Tech Career Library in the Age of AI by Apress

Create simple animations that can be used on websites or in presentations using Adobe Animate. If you are a graphic artist, and not a professional animator, the idea of creating animations may sound appealing; you may feel overwhelmed as to where to start or how you can use your past knowledge of other Adobe design applications, such as Photoshop or Illustrator, to assist you in completing your goals. In this book we will start by looking at an overview of Animate. You will create and review some projects and use various assets that you created or are created by Adobe to make some basic animation sequences that you can use for your next project. We will also look at how we can edit movements, which may include the representation of speed, rotation, human walking, and animal movement as well as speech, to make the action in the scene appear more fluid. You will also see how to incorporate sound/audio. Finally, you will publish the completed animation in a variety of formats. Readers will appreciate how they can quickly turn their imported Illustrated vector drawings or background graphics into a short animation that they can use in their next project. You Will Discover which applications to use for creating your assets Find out how to use basic and some advanced tools in Animate Learn to publish your animations in the correct format for your own projects Who This Book Is For Professionals who would like to start using this application for their next project but need some guidance on how to use the basic tools would find the book valuable.

Unlock the future of cloud data engineering with generative and agentic AI on AWS. This hands-on guide shows you how to build intelligent, responsive data platforms using cutting-edge AI capabilities and modern AWS services. Learn to design next-generation data architectures—from data lakes and data mesh to scalable pipelines and real-time analytics. Discover how generative AI and agentic automation are transforming every aspect of enterprise data work: ingesting unstructured data, enabling semantic search with Retrieval-Augmented Generation (RAG), building autonomous data agents, and using natural language interfaces to turn business questions into instant insights. Author Justin J. Leto, PE, MBA, PMP, is a Principal Solutions Architect at AWS with over 20 years of experience in data engineering and AI. He doesn't just teach today's techniques—he prepares you for the future disruptions reshaping the field. His book is essential reading for current and aspiring data engineers, data analysts, data architects, engineering managers, CTOs, CDOs, and data-focused entrepreneurs looking to gain an edge over the competition. What You Will Learn: Master the core principles and practices of data engineering to build a long, successful career in the field. Accelerate your impact using AWS cloud services for scalable, modern data solutions. Explore how the role of the modern data engineer is evolving to support generative and agentic AI use cases. Develop a modern data strategy by working backwards from business goals to gain buy-in from CxO-level leadership. Design and deploy modern data architectures—including data lakes, data mesh, and data marts—and understand when to use each. Apply generative and agentic AI to enhance every stage of the data engineering lifecycle. Evaluate emerging data and AI technologies using proven methodology to separate real value from hype. Prepare for the future of data engineering powered by autonomous agents that scale enterprise impact. Who this Book Is For: Data engineers, analysts, architects, and tech leaders seeking practical guidance on AWS data engineering and generative AI, with or without prior cloud experience.

This book is a comprehensive guide for business leaders, CXOs, and technical architects looking to adopt Generative AI technologies to create innovative applications for their organizations and clients. The book is divided into three parts. In Part 1, readers will learn the fundamental concepts of Generative AI, such as foundational models and large language models like ChatGPT. It provides an overview of various Generative AI techniques, including Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). This section helps leaders understand the benefits and risks associated with Generative AI, supported by industry case studies showcasing real-world applications. Part 2 focuses on developing a winning strategy for implementing Generative AI. It discusses choosing the right approach, building the necessary architecture and infrastructure, and ensuring data quality for training models. It also addresses technical challenges like computing power and offers best practices for execution. In Part 3, the book explores best practices for adopting Generative AI, emphasizing governance, talent development, and continuous improvement. It also discusses emerging trends and the societal impact of Generative AI, particularly concerning sustainability. By the end, readers will be equipped with the knowledge and strategies to effectively harness Generative AI, paving the way for innovation in their organizations. What you will learn: What are the various Generative AI technologies – Foundational models, LLM, Auto encoders, GANs, VAEs, RAG, prompt engineering and more. How to build a successful strategy for architecting and implementing Generative AI Understand Preprocessing and setting up quality data for training generative AI models What are the future trends in generative AI to build sustainable and trustworthy solutions Who this book is for: Individuals in leadership positions, such as CEOs, CTOs, CIOs, and other senior executives, who are responsible for driving innovation and strategy. decision-making in their organizations and interested in adopting Gen AI for that purpose. Professionals who design and implement technical solutions and infrastructure using Generative AI and machine learning systems.

