 1: Factory Method Pattern.- Chapter 2: Singleton Pattern.- Chapter 3: Builder Pattern.- Chapter 4: Prototype Pattern.- Chapter 5: Dependency Injection Pattern.- Appendix A: What’s Next?.- Appendix B: Other Books by the Author.
Apache Kafka (2. Auflage)
Apache Kafka in 2. Auflage aus dem Juli 2025: Von den Grundlagen bis zum Produktiveinsatz- Kafkas verteilte Streaming-Fähigkeiten beherrschen- Echtzeit-Datenlösungen implementieren- Kafka in Unternehmensumgebungen integrieren- Kafka-Anwendungen entwickeln und verwalten- Ausfallsicherheit und Skalierbarkeit erreichenApache Kafka ist der Goldstandard für Streaming- Datenplattformen und Kernbestandteil moderner Echtzeitdaten-Architekturen. Es ermöglicht einen nahtlosen Datenfluss zwischen verschiedenen Services, IT-Systemen und Teams in Unternehmen. Kafkas solides Design gewährleistet hohe Ausfallsicherheit und reibungslose Skalierbarkeit.Dieses Buch ist ein praktischer Leitfaden für IT-Fachleute, die Kafka in datenintensive Anwendungen und Infrastrukturen integrieren. Von der Einrichtung von Kafka-Clustern über das Produzieren und Konsumieren von Nachrichten bis zur Integration in Unternehmenssysteme – leicht verständlich wird das nötige Wissen für zuverlässige und skalierbare Kafka-Anwendungen vermittelt.Neu in der 2. Auflage: Das Buch wurde vollständig überarbeitet und um den umfangreichen Teil „Kafka im Unternehmenseinsatz“ erweitert. Dieser behandelt ausführlich Kafka Connect zur Systemintegration, Stream-Processing, Governance-Aspekte sowie eine praxisnahe Referenzarchitektur. Zusätzlich bietet das Buch erweiterte Einblicke in Monitoring, Desaster Management und Kafkas Rolle in modernen Datenarchitekturen.Anatoly Zelenin und Alexander Kropp begeistern sich seit ihrer Kindheit für IT und arbeiten heute als Experten für Echtzeitdaten und moderne IT-Architekturen mit Fokus auf Apache Kafka. Anatoly begeistert mit seinen Trainings Teams und bringt sie aufs nächste Kafka-Level. Alexander gestaltet aktiv Cloud- Plattformen mit Kubernetes und entwickelt kreative IT-Lösungen. Als Teil der DataFlow Academy verhelfen sie Unternehmen durch praxisnahe Trainings und Beratung zum Erfolg mit Echtzeitdaten.AUS DEM INHALT- Kafka-Grundlagen und Architektur: Vom Aufbau bis zur Verwaltung von Clustern- Zentrale Konzepte: Topics, Nachrichten, verteilte Logs und Zuverlässigkeit- Tiefer Einblick: Cluster-Management, Nachrichten produzieren, konsumieren und bereinigen- Kafka im Unternehmenseinsatz: Kafka Connect, Stream-Processing, Governance und Referenzarchitektur- Betriebliche Aspekte: Monitoring, Alerting und Desaster Management- Kafka in modernen Architekturen: Vergleich mit anderen Technologien und praktische Einsatzszenarien
Das Root-Manifest
Das Root-Manifest ist ein taktischer und philosophischer Aufruf zur Rückeroberung digitaler Kontrolle. Mehr als nur ein Sicherheitsleitfaden ist es ein Aufstand gegen passive Verteidigung, verwaltete Mittelmäßigkeit und Sicherheitstheater. Root ist kein Privileg – es ist eine Haltung. Dieses Buch rüstet Ingenieure, Verteidiger und Entwickler aus, um Infrastrukturen zu härten, Compliance-Theater infrage zu stellen und digitale Autonomie zu gestalten.
Game Development Concepts in C++
Leverage Unreal Engine to implement a wide variety of mechanics using C++. This book arms you with the knowledge and practices of game mechanics programming in C++, supported by detailed mathematical and programmatic explanations.Detailing everything from collision mechanics and AI pathfinding to networking and advanced physics, this book offers a holistic approach to game development, ensuring you get the most out of your gameplay experiences. You will work on character mechanics, environmental interactions, combat systems, and visual effects, gaining a thorough understanding of how to implement these features in C++.By the time you finish this book, you will be able to create complex game mechanics and to make your projects optimization-intensive and performance-driven. Whether you want to polish your skills or experiment with new techniques, _Game Dev Concepts in C++ for Unreal Engine Practitioners_ gives you the right guidance and helps you to stand out in this competitive world of game development.WHAT YOU WILL LEARN* Understand detailed scenarios that demonstrate how each mechanic is applied in real-world game development projects.* Utilize ready template codes for quicker implementation of mechanics in your games, saving time, and boosting productivity.* Develop your skills in advanced Unreal Engine and C++ programming, making you a versatile and proficient game developer.* See how different mechanics work together to create engaging gameplay experiences.__WHO IS THIS BOOK FORIdeal for intermediate level game developers who have an understanding of Unreal Engine and have a set of basic programming skills in C++.Chapter 1: Introduction.- Chapter 2: Collision Mechanics.- Chapter 3: Interaction Mechanics.- Chapter 4: Environmental Mechanics.- Chapter 5: Character Mechanics.- Chapter 6: Combat Mechanics.- Chapter 7: Physics and Dynamics.- Chapter 8: Audio and Visual Effects.- Chapter 9: AI and Pathfinding.- Chapter 10: Networking and Multiplayer.- Chapter 11: Advanced Mechanics.- Chapter 12: Case Studies.- Chapter 13: Appendices.
Microsoft Project Essentials
Microsoft Project has been a trusted tool for professionals worldwide, providing robust solutions for planning, executing, and tracking projects of any scale. This book serves as a comprehensive guide to mastering Microsoft Project, offering everything you need to elevate your project management skills and achieve your goals efficiently.The book starts by exploring Microsoft Project features and setting up your Project profile. You will then learn how to manage tasks and resources in Microsoft Project. Further, you will demonstrate scheduling and timelines followed by a deep dive into budgeting and cost management of the project. Moving forward, you will understand Microsoft Project's integration with M365 and learn Agile Project Management techniques. In the end, you will learn the advanced features and future patterns in Microsoft Project along with expert tips for efficiency.After reading the book, you will be able to create detailed project plans, set milestones, and develop schedules using Gantt charts.WHAT YOU WILL LEARN:* Discover how to assign resources to tasks, manage workloads, and optimize resource allocation* Gain skills in estimating project costs, tracking expenditures, and managing budgets effectively* Learn how to update project status, track progress against the plan, and manage changes efficiently* Integrate Microsoft Project with other Microsoft tools like Teams and SharePoint for enhanced collaborationWHO THIS BOOK IS FOR:Project Managers, Business Analysts and ConsultantsChapter 1: Introduction to Microsoft Project: Understanding the Platform.- Chapter 2: Getting Started with Microsoft Project.- Chapter 3: Managing tasks with Microsoft Project.- Chapter 4: Resource Management using Microsoft Project.- Chapter 5: Fine Tuning Project Schedule.- Chapter 6: Budget and Cost Management using Microsoft Project.- Chapter 7: Seamless integration of Microsoft Project with Microsoft 365 family.- Chapter 8: Agile Project Management using Microsoft Project.- Chapter 9: Microsoft Project Reports and Analytics.- Chapter 10: Advanced Features of Microsoft Project and Future Trends in Project Management.
