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Paperback. Condition: new. Paperback. Modern software is no longer just programmed, it is orchestrated.As large language models evolve from simple text generators into reasoning engines, a new engineering discipline has emerged: Intelligent Automation with Large Language Models. This book is your practical, end-to-end guide to designing automation systems that can think, plan, and act autonomously across real-world environments.Intelligent Automation with Large Language Models shows you how to move beyond isolated prompts and experiments to build AI agents, workflow orchestration pipelines, and autonomous systems that integrate seamlessly with APIs, business tools, and production infrastructure. You'll learn how to engineer intelligent workflows where LLMs don't just respond, they coordinate, reason, and make decisions within structured systems.Bridging theory and hands-on implementation, this book walks you through the core building blocks of modern AI automation: agent design, context management, workflow orchestration, memory systems, and multi-step decision pipelines. Each concept is grounded in practical architectures that can be adapted to real applications, from document processing and research automation to business operations and internal tooling.Rather than focusing on hype or fragile one-off solutions, this guide emphasizes engineering discipline: scalability, reliability, cost control, monitoring, and responsible deployment. You'll learn how to design automation systems that are not only powerful, but maintainable, observable, and production-ready.What You'll LearnCore principles of intelligent automation and LLM-driven system designHow to build and coordinate AI agents for multi-step tasksDesigning workflow orchestration pipelines that connect LLMs, APIs, and toolsContext and memory strategies for long-running and stateful AI systemsPatterns for autonomous decision-making and agent collaborationBest practices for scaling, monitoring, and governing AI automation in productionReal-world architectures for business, research, and operational automationWho This Book Is ForThis book is written for developers, engineers, data professionals, and technical builders who want to move beyond experimentation and start delivering real value with AI automation. Whether you're integrating LLMs into existing systems, designing agentic workflows, or building automation platforms from the ground up, this guide provides reproducible frameworks you can apply immediately.No hype. No shortcuts. Just clear, practical engineering for the next generation of intelligent systems. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Paperback. Condition: new. Paperback. Modern software is no longer just programmed, it is orchestrated.As large language models evolve from simple text generators into reasoning engines, a new engineering discipline has emerged: Intelligent Automation with Large Language Models. This book is your practical, end-to-end guide to designing automation systems that can think, plan, and act autonomously across real-world environments.Intelligent Automation with Large Language Models shows you how to move beyond isolated prompts and experiments to build AI agents, workflow orchestration pipelines, and autonomous systems that integrate seamlessly with APIs, business tools, and production infrastructure. You'll learn how to engineer intelligent workflows where LLMs don't just respond, they coordinate, reason, and make decisions within structured systems.Bridging theory and hands-on implementation, this book walks you through the core building blocks of modern AI automation: agent design, context management, workflow orchestration, memory systems, and multi-step decision pipelines. Each concept is grounded in practical architectures that can be adapted to real applications, from document processing and research automation to business operations and internal tooling.Rather than focusing on hype or fragile one-off solutions, this guide emphasizes engineering discipline: scalability, reliability, cost control, monitoring, and responsible deployment. You'll learn how to design automation systems that are not only powerful, but maintainable, observable, and production-ready.What You'll LearnCore principles of intelligent automation and LLM-driven system designHow to build and coordinate AI agents for multi-step tasksDesigning workflow orchestration pipelines that connect LLMs, APIs, and toolsContext and memory strategies for long-running and stateful AI systemsPatterns for autonomous decision-making and agent collaborationBest practices for scaling, monitoring, and governing AI automation in productionReal-world architectures for business, research, and operational automationWho This Book Is ForThis book is written for developers, engineers, data professionals, and technical builders who want to move beyond experimentation and start delivering real value with AI automation. Whether you're integrating LLMs into existing systems, designing agentic workflows, or building automation platforms from the ground up, this guide provides reproducible frameworks you can apply immediately.No hype. No shortcuts. Just clear, practical engineering for the next generation of intelligent systems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Condition: new. Paperback. Building software that runs reliably across Windows, Linux, and macOS is one of the most demanding challenges in modern systems engineering. Each platform has its own APIs, toolchains, build systems, runtime behaviors, and subtle edge cases that can quietly break portability if not addressed deliberately. Cross-Platform Software Engineering with C++ is a comprehensive, architecture-first guide to solving these challenges the right way, without sacrificing performance, native integration, or long-term maintainability.Written for professional developers and advanced learners, this book focuses on the engineering principles, architectural decisions, and practical techniques required to design and build truly portable C++ applications. Rather than relying on fragile abstractions or "write once, debug everywhere" approaches, you'll learn how to create systems that respect platform differences while maintaining a clean, unified codebase.The book takes you through the full lifecycle of cross-platform development-from early planning and architectural design to implementation, testing, deployment, and long-term maintenance. You'll