Explore modern software engineering from design to deployment and everything in-between. This field guide is your ultimate reference to designing, building and deploying modern software applications in the tech industry today. Split into four parts, the book takes you through the stages of the software lifecycle, introducing you to aspects such as the command line interface, containerisation, design and practices before taking you through the major components of software system design, patterns, tools and best practices, before embarking on software deployment and all of the major areas of testing and security. You will also look at how the role of AI influences and impacts our approach to software engineering during different stages of development. The Developer's Field Guide to Modern Software Engineering is made up of 17, self-contained chapters for easy reference for those just starting their developer careers as well as those more experienced, who are looking for a quick refresher on a particular topic or issue. What You Will Learn Review the major components of modern software engineering !--[endif]--Explore major topics in a real-life context Troubleshoot potentional problems See how AI is impacting software at different stages of the lifecycle Who This Book Is For Developers, engineers, and product managers who are involved in the design and deployment of software applications. Also, for those just starting out, looking for a starting point, as well as those more experienced looking for a quick and easy refresher on a particular topic.

Build a sustainable, future-proof career in Quality Assurance. Focusing on both broader strategies for building a long-lasting and fruitful career, as well as tactical interview preparation, this book is a comprehensive roadmap to developing the technical, functional, and soft skills that will help you stand out in the job market. The book takes a two-pronged approach to building your career in quality assurance and testing. It first helps you develop the foundational functional skills that large tech companies look for, and then guides you through step-by-step strategies to ace QA interviews. Combining real-world insights, structured skill-building, and a strategic approach to career growth, this ultimate guide for QA professionals will help you gain expertise in test planning, test automation, AI in testing, metrics, and quality reporting, making you a competitive candidate. The book begins with an introduction to the QA profession, followed by a clear plan on which skills to develop and how to develop them. It then goes into detail on how to tailor your skills to specific industries and position yourself as a highly relevant expert. The second part of the book delves deeper into the interview process itself, offering crucial tips on how to showcase your competencies and deal with challenges, such as areas where you may have less experience. It also provides valuable advice on what to do after the interview, regardless of whether you got the job or not. Using practical QA methodologies combined with career guidance and interview strategies, this book offers readers a holistic view, from mastering technical skills to navigating career advancements and succeeding in interviews. What You Will Learn Develop critical abilities like process thinking, structured communication, leadership, and assertiveness Learn how to handle both technical and behavioral interview questions with confidence Gain deep expertise in test planning, test automation, AI in testing, metrics, and quality reporting Master the art of handling setbacks, learning from interview experiences, and position yourself for future success Who This Book Is For QA Engineers, Software Testers, Test Automation Engineers, QA Leads and QA Managers