CompTIA A+ Complete Study Guide, 2-Volume Set
YOUR COMPLETE, ACCURATE RESOURCE FOR THE UPDATED COMPTIA A+ CORE 1 AND CORE 2 EXAMSIn the newly revised sixth edition of CompTIA A+ Complete Study Guide 2-Volume Set: Volume 1 Core 1 Exam 220-1201 and Volume 2 Core 2 Exam 220-1202, you'll discover comprehensive coverage of all A+ certification exam objectives. A team of A+ certified IT professionals with a combined 50 years' experience in the industry walk you through the most popular information technology certification on the market today, preparing you for success on both the 220-1201 and 220-1202 A+ exams. The set emphasizes on-the-job skills you'll use every day as a PC technician or in a related role, with timely updates covering major advances in mobile, cloud, network, and security technology. It walks you through mobile devices, networking, hardware, virtualization and cloud computing, hardware and network troubleshooting, operating systems, security, software troubleshooting, and operational procedures. You'll also find:* Practical examples and technology insights drawn from the real-world experiences of current IT professionals* Exam highlights, end-of-chapter reviews, and other useful features that help you learn and retain the detailed info contained within* Complimentary access to the Sybex online test bank, including hundreds of practice test questions, flashcards, and a searchable key term glossaryPrepare smarter and faster, the Sybex way. CompTIA A+ Complete Study Guide 2-Volume Set is perfect for anyone preparing to take the A+ certification exams for the first time, as well as those seeking to renew their A+ certification and PC or hardware technicians interested in upgrading their skillset.
NoOps
Traditional DevOps is struggling with new challenges in today's fast-changing software world. With the rise of microservices, cloud-based systems, and AI-driven automation, managing software has become increasingly difficult. Teams often deal with too many tools, repetitive manual tasks, and slow innovation. NoOps provides a clear guide to using AI to streamline DevOps and reduce manual work.The book starts by explaining how DevOps has evolved and why software development has become so fragmented. It highlights the importance of standardization as the first step toward NoOps. Readers will learn how AI can improve coding, testing, infrastructure management, and software deployment. It covers AI-powered development tools, automated testing, self-managing infrastructure, and intelligent AI agents that handle deployments and fix problems automatically. Real-world case studies show how companies are already using AI to transform their DevOps processes. Beyond automation, NoOps also explores how AI will change job roles, requiring new skills and shifting how teams work. It discusses ethical concerns, team dynamics, and the future of AI-driven software development.Whether you're a developer, DevOps engineer, or tech leader, this book will help you understand and prepare for a future where AI plays a major role in software delivery.What you will learn:* How DevOps has evolved and why traditional methods struggle with modern software challenges.* How AI can automate coding, testing, and infrastructure management to streamline workflows.* Explore AI-driven DevOps strategies, including AI orchestration, self-healing infrastructure, and predictive analytics.* Discover real-world case studies of companies successfully using AI to improve software delivery.Who this book is for:Technical Executives, DevOps Engineers & SREs looking to automate testing, monitoring, infrastructure, and CI/CD. Software Developers who want to write better code faster using AI-driven development tools. QA Engineers & Testers responsible for functional, integration, and performance testing who need to automate and self-heal test cases with AI.Chapter 1 – The Evolution of DevOps.- Chapter 2 - Fragmented Software Development: Why DevOps Isn’t Always Enough.- Chapter 3 – The Case for Standardization: Building the Foundation for NoOps.- Chapter 4 - Cloud-Native & Data-Centric Approaches.- Chapter 5 – What “Good” Looks Like.- Chapter 6 – Generative AI for Coding & Unit Testing.- Chapter 7 – Generative AI for System & Integration Testing.- Chapter 8 – Generative AI for IaC & Data Provisioning.- Chapter 9 – AI-Orchestration.- Chapter 10 – Rise of AI Agents.- Chapter 11 – Rethinking Roles in NoOps World.- Chapter 12 – The Future: Beyond DevOps to NoOps.
3D-Druck mit Resin für Einsteiger
Resindruck verstehen und sicher anwendenResindruck gilt als präzise, aber anspruchsvoll. Der Autor erläutert alle dafür benötigten Grundlagen und Techniken leicht verständlich – egal, ob Sie kompletter Neueinsteiger oder Umsteiger vom FDM-Druck sind. Sie finden eine fundierte Einführung in Technik, Materialwahl, Sicherheitsaspekte und Vorbereitung und werden Schritt für Schritt vom Aufbau Ihres Druckers bis zum ersten erfolgreichen Druck begleitet.Von der Idee zum perfekten DruckSie erfahren, wie Sie geeignete 3D-Modelle finden oder selbst erstellen und optimal für den Resindruck vorbereiten. Anhand eines durchgängigen Beispielprojekts – dem Druck eines filigranen Drachenmodells – lernen Sie den gesamten Workflow kennen: slicen, Stützstrukturen setzen, drucken, waschen, aushärten und nachbearbeiten.Tipps und Anleitungen aus der PraxisAnschauliche Anleitungen und zahlreiche Praxistipps erleichtern den Einstieg und helfen, typische Fehler zu vermeiden. Der klare Aufbau und die langjährige Erfahrung des Autors machen dieses Buch zu einem hilfreichen Begleiter – ideal für alle, die sich mit Freude und Sicherheit an den Resindruck wagen möchten.Über den Autor:Stephan Knaus beschäftigt sich seit 2015 leidenschaftlich mit dem Thema 3D-Druck und kennt die typischen Herausforderungen beim Drucken mit Resin aus erster Hand. Als Betreiber der Community drucktipps3d.de verfasst er Testberichte, Tutorials und bietet täglich fundierte Unterstützung im Forum.
