Barrton vincent (69 resultados)

Condición: New.

Condición: As New. Unread book in perfect condition.

Condición: New.

Paperback. Condición: New.

Condición: As New. Unread book in perfect condition.

Condición: New.

Condición: New.

Condición: New.

Condición: New.

Condición: New.

Paperback. Condición: New.

Condición: New. In English.

Condición: New.

Condición: New.

Condición: As New. Unread book in perfect condition.

Condición: As New. Unread book in perfect condition.

Paperback. Condición: new. Paperback. As customer demands evolve, traditional support teams face mounting pressure to resolve inquiries quickly and accurately. Many organizations encounter significant obstacles managing the sheer volume and increasing complexity of customer requests. Relying on manual triage or simple rule-based systems often results in inconsistent answers, slow response times, and a backlog of unresolved tickets. The limitations of these methods become even more apparent as knowledge bases grow and customer expectations rise for real-time, precise answers across channels.A scalable customer support solution is now essential to uphold positive brand perception and maintain operational efficiency. AI agents, especially those leveraging Retrieval-Augmented Generation (RAG), offer a path toward delivering consistent, brand-aligned responses at speed and scale. Without robust automation, support teams risk not only reduced productivity, but the spread of misinformation or contradictory advice that can erode customer trust.Introducing AI agents into customer support, however, is not simply a plug-and-play exercise. Many teams hesitate due to the technical complexity and uncertainty around maintaining reliability, privacy, and compliance. This guide addresses those concerns directly, focusing on practical clarity for every phase of an AI agent project from initial planning and tool selection through deployment, risk management, and ongoing refinement. The emphasis remains tightly on customer support applications and methods that can be actioned without the need for deep AI research backgrounds or custom large language model (LLM) training.Efforts outside this scope, such as advanced model development or cross-industry deployments, are not addressed here. Instead, the guide walks sequentially through foundational concepts and actionable steps tailored for customer support scenarios. It begins by demystifying essential terms and RAG workflows, then outlines how to configure and implement a robust, modular architecture. Special attention is paid throughout to integration points ensuring that AI agents can connect and coexist with existing customer relationship management (CRM) and ticketing platforms.In this guide, you will learn: How to define and plan your RAG-powered customer support projectKey considerations for toolchain selection, including managed vs. open-source solutionsStep-by-step instructions for configuring and integrating AI agents with CRM and ticketing platformsBest practices for prompt engineering, data pipeline health checks, and escalation logicHow to operationalize guardrails for responsible AI agent behavior, including privacy, moderation, and escalation protocolsDeployment blueprints, monitoring templates, and troubleshooting frameworks to maintain high service quality and resolve issues quickly Practical tips to future-proof your support operations and ensure ongoing reliability Moving through the process, the guide provides a clear roadmap for prompt engineering and toolchain selection, highlighting decision points around managed versus open-source solutions and listing connectors for mainstream support platforms. Before reaching broader best practices and future-proofing guidance, the guide ensures all crucial elements prompt design, data pipeline health checks, and escalation logic are addressed in practical detail. This approach reduces risk and demystifies building and maintaining RAG-powered customer support agents, even for teams new to the field. By the end, a clear understanding emerges of how to move from legacy support methods to an adaptive, resilient AI-driven system that safeguards both customer experience and brand integrity. Transform Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condición: new. Paperback. Mastering data-intensive systems requires more than traditional programming skills or a focus on hardware. The velocity and variety of modern data create unique design demands that must be addressed from the ground up. This guide approaches these challenges by equipping you with the fundamental knowledge and practical tools needed to make sharp architectural decisions in contemporary, data-driven environments.The core of any data-intensive system is built upon a series of foundational choices. The approach you take to storage, streaming, and scalability shapes the flexibility, reliability, and future growth potential of your applications. These pillars aren't just technical categories; they represent the everyday decisions that determine whether systems will gracefully accommodate surging user demand or collapse under unanticipated stress.The modern landscape is defined by explosive data growth. Applications today generate, receive, and process data from an expanding array of sources, often in real time. Alongside the data deluge, reliability expectations have tightened. Users, teams, and regulators expect uninterrupted service and precise outcomes, regardless of scale. Systems that cannot handle data influx or recover quickly from errors risk downtime, data loss, or even organizational setbacks. This means system failures and capacity shortfalls are no longer minor inconveniences; they can represent significant business risk.This guide provides actionable visual tools and decision checklists aimed at identifying and addressing essential design points before performance bottlenecks and outages occur. Visual concept maps break down complex relationships among storage types and streaming mechanisms, giving clarity at every planning stage. Checklists flag critical requirements and edge conditions, helping catch potential flaws early. Failure pattern tables highlight common pitfalls, making diagnostic work more effective when things go wrong.This guide focuses intentionally on core architecture and design fundamentals. It does not cover deep algorithmic tutorials, industry-specific compliance standards, or application-layer user guidance. Instead, each chapter delivers clear, usable frameworks suitable across a broad range of domains and systems. By maintaining this scope, the guide ensures that every visual, checklist, and diagram remains general, practical, and widely applicable.In this guide, you will discover: Definitions and distinctions among functional storage categories, reviewing where block, file, and object storage each excel.Operating