Generative AI and Agentic AI together are revolutionizing the technology landscape, with profound and far-reaching impacts across industries. Organizations are increasingly adopting these technologies to drive innovation, enhance unstructured content management, and improve problem-solving capabilities. With Agentic AI, enterprises are moving towards the development of intelligent systems that can plan, reason, and act with autonomy. While early proof-of-concepts (POCs) demonstrated the potential of these technologies, the current shift is toward responsible and scalable production implementations that leverage both generative and agentic capabilities. This book begins by guiding you through the technological evolution of AI, from early machine learning to today’s large language models (LLMs) and agentic systems. It then explores a wide range of use cases across industries, highlighting how LLMs can support decision-making, and how Agentic AI enables dynamic, collaborative systems that act with autonomy and intent. This is followed by Design Patterns across the lifecycle of AI solution development, deployment and monitoring. Readers will then gain insights into the methodologies for developing and deploying Generative and Agentic AI solutions at an enterprise level. A featured implementation demonstrates how Agentic AI can be effectively put into action. The book also introduces essential concepts such as MLOps, LLMOps, and Responsible AI principles which are critical for transitioning the AI solutions from experimentation to production. These principles ensure that AI deployments are scalable, secure, ethical and compliant. The book concludes with key takeaways and best practices for developing, evaluating, deploying and scaling AI applications responsibly and effectively within enterprise settings. You Will: Understand key design patterns to develop, deploy and monitor a Generative AI solution effectively. Learn how to develop and implement a production-ready Agentic AI use case. Discover best practices for building scalable, secure, and enterprise-grade AI solutions. Understand how to assess and mitigate risks using Responsible AI principles and LLMOps best practices. This book is for : Enterprise Software Engineers and Architects

This book is the first practical book on AI risk assessment and management. It will enable you to evaluate and implement safe and accurate AI models and applications. The book features risk assessment frameworks, statistical metrics and code, a risk taxonomy curated from real-world case studies, and insights into AI regulation and policy, and is an essential tool for AI governance teams, AI auditors, AI ethicists, machine learning (ML) practitioners, Responsible AI practitioners, and computer science and data science students building safe and trustworthy AI systems across businesses, organizations, and universities. The centerpiece of this book is a risk management and assessment framework titled “Safe Human-centered AI (SAFE-HAI),” which highlights AI risks across the following Responsible AI principles: accuracy, sustainability and robustness, explainability, transparency and accountability, fairness, privacy and human rights, human-centered AI, and AI governance. Using several statistical metrics such as Area Under Curve (AUC), Rank Graduation Accuracy, and Shapley values, you will learn to apply Lorenz curves to measure risk and inequality across the different principles and will be equipped with a taxonomy/scoring rubric to identify and mitigate identified risks. This book is a true practical guide and covers a real-world case study using the proposed SAFE-HAI framework. The book will help you adopt standards and voluntary codes of conduct in compliance with AI risk and safety policies and regulations, including those from the NIST (National Institute of Standards and Technology) and EU AI Act (European Commission). What You Will Learn Know the key principles behind Responsible AI and associated risks Become familiar with risk assessment frameworks, statistical metrics, and mitigation measures for identified risks Be aware of the fundamentals of AI regulations and policies and how to adopt them Understand AI governance basics and implementation guidelines Who This Book Is For AI governance teams, AI auditors, AI ethicists, machine learning (ML) practitioners, Responsible AI practitioners, and computer science and data science students building safe and trustworthy AI systems across businesses, organizations, and universities

Gain a clear and practical understanding of ChatGPT, the cutting-edge language model that is revolutionizing how we create content, analyse data, and communicate. This book will be your essential companion for navigating the rapidly-evolving world of generative AI. You’ll explore an army of diverse features boasted by ChatGPT, including web searching, analysing images, PDFs and other file types, image creation with ChatGPT’s DALL-E, and video generation with ChatGPT’s Sora. Highlighted by over 100 prompts, this book boasts step-by-step guides on how to use ChatGPT for a variety of tasks such as thesis writing, creating a sales report, and even creating a budget planner from actual receipts. Each chapter has a series of practical examples you can follow along with so you can try out the explanations for yourself. You’ll also learn how to use ChatGPT as a personal assistant, yet another tool to streamline and simplify productivity. With these powerful capabilities in hand, Making ChatGPT Work for You is your prompt cookbook! What You Will Learn Generate unique and engaging text for social media posts, blogs articles, and creative writing. Upload files and images to ChatGPT to analyse data, explain text, and analyse photos. Leverage ChatGPT to interpret and analyse datasets, provide insights and solutions, create budget plans, sales reports, and more. Use ChatGPT as a versatile educational tool for learning new skills. Work with ChatGPT’s DALL-E and Sora features to generate images and short videos for creating content for landing pages and YouTube videos. Who This Book Is For Professionals, students, educators, and content creators curious about AI and looking to use ChatGPT for practical applications.