A Friendly Guide to Data Science
UNLOCK THE WORLD OF DATA SCIENCE—NO CODING REQUIRED.Curious about data science but not sure where to start? This book is a beginner-friendly guide to what data science is and how people use it. It walks you through the essential topics—what data analysis involves, which skills are useful, and how terms like “data analytics” and “machine learning” connect—without getting too technical too fast.Data science isn’t just about crunching numbers, pulling data from a database, or running fancy algorithms. It’s about asking the right questions, understanding the process from start to finish, and knowing what’s possible (and what’s not). This book teaches you all of that, while also introducing important topics like ethics, privacy, and security—because working with data means thinking about people, too.Whether you're a student exploring new skills, a professional navigating data-driven decisions, or someone considering a career change, this book is your friendly gateway into the world of data science, one of today’s most exciting fields. No coding or programming experience? No problem. You'll build a solid foundation and gain the confidence to engage with data science concepts— just as AI and data become increasingly central to everyday life.WHAT YOU WILL LEARN* Grasp foundational statistics and how it matters in data analysis and data science* Understand the data science project life cycle and how to manage a data science project* Examine the ethics of working with data and its use in data analysis and data science* Understand the foundations of data security and privacy* Collect, store, prepare, visualize, and present data* Identify the many types of machine learning and know how to gauge performance* Prepare for and find a career in data scienceWHO THIS BOOK IS FORA wide range of readers who are curious about data science and eager to build a strong foundation. Perfect for undergraduates in the early semesters of their data science degrees, as it assumes no prior programming or industry experience. Professionals will find particular value in the real-world insights shared through practitioner interviews. Business leaders can use it to better understand what data science can do for them and how their teams are applying it. And for career changers, this book offers a welcoming entry point into the field—helping them explore the landscape before committing to more intensive learning paths like degrees or boot camps.Part I: Foundations.- Chapter 1: Working with Numbers: What Is Data, Really?.- Chapter 2: Figuring Out What’s Going on in the Data: Descriptive Statistics.- Chapter 3: Setting Us Up for Success: The Inferential Statistics Framework and Experiments.- Chapter 4: Coming to Complex Conclusions: Inferential Statistics and Statistical Testing.- Chapter 5: Figuring Stuff Out: Data Analysis.- Chapter 6: Bringing It into the 21st Century: Data Science.- Chapter 7: A Fresh Perspective: The New Data Analytics.- Chapter 8: Keeping Everyone Safe: Data Security and Privacy.- Chapter 9: What’s Fair and Right: Ethical Considerations.- Part II: Doing Data Science.- Chapter 10: Grasping the Big Picture: Domain Knowledge.- Chapter 11: Tools of the Trade: Python and R.- Chapter 12: Trying Not to Make a Mess: Data Collection and Storage.- Chapter 13: For the Preppers: Data Gathering and Preprocessing.- Chapter 14: Ready for the Main Event: Feature Engineering, Selection, and Reduction.- Chapter 15: Not a Crystal Ball: Machine Learning.- Chapter 16: How’d We Do? Measuring the Performance of ML Techniques.- Chapter 17: Making the Computer Literate: Text and Speech Processing.- Chapter 18: A New Kind of Storytelling: Data Visualization and Presentation.- Chapter 19: This Ain’t Our First Rodeo: ML Applications.- Chapter 20: When Size Matters: Scalability and the Cloud.- Chapter 21: Putting It All Together: Data Science Solution Management.- Chapter 22: Errors in Judgment: Biases, Fallacies, and Paradoxes.- Part III: The Future.- Chapter 23: Getting Your Hands Dirty: How to Get Involved in Data Science.- Chapter 24: Learning and Growing: Expanding Your Skillset and Knowledge.- Chapter 25: Is It Your Future?: Pursuing a Career in Data Science.- Appendix A.
Artificial Intelligence in Neurological Disorders
THE BOOK GIVES INVALUABLE INSIGHTS INTO HOW ARTIFICIAL INTELLIGENCE IS REVOLUTIONIZING THE MANAGEMENT AND TREATMENT OF NEUROLOGICAL DISORDERS, EMPOWERING YOU TO STAY AHEAD IN THE RAPIDLY EVOLVING LANDSCAPE OF HEALTHCARE.Embark on a groundbreaking exploration of the intersection between cutting-edge technology and the intricate complexities of neurological disorders. Artificial Intelligence in Neurological Disorders: Management, Diagnosis and Treatment comprehensively introduces how artificial intelligence is becoming a vital ally in neurology, offering unprecedented advancements in management, diagnosis, and treatment. As the digital age converges with medical expertise, this book unveils a comprehensive roadmap for leveraging artificial intelligence to revolutionize neurological healthcare. Delve into the core principles that underpin AI applications in the field by exploring intricate algorithms that enhance the precision of diagnosis and how machine learning not only refines the understanding of neurological disorders but also paves the way for personalized treatment strategies tailored to individual patient needs. With compelling case studies and real-world examples, the realms of neuroscience and artificial intelligence converge, illustrating the symbiotic relationship that holds the promise of transforming patient care. Readers of this book will find it:* Provides future perspectives on advancing artificial intelligence applications in neurological disorders;* Focuses on the role of AI in diagnostics, delving into how advanced algorithms and machine learning techniques contribute to more accurate and timely diagnosis of neurological disorders;* Emphasizes practical integration of AI tools into clinical practice, offering insights into how healthcare professionals can leverage AI technology for more effective patient care;* Recognizes the interdisciplinary nature of neurology and AI, bridging the gap between these fields, making it accessible to healthcare professionals, researchers, and technologists;* Addresses the ethical implications of AI in healthcare, exploring issues such as data privacy, bias, and the responsible deployment of AI technologies in the neurological domain.AUDIENCEResearchers, scientists, industrialists, faculty members, healthcare professionals, hospital management, biomedical industrialists, engineers, and IT professionals interested in studying the intersection of AI and neurology. RISHABHA MALVIYA, PHD is an associate professor in the Department of Pharmacy in the School of Medical and Allied Services at Galgotias University with over 13 years of research experience. He has authored 57 books, 58 chapters, and over 150 research papers for national and international journals of repute, as well as 51 patents. His areas of interest include formulation optimization, nanoformulation, targeted drug delivery, localized drug delivery, and characterization of natural polymers as pharmaceutical excipients. SURAJ KUMAR is an assistant professor in the School of Medical and Allied Sciences at Galgotias University. He has published over ten papers in international journals and five book chapters. His research interests include sustainable polymeric fibers, nanoparticles, and controlled drug delivery. ADITYA SUSHIL SOLANKE, PHD is a Senior Resident in Neurosurgery at Byramjee Jeejeebhoy Government Medical College and Sassoon Hospital, India. He completed his Bachelor of Medicine, Bachelor of Surgery, and Masters in General Surgery from the Government Medical College in Nagpur. PRIYANSHI GOYAL, M.PHARM is an assistant professor in the School of Pharmacy at Mangalayatan University. She has authored seven review articles and two books and attended 14 national and international conferences and webinars. Her area of interest is treatment strategies for neurological disorders. KAPIL CHAUHAN, PHD is an emergency physician at Max Hospital in Dehradun, India. He completed his Bachelor of Medicine and Bachelor of Surgery from Teerthanker Mahavir Medical College and Masters in Emergency Medicine from Max Hospital.
Einstieg in die Datenanalyse mit Excel
In diesem Praxisbuch führt George Mount Daten- und Business-Analyst*innen durch zwei leistungsstarke in Excel integrierte Tools für die Datenanalyse: Power Pivot und Power Query. Er zeigt Ihnen, wie Sie mit Power Query Workflows zur Datenbereinigung erstellen und mit Power Pivot relationale Datenmodelle entwerfen. Darüber hinaus lernen Sie nützliche Funktionen wie dynamische Array-Formeln, KI-gestützte Methoden zur Aufdeckung von Trends und Mustern sowie die Integration von Python zur Automatisierung von Analysen und Berichten kennen.George Mount zeigt Ihnen, wie Sie Ihre Excel-Kenntnisse weiter ausbauen und das Programm effektiv für die moderne Datenanalyse nutzen. Das Buch gibt einen klaren Überblick über das volle Potenzial von Excel und hilft Ihnen, auch ohne Power BI oder anderer Zusatzsoftware Daten effektiv aufzubereiten und zu analysieren. Viele Übungen zur Vertiefung des Gelernten runden das Buch ab.Dieses Buch zeigt Ihnen, wie Sie:wiederholbare Datenbereinigungsprozesse für Excel mit Power Query erstellenrelationale Datenmodelle und Analysen mit Power Pivot entwickelnDaten schnell mit dynamischen Arrays abrufenKI nutzen, um Muster und Trends in Excel aufzudeckenPython-Funktionen in Excel für automatisierte Analysen und Berichte integrierenLeseprobe (pdf-Link)Über den Autor:George Mount ist Gründer von Stringfest Analytics, einer Beratungsfirma, die sich auf die berufliche Weiterbildung im Bereich Datenanalyse spezialisiert hat. Er hat mit führenden Bootcamps, Lernplattformen und Fachorganisationen zusammengearbeitet, um Menschen dabei zu unterstützen, in der Datenanalyse erfolgreich zu sein. George Mount wurde mit dem „Microsoft Most Valuable Professional“-Award für herausragende technische Expertise und sein Engagement in der Excel-Community ausgezeichnet.