principles for streaming data flows, including real-time pipeline mechanics, buffering, and event management.Proven approaches to system scaling, with frameworks for vertical and horizontal growth and clear diagrams illustrating partitioning and replication.Visual concept maps that tie together architecture choices, making system relationships easy to understand.Decision checklists for evaluating durability, consistency, throughput, and scaling capacity at critical design milestones.Failure pattern tables and comparative diagrams to quickly identify and address common bottlenecks or design missteps.Diagnostic templates and reference charts for live evaluation and troubleshooting, so you can adapt strategies as data volumes increase.Explanations of common bottlenecks and clear guides to preventing the most frequent system slowdowns and breakdowns. By the end of this guide, you'll be able to navigate the essentials of data-intensive systems with clarity. The diagrams, frameworks, and diagnostic tools provided will serve as your roadmap through the complexities of modern storage, streaming, and scaling, empowering you to plan and a Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condición: new. Paperback. Sports betting can quickly feel overwhelming when every wager is made on instinct or emotion. Many bettors find themselves at the mercy of luck, placing scattered bets without a clear plan - resulting in erratic cash flow and a bankroll that vanishes before any real learning happens. At the core of most early mistakes is a lack of structure: no framework for how much to bet, when to stop, or how to monitor activity over time.Sports Betting Bankroll Blueprint is a practical, template-driven guide built around a 30-day unit-based staking system. Rather than offering gambling tips or picking strategies, it focuses entirely on the decision-making framework required for long-term sustainability. Every chapter delivers actionable checklists, calculation tools, and tracking templates that turn betting into a measured, managed process.The guide opens by helping readers establish a dedicated betting bankroll - isolated from personal finances - and size it appropriately using practical reference tables. From there, it introduces the unit system: a standardized method for allocating bet sizes that keeps every wager within a controlled range and prevents common errors like unit creep or variable staking.Chapters on risk rules define hard daily, weekly, and total exposure limits that never shift in the heat of the moment. Four weekly modules walk readers through the full 30-day cycle: initiating the system in Week 1, detecting patterns in Week 2, conducting a midpoint evaluation in Week 3, and consolidating results in Week 4. Each phase includes dedicated tracker templates, correction checklists, and criteria for when to continue, pause, or reset.The tracking templates section provides a daily betting log, a cumulative performance dashboard, an error audit table, and a monthly summary model - giving readers a complete record-keeping infrastructure. A final troubleshooting chapter addresses the top five beginner mistakes with symptom diagnostics, deviation recovery protocols, and a sustainable commitment checklist.This book does not promise profits. It does not offer advice on picking winners or exploiting odds. What it delivers is a repeatable, measurable system for making sports betting a controlled and accountable activity - one that minimizes avoidable risk and supports consistent decision-making.Ideal for new bettors building their first structured approach and experienced bettors seeking to eliminate costly habits. A 30-day unit-based staking system with bankroll setup, risk thresholds, tracking templates, and week-by-week recovery protocols for controlled, disciplined sports betting decision-making. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Taschenbuch. Condición: Neu. Neuware - As customer demands evolve, traditional support teams face mounting pressure to resolve inquiries quickly and accurately.Many organizations encounter significant obstacles managing the sheer volume and increasing complexity of customer requests. Relying on manual triage or simple rule-based systems often results in inconsistent answers, slow response times, and a backlog of unresolved tickets. The limitations of these methods become even more apparent as knowledge bases grow and customer expectations rise for real-time, precise answers across channels.A scalable customer support solution is now essential to uphold positive brand perception and maintain operational efficiency. AI agents, especially those leveraging Retrieval-Augmented Generation (RAG), offer a path toward delivering consistent, brand-aligned responses at speed and scale. Without robust automation, support teams risk not only reduced productivity, but the spread of misinformation or contradictory advice that can erode customer trust.Introducing AI agents into customer support, however, is not simply a plug-and-play exercise. Many teams hesitate due to the technical complexity and uncertainty around maintaining reliability, privacy, and compliance. This guide addresses those concerns directly, focusing on practical clarity for every phase of an AI agent project from initial planning and tool selection through deployment, risk management, and ongoing refinement. The emphasis remains tightly on customer support applications and methods that can be actioned without the need for deep AI research backgrounds or custom large language model (LLM) training.Efforts outside this scope, such as advanced model development or cross-industry deployments, are not addressed here. Instead, the guide walks sequentially through foundational concepts and actionable steps tailored for customer support scenarios. It begins by demystifying essential terms and RAG workflows, then outlines how to configure and implement a robust, modular architecture. Special attention is paid throughout to integration points ensuring that AI agents can connect and coexist with existing customer relationship management (CRM) and ticketing platforms.In this guide, you will learn: - How to define and plan your RAG-powered customer support project- Key considerations for toolchain selection, including managed vs. open-source solutions- Step-by-step instructions for configuring and integrating AI agents with CRM and ticketing platforms- Best practices for prompt engineering, data pipeline health checks, and escalation logic- How to operationalize guardrails for responsible AI agent behavior, including privacy, moderation, and escalation protocols- Deployment blueprints, monitoring templates, and troubleshooting frameworks to maintain high service quality and resolve issues quickly- Practical tips to future-proof your support operations and ensure ongoing reliability.…