AI-Driven Software Testing explores how Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing quality engineering (QE), making testing more intelligent, efficient, and adaptive. The book begins by examining the critical role of QE in modern software development and the paradigm shift introduced by AI/ML. It traces the evolution of software testing, from manual approaches to AI-powered automation, highlighting key innovations that enhance accuracy, speed, and scalability. Readers will gain a deep understanding of quality engineering in the age of AI, comparing traditional and AI-driven testing methodologies to uncover their advantages and challenges. Moving into practical applications, the book delves into AI-enhanced test planning, execution, and defect management. It explores AI-driven test case development, intelligent test environments, and real-time monitoring techniques that streamline the testing lifecycle. Additionally, it covers AI’s impact on continuous integration and delivery (CI/CD), predictive analytics for failure prevention, and strategies for scaling AI-driven testing across cloud platforms. Finally, it looks ahead to the future of AI in software testing, discussing emerging trends, ethical considerations, and the evolving role of QE professionals in an AI-first world. With real-world case studies and actionable insights, AI-Driven Software Testing is an essential guide for QE engineers, developers, and tech leaders looking to harness AI for smarter, faster, and more reliable software testing. What you will learn: • What are the key principles of AI/ML-driven quality engineering • What is intelligent test case generation and adaptive test automation • Explore predictive analytics for defect prevention and risk assessment • Understand integration of AI/ML tools in CI/CD pipelines Who this book is for: Quality Engineers looking to enhance software testing with AI-driven techniques. Data Scientists exploring AI applications in software quality assurance and engineering. Software Developers – Engineers seeking to integrate AI/ML into testing and automation workflows.

“An outstanding companion for any data professional seeking to deepen their expertise and grow in the field of Data Product Management.” (Eduardo Juremeira, Data Engineering Manager, Adyen) “This book is a valuable resource for data professionals, offering clear concepts and practical tools.” (Luis Oliveira, Analytics Engineering Tech Manager, Nubank) A practical guide to mastering data product management—bridging strategy, architecture, and execution for the AI era. We have firmly entered the age of artificial intelligence (AI). Data, the backbone of this technology, is more crucial than ever. If the algorithm is the brain, data is the content that feeds it. This book argues that to succeed in this era, organizations should adopt a holistic approach to data—one that uses product management principles to inform how data is sourced, designed, managed, maintained, optimized, and leveraged. This book is divided into two sections, moving from fundamentals to practical applications. In the first part, you’ll learn about the concepts that underlie product theory, the data architecture journey, and the essential knowledge needed to manage data products. The second part focuses on putting everything into practice, with particular attention to designing solutions, ongoing maintenance, and optimization. Additionally, the book introduces the Golden Data Platform and the Data Product Management Canvas, important tools and frameworks coined by author Jessika Milhomem. These resources will help you begin transforming your organization’s data strategy, empowering you to stay ahead of the competition and thrive in the AI age. What You Will Learn !--[endif]--Explore the evolution of data architecture and strategies !--[endif]--Understand the fundamentals of data product management !--[endif]--Differentiate between Data Product and Data as a Product !--[endif]--Know what the Golden Data Platform is and how to use it !--[endif]--Utilize the Data Product Management Canvas effectively !--[endif]--Reorient your data strategy with product management principles !--[endif]--Examine the concepts of products and data architecture evolution in relation to leadership !--[endif]--Evaluate how to solve data architecture without bias by technology !--[endif]--Design and implement Data as a Product and Data Products through a project !--[endif]--Maintain and enhance Data Products once launched Who This Book is for Data leaders and managers responsible for designing and delivering data products, as well as product managers who want to collaborate more effectively with data teams. Analytics engineers, data engineers, data scientists, and machine learning engineers will find practical guidance for building impactful data solutions. Developers and data professionals aiming to move into leadership or product roles will also find valuable insights throughout.