Large Language Models selbst programmieren
LLMs selbst erstellen und von Grund auf verstehen!Der Bestseller aus den USA ist jetzt endlich in deutscher Übersetzung erhältlich und bietet den idealen Einstieg in die Welt der Large Language Models (LLMs). Auf dem eigenen Laptop entwickeln, trainieren und optimieren Sie ein LLM, das mit GPT-2 vergleichbar ist – ganz ohne vorgefertigte Bibliotheken. Bestsellerautor Sebastian Raschka erklärt die Grundlagen und Vorgehensweise Schritt für Schritt und leicht verständlich.Das Buch nimmt Sie mit auf eine spannende Reise in die Blackbox der generativen KI: Sie programmieren ein LLM-Basismodell, bauen daraus einen Textklassifikator und schließlich einen eigenen Chatbot, den Sie als persönlichen KI-Assistenten nutzen können. Dabei lernen Sie nicht nur die technischen Details kennen, sondern auch, wie Sie Datensätze vorbereiten, Modelle mit eigenen Daten verfeinern und mithilfe von menschlichem Feedback verbessern. Ein praxisnaher Leitfaden für alle, die wirklich verstehen wollen, wie LLMs funktionieren – weil sie ihren eigenen gebaut haben.Über den Autor:Sebastian Raschka, PhD, arbeitet sehr mehr als einem Jahrzehnt im Bereich Machine Learning und KI. Er ist Staff Research Engineer bei Lightning AI, wo er LLM-Forschung betreibt und Open-Source-Software entwickelt. Sebastian ist nicht nur Forscher, sondern hat auch eine große Leidenschaft für die Vermittlung von Wissen. Bekannt ist er für seine Bestseller zu Machine Learning mit Python und seine Beiträge zu Open Source.Richtet sich an:Data ScientistsPython-Programmierer*innen, die sich tiefer in die Funktionsweise von LLMs einarbeiten möchten und ML-Grundkenntnisse haben
Einstieg in die Datenanalyse mit Excel
Daten analysieren und Prozesse automatisieren mit Excel - Automatisieren Sie Ihre Datenbereinigung in Excel mit Power Query. - Strukturieren Sie Ihre Daten mit Power Pivot. - Nutzen Sie Python für automatisierte Analysen und Berichterstattung. In diesem Praxisbuch führt George Mount Daten- und Business-Analyst*innen durch zwei leistungsstarke in Excel integrierte Tools für die Datenanalyse: Power Pivot und Power Query. Er zeigt Ihnen, wie Sie mit Power Query Workflows zur Datenbereinigung erstellen und mit Power Pivot relationale Datenmodelle entwerfen. Darüber hinaus lernen Sie nützliche Funktionen wie dynamische Array-Formeln, KI-gestützte Methoden zur Aufdeckung von Trends und Mustern sowie die Integration von Python zur Automatisierung von Analysen und Berichten kennen. George Mount zeigt Ihnen, wie Sie Ihre Excel-Kenntnisse weiter ausbauen und das Programm effektiv für die moderne Datenanalyse nutzen. Das Buch gibt einen klaren Überblick über das volle Potenzial von Excel und hilft Ihnen, auch ohne Power BI oder anderer Zusatzsoftware Daten effektiv aufzubereiten und zu analysieren. Viele Übungen zur Vertiefung des Gelernten runden das Buch ab. Dieses Buch zeigt Ihnen, wie Sie: - wiederholbare Datenbereinigungsprozesse für Excel mit Power Query erstellen - relationale Datenmodelle und Analysen mit Power Pivot entwickeln - Daten schnell mit dynamischen Arrays abrufen - KI nutzen, um Muster und Trends in Excel aufzudecken - Python-Funktionen in Excel für automatisierte Analysen und Berichte integrieren
Generative KI im Kontext der Wirtschaftsinformatik
Generative KI stellt eine neue wichtige Technologie dar, die in der Lage ist, betriebliche Prozesse und Anwendungen zu automatisieren und zu optimieren. Inwieweit sich Generative KI wie ChatGPT für den Einsatz im Kontext der Wirtschaftsinformatik eignet, wird im Rahmen dieses Herausgeber-Bandes thematisiert. Dazu untersuchen einige Autoren-Teams den Einsatz von ChatGPT anhand verschiedener Beispiele. Die Beispiele umfassen sowohl Fragestellungen zum Lernen an der Hochschule wie zum Beispiel zur Texterstellung und zur Mathematik als auch zum betrieblichen Einsatz in der unternehmerischen Praxis in verschiedenen betriebswirtschaftlichen Anwendungsfeldern. Das Buch bietet wichtige Informationen, die für Praktiker ebenso relevant sind wie für Studierende und Lehrende..- Teil 1 – Einleitung..- 1 Generative KI im Kontext digitaler Ressourcen, Lehre, Innovation und Kundenbeziehung..- Teil 2 – Digitale Ressourcen und Lehre..- 2 Versionsverwaltung von Dokumenten in Zeiten von Cloud, Cybersecurity und KI..- 3 "Versteht" ein System wie ChatGPT seine eigenen Texte zur Mathematik?..- 4 Ein Vergleich von Wirtschaftsinformatik-Lehrmaterialien mit generierten Erklärungstexten..- Teil 3 – Innovationsmanagement..- 5 Die Zukunft des Technologiescoutings: Wie ein digitales Transfertool von KI-basierter Contentgenerierung profitiert..- 6 Zukunftsszenarien Szenarioanalysen mit ChatGPT – Potenziale und Grenzen KI-gestützter Vorausschau..- Teil 4 – Kundenanalyse und Kundeninteraktion..- 7 Data Science und KI – made by Data Science und KI..- 8 Wie Produktivitätsgewinne in der Kundenbetreuung durch KI-basierte Textgenerierung erzielt werden – heutige und zukünftige Einsatzmöglichkeiten..- 9 Multimodales Fenster in die Vergangenheit der ehemaligen Vauban-Festung Saarlouis mittels ChatGPT.