explore how to structure your code for portability, when to leverage native APIs, and how to introduce abstraction layers that reduce coupling without hiding critical platform behavior. Special attention is given to performance, correctness, and reliability, ensuring that applications behave consistently across operating systems while still feeling native to each environment.Using real-world scenarios and practical examples, you'll gain deep insight into the most common portability pitfalls in C++, including data types, memory layout, floating-point behavior, file systems, threading models, and build configurations. The book also covers modern build systems, compiler toolchains, and testing strategies that enable teams to detect cross-platform issues early-before they become expensive to fix.Beyond code, Cross-Platform Software Engineering with C++ addresses the process and discipline behind successful portable software. You'll learn how to establish cross-platform workflows, manage platform-specific defects, and design systems that can evolve as operating systems, compilers, and hardware architectures change over time.By the end of this book, you will be able to: Design C++ applications with portability as a first-class architectural concernBuild high-performance native software that runs consistently across desktop platformsBalance platform abstraction with direct access to native APIs when necessaryIdentify and eliminate hidden portability bugs before they reach productionCreate scalable build, testing, and deployment pipelines for multi-platform projectsEngineer software systems that remain maintainable as platforms and requirements evolveThis book is ideal for software engineers, systems programmers, technical leads, and architects who already have working knowledge of C++ and want to elevate their skills to a professional, cross-platform level. Whether you're building new applications, porting existing systems, or future-proofing your software for additional platforms, this guide provides the technical depth and practical insight needed to engineer portable C++ software with confidence. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Condition: new. Paperback. In an increasingly connected world, understanding relationships within data has become essential for building intelligent applications and solving complex problems. From recommendation systems and fraud detection to knowledge graphs and operational analytics, modern software relies on efficiently modeling and querying connected data. Graph databases are purpose-built for this challenge, and Neo4j stands as the leading platform for managing relationships at scale.This book provides a practical and structured guide to working with Neo4j, combining foundational graph concepts with real-world engineering techniques. It equips readers with the knowledge needed to design, query, and analyze graph data effectively, while avoiding common pitfalls encountered in production systems.What You'll LearnThis book offers a clear roadmap for mastering Neo4j, blending theory with hands-on application: Understanding Graph DatabasesLearn why graph databases excel at modeling connected data, how they differ from traditional databases, and when Neo4j is the right choice.Cypher Query LanguageMaster Cypher to query, traverse, and manipulate graph data efficiently, with a focus on clarity, performance, and scalability.Data Modeling and OptimizationDiscover best practices for designing flexible and efficient graph models, including indexing, constraints, and performance considerations.Graph Analytics and AlgorithmsExplore Neo4j's graph analytics capabilities to uncover patterns, analyze networks, and extract meaningful insights from connected data.Building Intelligent ApplicationsLearn how Neo4j integrates into modern application architectures and supports graph-powered features through APIs and programming languages.Real-World Use CasesSee how graph databases are applied to practical scenarios such as fraud detection, recommendation systems, knowledge graphs, and supply chain analysis.Maintenance and TroubleshootingUnderstand how to monitor performance, troubleshoot queries, and maintain reliable graph database systems.Why Choose This Book?This guide is designed for professionals who want a practical understanding of Neo4j without unnecessary complexity. It explains not only how to use graph databases, but why graph-based approaches are effective for connected data problems. With a focus on real-world application, the book enables readers to apply concepts immediately and confidently.Who Is This Book For?This book is suitable for anyone working with connected data, including: Developers building graph-powered applicationsData professionals analyzing networks and relationshipsArchitects and technical leaders designing scalable data systemsBusiness and technology professionals seeking insight into graph-driven solutions This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Condition: new. Paperback. As PyQt5 applications grow, poor structure quickly leads to tangled logic, fragile code, and applications that are difficult to extend or maintain.Structured Applications with PyQt5 for Python GUI Engineering builds on the foundations established in Book 1 and focuses on the architecture and organization required for larger, long-lived desktop applications. This book is about moving from "working GUIs" to engineered software systems.Readers are guided through architectural patterns, modular design techniques, and application-level organization strategies that help manage complexity in real-world PyQt5 projects. Emphasis is placed on separating concerns, managing application state, organizing workflows, and designing systems that remain understandable as features grow.You will learn how to: Apply architectural patterns to PyQt5 desktop applicationsOrganize large GUI projects into clear, modular componentsManage application state and user-driven workflowsReduce coupling between UI, logic, and data layersDesign PyQt5 applications built for long-term maintenance and evolutionThis book is ideal for developers who already understand PyQt5 basics and want to build larger, more professional desktop applications with confidence.As Book 2 in the Python GUI Engineering series, this volume focuses on structure, architecture, and scalability bridging the gap between introductory GUI programming and production-ready desktop software. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.