In the era of Generative AI, creating immersive and adaptive experiences in game development has become more innovative and practical than ever before. This book looks to deepen your expertise in using tools such as GPT, DALL-E, and VADER to design dynamic narratives, procedurally generated content, and create AI-powered gameplay. Divided into six parts, you will start from building foundational concepts to implementing advanced projects. Early chapters establish a shared understanding of narrative structures, interactive storytelling types, and tools like Twine, Flask and Python. It then progresses to designing branching story frameworks, crafting emotionally adaptive narratives, and creating AI-driven characters with dynamic dialogue and behavioral depth. Further chapters focus on procedural generation for world building, showing how to leverage AI for dynamic environments, levels, and storytelling events. A dedicated section dives into C# and Unity, providing hands-on projects such as integrating AI for real-time narrative adaptation, dynamic NPC behavior, and procedural world generation in Unity. These patterns are also revisited in non-game contexts to highlight their broader applicability. This book equips you with knowledge to master Generative AI tools and techniques for creating sophisticated, interactive games. Whether you're looking to enhance your current workflows or explore new creative frontiers, this book guides you to building engaging, adaptive, and innovative experiences that blend AI technology with human creativity. What You Will Learn • Master designing dynamic narratives and characters with Generative AI • Learn to integrate AI into game development with C# and Unity • Find out how to leverage AI for procedural world building and interactive experiences Who Is This Book For This book is designed for intermediate-level developers, writers, and game designers who already have foundational knowledge in storytelling, programming, and/or game design and are looking to expand their skills with Generative AI technologies.

The world stands at a pivotal moment due to the emergence of Generative AI, specifically ChatGPT. This groundbreaking technology has provoked and impressed almost every industry globally, evoking every emotion from awe to anxiety. Many are apprehensive about the future, fearing job losses due to rapid artificial intelligence (AI advancements). But if history has taught us anything, progress, while challenging, often paves the way for broader opportunities and growth. This book explains in depth the core building blocks that make up the current landscape of transformer and language models and, more broadly speaking, AI as a whole. We have seen how the internet and the mobile revolution changed our world. Is AI following a similar trajectory? Are we on the verge of something even more transformative? This book strives to provide a complete picture of the challenges and opportunities and the implications for our shared futures. In subsequent chapters, the book will discuss language models in depth. These are not just algorithms; they represent a nexus of linguistics, cognitive science, and cutting-edge technology. You’ll trace AI’s unexpected and exhilarating evolution, observing how it has grown from a mere concept to a force reshaping entire industries. Finally, you’ll consider the rise of AI in the context of advancements. While ChatGPT has gained significant attention for certain applications, it’s essential to recognize that its capabilities extend far beyond what’s immediately evident. Artificial Intelligence, represented by models like ChatGPT, is not a static field. It’s dynamic and ever-evolving, and its potential applications are broadening each day. Technology is not, by any means, limited to chatbots or translation use cases. This book captures this vast and ever-expanding horizon of possibilities. What You Will Learn How large language models came to be, and how they work What ethical AI design looks like The role of regulation in artificial intelligence Why you should not be afraid of losing your job Who This Book is For Working professionals and students in any field

Harness the full potential of Intelligent Automation (IA) to transform your businesses, stay competitive, and navigate the complexities of today’s digital landscape. This book is a comprehensive guide for business leaders, executives, and automation professionals, exploring how Intelligent Automation (IA) has evolved from traditional Robotic Process Automation (RPA) to become a powerful catalyst in Digital Transformation. Begin with the fundamentals of IA and its progression from simple, rule-based RPA to advanced IA, incorporating AI-driven technologies like Machine Learning, Generative AI, and Agentic AI. These emerging technologies empower organizations to tackle more complex processes, enabling intelligent decision-making and enhanced customer interactions prompting the necessity for robust governance and alignment with broader business objectives, which is a primary focus of this book. To combat that, you’ll review actionable frameworks and governance models designed to support the development and sustainability of IA programs at scale. The book provides a structured roadmap to establish an IA Center of Excellence (CoE), ensuring that automation efforts align with the organization’s Digital Transformation goals. By integrating governance processes, risk management, and best practices, you’ll be able to build IA initiatives that are resilient, compliant, and strategically impactful. Intelligent Automation for Digital Transformation demystifies the journey to becoming a digitally native organization. Who This Book is For Leaders responsible for shaping their organization’s long-term strategy, digital innovation, and business process efficiency agenda. Executives in the financial services, healthcare, and other highly regulated industries where a risk management mindset and proper governance framework is key. Consultants and advisors within the business transformation and automation space who are ena-bling companies in their journey to become digitally native.