Emerging Smart Agricultural Practices Using Artificial Intelligence
BRING THE LATEST TECHNOLOGY TO BEAR IN THE FIGHT FOR SUSTAINABLE AGRICULTURE WITH THIS TIMELY VOLUMEArtificial intelligence (AI) has the potential to revolutionize virtually every area of research and scientific practice, including agriculture. With AI solutions emerging to drive higher yields, produce increased resource efficiency, and foster sustainability, there is an urgent need for a volume outlining this progress and charting its future course. Emerging Smart Agricultural Practices Using Artificial Intelligence meets this need with a deep dive into the rapidly developing intersection of agriculture and artificial intelligence. Taking an interdisciplinary approach which applies data science, computer science, and engineering techniques, the book provides cutting-edge insights on the latest advancements in AI-driven agricultural practices. The result is an absolutely critical tool in the ongoing fight to develop sustainable world agriculture. In addition, this book provides:* Case studies and real-world applications of new techniques throughout* Detailed discussion of agricultural applications for AI-driven technologies such as machine learning, computer vision, and data analytics * A regional approach showcasing international best practices and addressing the varying needs of farmers worldwideEmerging Smart Agricultural Practices Using Artificial Intelligence is ideal for agricultural professionals and scientists, as well as data scientists, technologists, and agricultural policymakers. ASHISH KUMAR, PHD, is an Associate Professor with Bennett University, Greater Noida, U.P. India. He has published widely on subjects including object tracking, image processing, artificial intelligence, and medical imaging analysis, and is a member of the IEEE. JAI PRAKASH VERMA, PHD, is an Associate Professor in the Department of Computer Science and Engineering, Nirma University, Ahmedabad, India. He offers customized training on big data analytics to the Indian Navy, SAC-ISRO scientists in Ahmedabad, and other experts from industry and academia. RACHNA JAIN, PHD, is an Associate Professor in the Department of Information Technology Bhagwan Parshuram Institute of Technology. She has 18+ years of academic/research experience with more than 100+ publications in various international conferences and international journals (Scopus/ISI/SCI) of high repute.
Maschinelles Lernen lernen
Maschinelles Lernen prägt zunehmend unseren Alltag, doch fehlt bislang eine Fachdidaktik, die das Thema als Lerngegenstand für Schulen und Hochschulen systematisch untersucht. In der vorliegenden Arbeit werden Methoden der fachdidaktische Entwicklungsforschung genutzt, um unter Einbezug verschiedener Naturwissenschaftsdidaktiken eine Lehr-Lernumgebung zu maschinellem Lernen für Ingenieurstudierende zu entwickeln. Die qualitative und quantitative empirische Untersuchung des entwickelten Materials in Design-Experimenten liefert Einsichten in die Lernprozesse der Studierenden, um den didaktischen Diskurs zu bereichern und Hinweise für die Lehrpraxis abzuleiten.Einleitung.- Maschinelles Lernen.- Spezifizierung und Strukturierung der Lerngegenstände.- Design(weiter)entwicklung.- Designentwicklung und Datenerhebung.- Methoden der Datenanalyse.- Die Wirksamkeitsanalyse.- Evaluation der Mikrozyklen.- Erstellung und Nutzung von ML-Modellen.- Das individuelle Modellkonzept.- Die Weiterentwicklung des Designs und der Wirksamkeitszyklus.- Zusammenfassung und Diskussion.- Ausblick.
AWS Certified Machine Learning Engineer Study Guide
PREPARE FOR THE AWS MACHINE LEARNING ENGINEER EXAM SMARTER AND FASTER AND GET JOB-READY WITH THIS EFFICIENT AND AUTHORITATIVE RESOURCEIn AWS Certified Machine Learning Engineer Study Guide: Associate (MLA-C01) Exam, veteran AWS Practice Director at Trace3—a leading IT consultancy offering AI, data, cloud and cybersecurity solutions for clients across industries—Dario Cabianca delivers a practical and up-to-date roadmap to preparing for the MLA-C01 exam. You'll learn the skills you need to succeed on the exam as well as those you need to hit the ground running at your first AI-related tech job.You'll learn how to prepare data for machine learning models on Amazon Web Services, build, train, refine models, evaluate model performance, deploy and secure your machine learning applications against bad actors.INSIDE THE BOOK:* Complimentary access to the Sybex online test bank, which includes an assessment test, chapter review questions, practice exam, flashcards, and a searchable key term glossary* Strategies for selecting and justifying an appropriate machine learning approach for specific business problems and identifying the most efficient AWS solutions for those problems* Practical techniques you can implement immediately in an artificial intelligence and machine learning (AI/ML) development or data science rolePerfect for everyone preparing for the AWS Certified Machine Learning Engineer -- Associate exam, AWS Certified Machine Learning Engineer Study Guide is also an invaluable resource for those preparing for their first role in AI or data science, as well as junior-level practicing professionals seeking to review the fundamentals with a convenient desk reference.ABOUT THE AUTHORDARIO CABIANCA is the AWS Practice Director at Trace3—a leading IT consultancy and AWS Advanced Consulting Partner—offering AI, data, cloud and cybersecurity solutions. He is the author of Google Cloud Platform (GCP) Professional Cloud Security Engineer Certification Companion and Google Cloud Platform (GCP) Professional Cloud Network Engineer Certification Companion. Dario has collaborated with leading global consulting firms and enterprises for over 20 years, delivering impactful solutions in enterprise architecture, cloud computing, cybersecurity, and artificial intelligence. ContentsChapter 1Introduction to Machine Learning1Understanding Artificial Intelligence2Data, Information, Knowledge3Data3Information4Knowledge5Understanding Machine Learning6ML Lifecycle6Define ML Problem6Collect Data8Process Data8Choose Algorithm8Train Model9Evaluate Model9Deploy Model9Derive Inference11Monitor Model11ML Concepts11Features11Target Variable12Optimization Problem12Objective Function13ML Algorithms vs. ML Models13Differences Between ML and AI14Understanding Deep Learning16Introduction to Neural Networks16Structure of a Neural Network16Neuron16Input Layer18Hidden Layers18Output Layer18How Neural Networks Work18Neural Networks Types19Artificial Neural Networks20Deep Neural Networks20Convolutional Neural Networks20Recurrent Neural Networks20Differences Between DL and ML21Case Studies21Case Study 1: Mobileye’s Autonomous Driving Technology21Case Study 2: Leidos’ Healthcare ML Applications21Summary22Exam Essentials23Review Questions24Chapter 2Data Ingestion and Storage27Introducing Ingestion and Storage28Ingesting and Storing Data28Data Formats and Ingestion Techniques31Choosing AWS Ingestion Services34Amazon Data Firehose35Amazon Kinesis Data Streams35Amazon Managed Streaming for Apache Kafka (MSK)36Amazon Managed Service for Apache Flink38AWS DataSync39AWS Glue40Choosing AWS Storage Services41Amazon Simple Storage Service (S3)42Amazon Elastic File System (EFS)45Amazon FSx for Lustre47Amazon FSx for NetApp ONTAP49Amazon FSx for Windows File Server50Amazon FSx for OpenZFS51Amazon Elastic Block Storage (EBS)51Amazon Relational Database Service (RDS)52Amazon DynamoDB52Troubleshooting53Summary54Exam Essentials55Review Questions57Chapter 4Model Selection61Understanding AWS AI Services63Vision64Amazon Rekognition64Amazon Textract65Speech66Amazon Polly66Amazon Transcribe67Language67Amazon Translate67Amazon Comprehend68Chatbot69Amazon Lex69Recommendation70Amazon Personalize70Generative AI71Amazon Bedrock71Developing Models with Amazon SageMaker Built-in Algorithms81Supervised ML Algorithms81General Regression and Classification Algorithms83Recommendation102Forecasting104Unsupervised ML Algorithms105Clustering105Dimensionality Reduction113Topic Modeling119Anomaly Detection121Textual Analysis123BlazingText124Sequence-to-Sequence126Image Processing127Image Classification127Object Detection128Semantic Segmentation130Criteria for Model Selection131Summary132Exam Essentials133Review Questions136Chapter 5Model Training and Evaluation141Training143Local Training144Remote Training145Distributed Training146Monitoring Training Jobs147Debugging Training Jobs148Hyperparameter Tuning149Model Parameter and Hyperparameter151Exploring the Hyperparameter Space with Amazon SageMaker AI Automatic Model Tuning152Evaluation Metrics154Classification Problem Metrics154Regression Problem Metrics160Hyperparameter Tuning Techniques164Manual Search164Grid Search165Random Search165Bayesian Search165Multi-algorithm Optimization166Managing Bias and Variance Trade-Off166Addressing Overfitting and Underfitting168Underfitting168Overfitting170Regularization170Advanced Techniques173Model Performance Evaluation173Performance Evaluation Methods173K-Fold Cross-Validation174Random Train-Test Split175Holdout Set176Bootstrap176Evaluating Foundation Models177Automatic Evaluations177Human Evaluations177LLM-as-a-Judge177Programmatic Evaluations177Knowledge Base Evaluations177Deep Dive Model Tuning Example177Summary185Exam Essentials187Review Questions190Chapter 6Model Deployment and Orchestration193AWS Model Deployment Services194Deploying AI Services195Amazon Rekognition196Amazon Textract197Amazon Polly197Amazon Transcribe198Amazon Comprehend198Amazon Lex199Amazon Personalize199Amazon Bedrock200Deploying Your Model201Infrastructure Selection Considerations202Managed Model Deployments203Unmanaged Model Deployments211Optimizing ML Models for Edge Devices216Advanced Model Deployment Techniques218Autoscaling Endpoints218Deployment and Testing Strategies221Blue/Green Deployment221Orchestrating ML Workflows227Introducing Amazon SageMaker Pipelines228Code Repository and Version Control228Introducing Amazon SageMaker Model Registry229CI/CD230MLOps Orchestration230AWS Step Functions231Amazon Managed Workflows for Apache Airflow232Choosing an Orchestration Tool232Automating Model Building and Deployment233Define the Workflow Steps234Create and Configure Pipeline Steps234Define the Pipeline237Set Up Triggers and Schedules237Execute the Pipeline238Key Considerations238Deep-Dive Model Deployment Example238Summary247Exam Essentials248Review Questions250Chapter 7Model Monitoring and Cost Optimization253Monitoring Model Inference255Drifts in Models256Techniques to Monitor Data Quality and Model Performance257Monitoring Workflow259Design Principles for Monitoring261Operational Excellence Pillar261Security Pillar262Reliability Pillar263Performance Efficiency Pillar264Cost Optimization Pillar266Sustainability Pillar269Monitoring Infrastructure and Cost270Monitoring and Observability Services271Amazon CloudWatch Logs Insights272Amazon EventBridge273AWS CloudTrail274AWS X-Ray274Amazon GuardDuty275Amazon Inspector276AWS Security Hub277Cost Tracking and Optimization Services278AWS Cost Explorer278AWS Cost and Usage Reports279AWS Trusted Advisor280AWS Budgets280Pricing Models281Summary283Exam Essentials284Review Questions286Chapter 8Model Security289Security Design Principles290Implement a Strong Identity Foundation290Apply Security at all Layers291Enable Traceability292Protect Your Data (At-Rest, In-Use, and In-Transit)293Automate Security Processes294Prepare for Security Events295Securing AWS Services295Securing Identities with IAM296Identities296Access Policies302Securing Infrastructure and Data305Network Isolation with VPC305Private Connectivity306Data Protection306Monitoring and Auditing307Ensuring Compliance307Summary308Exam Essentials309Review Questions311
96 Common Challenges in Power Query
This comprehensive guide is designed to address the most frequent and challenging issues faced by users of Power Query, a powerful data transformation tool integrated into Excel, Power BI, and Microsoft Azure. By tackling 96 real-world problems with practical, step-by-step solutions, this book is an essential resource for data analysts, Excel enthusiasts, and Power BI professionals. It aims to enhance your data transformation skills and improve efficiency in handling complex data sets.Structured into 12 chapters, the book covers specific areas of Power Query such as data extraction, referencing, column splitting and merging, sorting and filtering, and pivoting and unpivoting tables. You will learn to combine data from Excel files with varying column names, handle multi-row headers, perform advanced filtering, and manage missing values using techniques such as linear interpolation and K-nearest neighbors (K-NN) imputation. The book also dives into advanced Power Query functions such as Table.Group, List.Accumulate, and List.Generate, explored through practical examples such as calculating running totals and implementing complex grouping and iterative processes. Additionally, it covers crucial topics such as error-handling strategies, custom function creation, and the integration of Python and R with Power Query.In addition to providing explanations on the use of functions and the M language for solving real-world challenges, this book discusses optimization techniques for data cleaning processes and improving computational speed. It also compares the execution time of functions across different patterns and proposes the optimal approach based on these comparisons.In today’s data-driven world, mastering Power Query is crucial for accurate and efficient data processing. But as data complexity grows, so do the challenges and pitfalls that users face. This book serves as your guide through the noise and your key to unlocking the full potential of Power Query. You’ll quickly learn to navigate and resolve common issues, enabling you to transform raw data into actionable insights with confidence and precision.WHAT YOU WILL LEARN* Master data extraction and transformation techniques for various Excel file structures* Apply advanced filtering, sorting, and grouping methods to organize and analyze data* Leverage powerful functions such as Table.Group, List.Accumulate, and List.Generate for complex transformations* Optimize queries to execute faster* Create and utilize custom functions to handle iterative processes and advanced list transformation* Implement effective error-handling strategies, including removing erroneous rows and extracting error reasons* Customize Power Query solutions to meet specific business needs and share custom functions across filesWHO THIS BOOK IS FORAspiring and developing data professionals using Power Query in Excel or Power BI who seek practical solutions to enhance their skills and streamline complex data transformation workflowsOMID MOTAMEDISEDEH is a seasoned data analyst, educator, and author with extensive experience in business intelligence and data visualization. Based in Australia, he specializes in leveraging Microsoft tools to provide practical solutions for complex data challenges. Omid is the author of eight books, seven written in Persian and one in English, covering a wide range of topics, including Excel, data visualization, and Power Query. His works are known for their clear explanations and actionable insights, catering to data professionals, students, and educators alike. Holding a PhD in Industrial Engineering, Omid combines academic depth with over a decade of hands-on experience in the IT industry, where he has served as a consultant and manager. His professional journey includes implementing data-driven strategies, optimizing processes, and mentoring teams to achieve their analytical goals. Omid is deeply committed to education and knowledge sharing. Beyond writing, he conducts workshops, creates YouTube tutorials, and mentors aspiring data analysts.Chapter 1: Data Extraction from Files.- Chapter 2: Referencing.- Chapter 3: Sorting & Filtering.- Chapter 4: Column Splitting & Merging.- Chapter 5: Pivoting & Unpivoting Tables.- Chapter 6: Grouping Rows with Table.Group.- Chapter 7: Merging & Appending Tables.- Chapter 8: Handling Missing Values.- Chapter 9: Looping in Power Query.- Chapter 10: Leveraging Scripting and External Integrations in Power Query.- Chapter 11: Error Handling Strategies.- Chapter 12: Custom Functions.