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 science Who This Book is for A 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.

In a world where AI is rapidly becoming embedded in everyday tools, this book helps you cut through the noise to understand Microsoft’s growing Copilot ecosystem and confidently choose the right tool for the right task. You’ll start by untangling the different Copilot experiences in Microsoft 365. From there, you’ll explore agents, learning why they matter and how they enable powerful automation without requiring deep coding skills. Along the way, you’ll get plain-language explanations of the buzzwords that make this space feel overwhelming and a realistic view of what AI can and can’t do. By the end of the book, you won’t just know the difference between a bot, a workflow, and an agent; you’ll know exactly when each is the right choice and how to apply them with confidence. What You will Learn: • Choose the right Copilot or agent for specific business scenarios • Understand how large language models work behind the scenes • Interpret common AI terminology without the jargon • Distinguish between bots, workflows, and agents and when to use each • Identify strong AI use cases and avoid common pitfalls Who This Book Is For Business users, developers and Power Platform makers

Turn raw data into real traction—and real revenue. This practical hands-on guide is built for the realities of startup life—where time is short, resources are limited, and every decision counts. Whether you're just getting started or scaling fast, this book is your essential playbook for building a data-driven startup ready for growth and innovation—and for turning insight into impact and data into dollars. Drawing from his hard-won lessons as the first data hire in both venture-backed successes and failures, author Piotr Sidoruk shares battle-tested strategies for building data systems that are both affordable and scalable, agile and reliable. You’ll explore how to create a strong data foundation free from the rigidity of traditional corporate models, and how to use analytics and AI to inform product development, customer insights, and investor communication—ultimately turning data into measurable business outcomes and revenue growth. What makes this book different? It unites data engineering, analytics, and machine learning into one actionable framework tailored for startups. With real-world examples from companies like Spotify, Airbnb, and Stripe, you’ll see how data can become a strategic asset from day one. From your first data hire to your next funding round, this book is your roadmap to making data your competitive advantage. What You Will Learn Develop a lean data-driven strategy to fuel startup growth Build a scalable data stack for AI and machine learning without overspending Discover user insights with analytics and qualitative research Turn data into financial value and build narratives that win investors Track key startup metrics including CAC, LTV, retention, and growth KPIs Build dashboards and reports that drive decisions and revenue Who This Book Is For Data engineers, analysts, and scientists who are the first data hires at startups—or preparing to work in fast-paced, high-growth startup environments. Ideal for professionals looking to build scalable data systems, drive product insights, and grow their careers in early-stage tech companies.

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 For Intermediate 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.

This book explores how artificial intelligence (AI) is transforming the design and operation of microservices and API architecture. It provides a clear and practical guide to using AI to automate tasks, enhance performance, and improve the scalability of microservice-based systems. Starting with the basics, you will learn about the core concepts of microservices and API design, gradually building an understanding of how AI can be seamlessly integrated. Through real-world examples, visual diagrams, and mock APIs, the book shows you how to bring theory into practice, making complex systems easier to manage and more efficient. You will also discover strategies for testing and scaling systems, securing APIs, and addressing ethical challenges in AI-powered environments. Case studies highlight successful implementations, offering valuable insights you can apply to your own projects. Whether you're a developer, architect, or tech enthusiast, this book gives you the tools and inspiration to build smarter, more resilient systems while staying ahead of future trends in AI and distributed computing. What You'll Learn: Understand the basics of microservices and API design and see how AI can make these systems smarter and more efficient. Discover how to use AI in microservices and APIs to automate tasks, improve performance, and boost security. Learn how to design scalable and secure systems by following best practices and innovative approaches. Get practical tips on troubleshooting and solving challenges in AI-powered microservice architectures. Who is this book for: Software architects and engineers, AI and machine learning professionals, and DevOps engineers

Unlock the power of Claude, an advanced conversational AI assistant, and integrate it into your daily professional workflows and decision-making. This book offers a clear, relatable path, regardless of your technical background, from first prompt to advanced user, through a guided, real-life learning approach that explains the AI learning curve. You’ll start with an overview of Claude’s capabilities. In Part I, Claude Fundamentals introduces you to Claude AI in a clear, beginner-friendly way, using relatable comparisons and real examples. It guides you through initial setup, early conversations, and key lessons learned from trial and error. The section also explores prompt design, showing how skills evolve from basic to advanced, with practical before-and-after insights. Part II dives into hands-on applications—writing, research, coding, creativity, and data analysis—demonstrating how Claude can support a wide range of professional and personal use cases. Part III then introduces advanced strategies like prompt chaining and feature optimization, while Part IV explores professional domains including business, education, and artistic collaboration. In Part V, you’ll gain insights into troubleshooting, responsible AI use, and keeping up with rapid AI advancements. Finally, Part VI synthesizes the full journey, offering guidance on becoming a true power user and shaping the future of human-AI collaboration. Through transparent storytelling, tested frameworks, and actionable strategies, this practical guide will empower you to turn Claude AI from a tool into a transformative partner. What You Will Learn Use Claude AI effectively, even with no technical background or coding experience Master prompting techniques that evolve from simple queries to advanced, optimized conversations Apply Claude in writing, research, coding, creativity, and data analysis with real-life examples Explore Claude’s advanced features and integrate them into daily professional workflows and decision-making Who This Book Is For Creative professionals, educators, and business leaders exploring practical AI integration. It’s also ideal for entrepreneurs seeking new opportunities and knowledge workers aiming to boost productivity.

As enterprises around the globe rely increasingly on Linux servers, understanding the basics of managing these systems effectively is more relevant than ever. This book provides a thorough introduction to Linux system administration, exploring the foundational terms and concepts needed for modern IT system administrators. You’ll start with the fundamentals of Linux and its ecosystem, looking at Linux distributions, tools, and concepts before moving on to user accounts, filesystem management, and configuration. Once you have aced these rudimentary elements, you’ll explore Shell scripting and learn how to write and execute simple code scripts. The book then, explores concepts like system monitoring and performance, common Linux testing, security tools, and best practices. You’ll finish by looking at networking and remote access, configuration and VPNs, as well as disaster recovery, including backup strategies, automation, and executing data recovery. Beginning Linux SysAdmin is your pathway to everything from initial system setup and configuration to basic security and troubleshooting techniques. What You Will Learn Install and configure a Linux system Manage users, groups, and permissions effectively Monitor, secure, and maintain system performance Troubleshoot basic issues in Linux environments Automate routine tasks using shell scripting Who This Book Is For IT professionals, system administrators, and developers who want to build a strong foundation in Linux administration. It is also suitable for students and hobbyists looking to enhance their skills in managing Linux systems

“Navigating Your Career in ServiceNow: From First Steps to Strategic Growth” offers a clear and actionable roadmap for anyone looking to enter or advance in one of today’s fastest-growing enterprise platforms. Whether you're just starting out or transitioning from another field, this book helps you understand the ServiceNow platform, choose your career path, and become job-ready with the right skills, certifications, and learning strategies. It goes beyond technical preparation to help you stand out in the job market. You’ll also discover how to grow beyond your first role, build a versatile skill stack, and stay relevant in a constantly evolving tech landscape. With insights on freelancing, personal branding, and long-term career planning, this book is a practical companion for every stage of your ServiceNow journey-from your first login to your future as a trusted expert in the field. You Will: Obtain a comprehensive overview of the ServiceNow ecosystem and the wide range of career opportunities it offers. Receive actionable guidance for navigating the emotional and professional challenges of career growth-including overcoming imposter syndrome, managing burnout, and pushing through plateaus. Attain guidance on handling real-world career challenges like imposter syndrome, burnout, and plateaus. This book is for: Professionals and individuals who are new to ServiceNow, seeking structured guidance on entering the field.