Software Engineering Made Easy
Learn how to write good code for humans. This user-friendly book is a comprehensive guide to writing clear and bug-free code. It integrates established programming principles and outlines expert-driven rules to prevent you from over-complicating your code.You’ll take a practical approach to programming, applicable to any programming language and explore useful advice and concrete examples in a concise and compact form. Sections on Single Responsibility Principle, naming, levels of abstraction, testing, logic (if/else), interfaces, and more, reinforce how to effectively write low-complexity code. While many of the principles addressed in this book are well-established, it offers you a single resource._Software Engineering Made Easy_ modernizes classic software programming principles with quick tips relevant to real-world applications. Most importantly, it is written with a keen awareness of how humans think. The end-result is human-readable code that improves maintenance, collaboration, and debugging—critical for software engineers working together to make purposeful impacts in the world.WHAT YOU WILL LEARN* Understand the essence of software engineering.* Simplify your code using expert techniques across multiple languages.* See how to structure classes.* Manage the complexity of your code by using level abstractions.* Review test functions and explore various types of testing.WHO THIS BOOK IS FORIntermediate programmers who have a basic understanding of coding but are relatively new to the workforce. Applicable to any programming language, but proficiency in C++ or Python is preferred. Advanced programmers may also benefit from learning how to deprogram bad habits and de-complicate their code.MARCO GÄHLER began his career studying physics at ETH Zurich before transitioning to software engineering. In 2018, he joined Zurich Instruments, where he developed electronic devices used in quantum computing. Throughout his career, Marco has observed the pitfalls in code written by self-taught developers, for example PhD students, and recognized the need for clear, practical guidance on simple programming practices. This book reflects his preference for clear, short functions, and minimal class usage, aiming to make good programming practices accessible to all.Chapter 1: Fundamentals of Software Engineering.- Chapter 2: Components of Code.- Chapter 3: Classes.- Chapter 4:Testing.- Chapter 5: Design Principles.- Chapter 6: Programming.- Chapter 7: High-Level Design.- Chapter 8: Refactoring.- Chapter 9: Other Common Topics.- Chapter 10: Collaborating.- Glossary.
Microsoft Power Platform Solution Architect Certification Companion
This comprehensive guide book equips you with the knowledge and confidence needed to prep for the exam and thrive as a Power Platform Solution Architect.The book starts with a foundation for successful solution architecture, emphasizing essential skills such as requirements gathering, governance, and security. You will learn to navigate customer discovery, translate business needs into technical requirements, and design solutions that address both functional and non-functional needs. The second part of the book delves into the Microsoft Power Platform ecosystem, offering an in-depth look at its core components—Power Apps, Power Automate, Power BI, Microsoft Copilot, and Robotic Process Automation (RPA).Detailed insights into data modeling, security strategies, and AI integration will guide you in building scalable, secure solutions. Coverage of application life cycle management, which empowers solution architects to design, implement, and deploy Power Platform solutions effectively, is discussed next. You will then go through real-world scenarios, giving you a practical understanding of the challenges and considerations in managing Power Platform projects within a business context.The book concludes with strategies for continuous learning and resources for professional development, including practice questions to assess knowledge and readiness for the PL-600 exam. After reading the book, you will be ready to take the exam and become a successful Power Platform Solution Architect.WHAT YOU WILL LEARN* Understand the Solution Architect's role, responsibilities, and strategic approaches to successfully navigate projects* Master the basics of Power Platform Solution Architecture* Understand governance, security, and integration concepts in real-world scenarios* Design and deploy effective business solutions using Power Platform components* Gain the skills necessary to prep for the PL-600 certification examWHO THIS BOOK IS FORProfessionals pursuing Microsoft PL-600 Solution Architect certification and IT consultants and developers transitioning to solution architect rolesLOGANATHAN K is a seasoned Microsoft Certified Trainer (MCT) and Functional Consultant with extensive experience in Power Platform, Dynamics 365, and business process automation. Currently working as a Functional Consultant in a Microsoft Partner company, he is passionate about helping organizations drive digital transformation through the Power Platform and Microsoft Business Applications.As the author of the popular blog LK Techs (lktechs.com), he shares knowledge of Microsoft technologies, certification paths, and real-world use cases to help individuals build careers in IT. He holds several advanced certifications, including those in Microsoft Business Central and the Power Platform, and regularly conducts training sessions for students, professionals, and educators.Chapter 1: Getting Started with the PL-600 Exam: Overview and Essentials.- Chapter 2: Building a Successful Solution Architect Framework: Key Stages and Skills.- Chapter 3: Governance, Architecture, and Core Components in Power Platform and Dynamics 365.- Chapter 4: Leveraging Microsoft Copilot, RPA, and Securing Data Models in Power Platform Solutions.- Chapter 5: Implementing Analytics, AI, and ALM Strategies for Power Platform Success.- Chapter 6: Evaluating Your Expertise Through Real-World Scenarios.
Emerging Smart Agricultural Practices Using Artificial Intelligence
BRING THE LATEST TECHNOLOGY TO BEAR IN THE FIGHT FOR SUSTAINABLE AGRICULTURE WITH THIS TIMELY VOLUMEArtificial intelligence (AI) has the potential to revolutionize virtually every area of research and scientific practice, including agriculture. With AI solutions emerging to drive higher yields, produce increased resource efficiency, and foster sustainability, there is an urgent need for a volume outlining this progress and charting its future course. Emerging Smart Agricultural Practices Using Artificial Intelligence meets this need with a deep dive into the rapidly developing intersection of agriculture and artificial intelligence. Taking an interdisciplinary approach which applies data science, computer science, and engineering techniques, the book provides cutting-edge insights on the latest advancements in AI-driven agricultural practices. The result is an absolutely critical tool in the ongoing fight to develop sustainable world agriculture. In addition, this book provides:* Case studies and real-world applications of new techniques throughout* Detailed discussion of agricultural applications for AI-driven technologies such as machine learning, computer vision, and data analytics * A regional approach showcasing international best practices and addressing the varying needs of farmers worldwideEmerging Smart Agricultural Practices Using Artificial Intelligence is ideal for agricultural professionals and scientists, as well as data scientists, technologists, and agricultural policymakers. ASHISH KUMAR, PHD, is an Associate Professor with Bennett University, Greater Noida, U.P. India. He has published widely on subjects including object tracking, image processing, artificial intelligence, and medical imaging analysis, and is a member of the IEEE. JAI PRAKASH VERMA, PHD, is an Associate Professor in the Department of Computer Science and Engineering, Nirma University, Ahmedabad, India. He offers customized training on big data analytics to the Indian Navy, SAC-ISRO scientists in Ahmedabad, and other experts from industry and academia. RACHNA JAIN, PHD, is an Associate Professor in the Department of Information Technology Bhagwan Parshuram Institute of Technology. She has 18+ years of academic/research experience with more than 100+ publications in various international conferences and international journals (Scopus/ISI/SCI) of high repute.