In 25 concise steps, you will learn the basics of blockchain technology. No mathematical formulas, program code, or computer science jargon are used. No previous knowledge in computer science, mathematics, programming, or cryptography is required. Terminology is explained through pictures, analogies, and metaphors. This book bridges the gap that exists between purely technical books about the blockchain and purely business-focused books. It does so by explaining both the technical concepts that make up the blockchain and their role in business-relevant applications. What You'll Learn What the blockchain is Why it is needed and what problem it solves Why there is so much excitement about the blockchain and its potential Major components and their purpose How various components of the blockchain work and interact Limitations, why they exist, and what has been done to overcome them Major application scenarios Who This Book Is For Everyone who wants to get a general idea of what blockchain technology is, how it works, and how it will potentially change the financial system as we know it

In an era defined by cloud computing, AI disruption, and evolving cyber threats, traditional security models are no longer enough. Foundations of Modern Information Security delivers a vendor-neutral, real-world guide to designing and maintaining secure systems that stand up to today’s—and tomorrow’s—challenges. This book bridges the gap between legacy infosec thinking and modern enterprise realities. Covering everything from Zero Trust Architecture and post-quantum cryptography to AI governance and secure cloud design, it equips professionals with the tools to build resilient, adaptable security programs. The methodology is process-centric and rooted in practical implementation, not product marketing. Structured around trusted industry frameworks like NIST, MITRE ATT&CK, and CSA, this guide helps readers align security strategy with compliance, business goals, and operational needs. Whether you're protecting identity, preventing data loss, or planning incident response, you’ll find actionable insights and examples that translate directly to your environment. Whether you're an experienced security architect or a technology leader seeking clarity in a complex landscape, this book offers the foundational knowledge and strategic guidance needed to elevate your security posture—without vendor bias or unnecessary complexity. YOU WILL · Design and implement vendor-neutral Zero Trust Architectures · Build resilient, privacy-respecting identity and data protection programs · Navigate AI-driven threats with quantum-resistant and governance-aware controls · Apply MITRE, NIST, and TOGAF frameworks to real-world cloud and hybrid environments · Establish forensic readiness and proactive incident response capabilities WHO THIS BOOK IS FOR This book is intended for cybersecurity professionals, IT architects, and technical leaders seeking a vendor-neutral, strategic understanding of modern information security. It’s ideal for mid-to-senior level professionals preparing for leadership roles or certifications like CISSP, CCSP, or CISM.

Master practical, code-driven techniques to safeguard gameplay integrity—without relying on online infrastructure or third-party anti-cheat tools. In a world where even offline games are vulnerable to tampering and exploits, this book is a hands-on guide to defending your Unreal Engine projects from cheats, hacks, and manipulation. Through real-world examples and game-ready implementations, you’ll learn how to encrypt sensitive variables, detect runtime tampering, secure saved files, and prevent rollback exploits. Each chapter walks you through structured solutions, including honeypot traps, debugger detection, and DLL injection prevention—backed by insights from titles like The Witcher 3 and Assassin’s Creed Odyssey. You will learn to build a tamper-proof health system, lock down stat-based mechanics, protect player progress to ensure a fair experience for every user. Learn To: Encrypt and validate in-game variables at runtime Secure saved files against edits and rollbacks Detect memory editing and cheat tools Implement honeypots and behavior-based cheat detection Apply anti-tampering strategies without online systems Who Is This Book For With a focus on Blueprints and C++, this book is tailored for beginner-to-intermediate level Unreal Engine developers who want to build secure single-player games that earn long-term player trust.