AWS Certified Machine Learning Engineer Study Guide
PREPARE FOR THE AWS MACHINE LEARNING ENGINEER EXAM SMARTER AND FASTER AND GET JOB-READY WITH THIS EFFICIENT AND AUTHORITATIVE RESOURCEIn AWS Certified Machine Learning Engineer Study Guide: Associate (MLA-C01) Exam, veteran AWS Practice Director at Trace3—a leading IT consultancy offering AI, data, cloud and cybersecurity solutions for clients across industries—Dario Cabianca delivers a practical and up-to-date roadmap to preparing for the MLA-C01 exam. You'll learn the skills you need to succeed on the exam as well as those you need to hit the ground running at your first AI-related tech job.You'll learn how to prepare data for machine learning models on Amazon Web Services, build, train, refine models, evaluate model performance, deploy and secure your machine learning applications against bad actors.INSIDE THE BOOK:* Complimentary access to the Sybex online test bank, which includes an assessment test, chapter review questions, practice exam, flashcards, and a searchable key term glossary* Strategies for selecting and justifying an appropriate machine learning approach for specific business problems and identifying the most efficient AWS solutions for those problems* Practical techniques you can implement immediately in an artificial intelligence and machine learning (AI/ML) development or data science rolePerfect for everyone preparing for the AWS Certified Machine Learning Engineer -- Associate exam, AWS Certified Machine Learning Engineer Study Guide is also an invaluable resource for those preparing for their first role in AI or data science, as well as junior-level practicing professionals seeking to review the fundamentals with a convenient desk reference.ABOUT THE AUTHORDARIO CABIANCA is the AWS Practice Director at Trace3—a leading IT consultancy and AWS Advanced Consulting Partner—offering AI, data, cloud and cybersecurity solutions. He is the author of Google Cloud Platform (GCP) Professional Cloud Security Engineer Certification Companion and Google Cloud Platform (GCP) Professional Cloud Network Engineer Certification Companion. Dario has collaborated with leading global consulting firms and enterprises for over 20 years, delivering impactful solutions in enterprise architecture, cloud computing, cybersecurity, and artificial intelligence. ContentsChapter 1Introduction to Machine Learning1Understanding Artificial Intelligence2Data, Information, Knowledge3Data3Information4Knowledge5Understanding Machine Learning6ML Lifecycle6Define ML Problem6Collect Data8Process Data8Choose Algorithm8Train Model9Evaluate Model9Deploy Model9Derive Inference11Monitor Model11ML Concepts11Features11Target Variable12Optimization Problem12Objective Function13ML Algorithms vs. ML Models13Differences Between ML and AI14Understanding Deep Learning16Introduction to Neural Networks16Structure of a Neural Network16Neuron16Input Layer18Hidden Layers18Output Layer18How Neural Networks Work18Neural Networks Types19Artificial Neural Networks20Deep Neural Networks20Convolutional Neural Networks20Recurrent Neural Networks20Differences Between DL and ML21Case Studies21Case Study 1: Mobileye’s Autonomous Driving Technology21Case Study 2: Leidos’ Healthcare ML Applications21Summary22Exam Essentials23Review Questions24Chapter 2Data Ingestion and Storage27Introducing Ingestion and Storage28Ingesting and Storing Data28Data Formats and Ingestion Techniques31Choosing AWS Ingestion Services34Amazon Data Firehose35Amazon Kinesis Data Streams35Amazon Managed Streaming for Apache Kafka (MSK)36Amazon Managed Service for Apache Flink38AWS DataSync39AWS Glue40Choosing AWS Storage Services41Amazon Simple Storage Service (S3)42Amazon Elastic File System (EFS)45Amazon FSx for Lustre47Amazon FSx for NetApp ONTAP49Amazon FSx for Windows File Server50Amazon FSx for OpenZFS51Amazon Elastic Block Storage (EBS)51Amazon Relational Database Service (RDS)52Amazon DynamoDB52Troubleshooting53Summary54Exam Essentials55Review Questions57Chapter 4Model Selection61Understanding AWS AI Services63Vision64Amazon Rekognition64Amazon Textract65Speech66Amazon Polly66Amazon Transcribe67Language67Amazon Translate67Amazon Comprehend68Chatbot69Amazon Lex69Recommendation70Amazon Personalize70Generative AI71Amazon Bedrock71Developing Models with Amazon SageMaker Built-in Algorithms81Supervised ML Algorithms81General Regression and Classification Algorithms83Recommendation102Forecasting104Unsupervised ML Algorithms105Clustering105Dimensionality Reduction113Topic Modeling119Anomaly Detection121Textual Analysis123BlazingText124Sequence-to-Sequence126Image Processing127Image Classification127Object Detection128Semantic Segmentation130Criteria for Model Selection131Summary132Exam Essentials133Review Questions136Chapter 5Model Training and Evaluation141Training143Local Training144Remote Training145Distributed Training146Monitoring Training Jobs147Debugging Training Jobs148Hyperparameter Tuning149Model Parameter and Hyperparameter151Exploring the Hyperparameter Space with Amazon SageMaker AI Automatic Model Tuning152Evaluation Metrics154Classification Problem Metrics154Regression Problem Metrics160Hyperparameter Tuning Techniques164Manual Search164Grid Search165Random Search165Bayesian Search165Multi-algorithm Optimization166Managing Bias and Variance Trade-Off166Addressing Overfitting and Underfitting168Underfitting168Overfitting170Regularization170Advanced Techniques173Model Performance Evaluation173Performance Evaluation Methods173K-Fold Cross-Validation174Random Train-Test Split175Holdout Set176Bootstrap176Evaluating Foundation Models177Automatic Evaluations177Human Evaluations177LLM-as-a-Judge177Programmatic Evaluations177Knowledge Base Evaluations177Deep Dive Model Tuning Example177Summary185Exam Essentials187Review Questions190Chapter 6Model Deployment and Orchestration193AWS Model Deployment Services194Deploying AI Services195Amazon Rekognition196Amazon Textract197Amazon Polly197Amazon Transcribe198Amazon Comprehend198Amazon Lex199Amazon Personalize199Amazon Bedrock200Deploying Your Model201Infrastructure Selection Considerations202Managed Model Deployments203Unmanaged Model Deployments211Optimizing ML Models for Edge Devices216Advanced Model Deployment Techniques218Autoscaling Endpoints218Deployment and Testing Strategies221Blue/Green Deployment221Orchestrating ML Workflows227Introducing Amazon SageMaker Pipelines228Code Repository and Version Control228Introducing Amazon SageMaker Model Registry229CI/CD230MLOps Orchestration230AWS Step Functions231Amazon Managed Workflows for Apache Airflow232Choosing an Orchestration Tool232Automating Model Building and Deployment233Define the Workflow Steps234Create and Configure Pipeline Steps234Define the Pipeline237Set Up Triggers and Schedules237Execute the Pipeline238Key Considerations238Deep-Dive Model Deployment Example238Summary247Exam Essentials248Review Questions250Chapter 7Model Monitoring and Cost Optimization253Monitoring Model Inference255Drifts in Models256Techniques to Monitor Data Quality and Model Performance257Monitoring Workflow259Design Principles for Monitoring261Operational Excellence Pillar261Security Pillar262Reliability Pillar263Performance Efficiency Pillar264Cost Optimization Pillar266Sustainability Pillar269Monitoring Infrastructure and Cost270Monitoring and Observability Services271Amazon CloudWatch Logs Insights272Amazon EventBridge273AWS CloudTrail274AWS X-Ray274Amazon GuardDuty275Amazon Inspector276AWS Security Hub277Cost Tracking and Optimization Services278AWS Cost Explorer278AWS Cost and Usage Reports279AWS Trusted Advisor280AWS Budgets280Pricing Models281Summary283Exam Essentials284Review Questions286Chapter 8Model Security289Security Design Principles290Implement a Strong Identity Foundation290Apply Security at all Layers291Enable Traceability292Protect Your Data (At-Rest, In-Use, and In-Transit)293Automate Security Processes294Prepare for Security Events295Securing AWS Services295Securing Identities with IAM296Identities296Access Policies302Securing Infrastructure and Data305Network Isolation with VPC305Private Connectivity306Data Protection306Monitoring and Auditing307Ensuring Compliance307Summary308Exam Essentials309Review Questions311