Taschenbuch. Condición: Neu. Neuware.

Taschenbuch. Condición: Neu. Neuware.

Paperback. Condición: new. Paperback. Prediction markets are one of the fastest-growing corners of online trading.Polymarket sits at the center of that growth, offering a decentralized platform where users trade on the outcomes of real-world events using USDC, a cryptocurrency pegged to the US dollar. The concept is straightforward. The setup is not.For beginners, the first obstacle is technical. Polymarket requires a self-custody crypto wallet before you can deposit a single dollar. Setting one up incorrectly means risking your funds or leaving yourself exposed to phishing attacks. Even after the wallet is ready, the platform uses terms and mechanics that take time to learn. Most beginners make costly errors in their first week, not because they lack judgment, but because they never had a clear roadmap.This guide is that roadmap. Written specifically for people who have never used Polymarket, never set up a crypto wallet, and never traded on a prediction market, it covers exactly what a new user needs for a safe and productive first seven days. Nothing more, nothing less.Following a structured, step-by-step approach, this guide walks you through the full setup process, from installing and securing a crypto wallet to funding it with USDC and connecting it to Polymarket. From there, it covers how to navigate the platform's interface, read and evaluate markets, interpret resolution rules, and understand the fees involved in each trade.More than a technical manual, this guide addresses the behavioral side of prediction market trading. It covers the most common emotional mistakes beginners make, including overcommitting funds, chasing losses, and misreading market rules in a rush to act. A structured daily routine for your first seven days and logbook tools to track trades and record your reasoning are built into the process.By the end of this guide, you will be able to set up and secure a self-custody crypto wallet, deposit USDC safely, navigate Polymarket's market listings, place and exit a trade with confidence, and recognize the warning signs of scams and unreliable markets. You will also have a first-week log of your decisions and outcomes, giving you a foundation to evaluate your own trading patterns as you continue.Designed for curious, practical learners who want to understand what they are doing before they risk real money, this guide prioritizes clarity and safety over speed. It is the guide that most Polymarket users wish they had found in their first hour on the platform. Master the world of prediction markets. This guide offers a safe, step-by-step roadmap to setting up your crypto wallet, navigating the platform, and making your first trades with confidence. Your essential seven-day plan to start smart. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condición: new. Paperback. Mastering data-intensive systems requires more than traditional programming skills or a focus on hardware. The velocity and variety of modern data create unique design demands that must be addressed from the ground up. This guide approaches these challenges by equipping you with the fundamental knowledge and practical tools needed to make sharp architectural decisions in contemporary, data-driven environments.The core of any data-intensive system is built upon a series of foundational choices. The approach you take to storage, streaming, and scalability shapes the flexibility, reliability, and future growth potential of your applications. These pillars aren't just technical categories; they represent the everyday decisions that determine whether systems will gracefully accommodate surging user demand or collapse under unanticipated stress.The modern landscape is defined by explosive data growth. Applications today generate, receive, and process data from an expanding array of sources, often in real time. Alongside the data deluge, reliability expectations have tightened. Users, teams, and regulators expect uninterrupted service and precise outcomes, regardless of scale. Systems that cannot handle data influx or recover quickly from errors risk downtime, data loss, or even organizational setbacks. This means system failures and capacity shortfalls are no longer minor inconveniences; they can represent significant business risk.To meet these demands, you need a systematic approach to system planning. This guide provides actionable visual tools and decision checklists aimed at identifying and addressing essential design points before performance bottlenecks and outages occur. Visual concept maps break down complex relationships among storage types and streaming mechanisms, giving clarity at every planning stage. Checklists flag critical requirements and edge conditions, helping catch potential flaws early. Failure pattern tables highlight common pitfalls, making diagnostic work more effective when things go wrong.This guide focuses intentionally on core architecture and design fundamentals. It does not cover deep algorithmic tutorials, industry-specific compliance standards, or application-layer user guidance. Instead, each chapter delivers clear, usable frameworks suitable across a broad range of domains and systems. By maintaining this scope, the guide ensures that every visual, checklist, and diagram remains general, practical, and widely applicable.In this guide, you will discover: Definitions and distinctions among functional storage categories, reviewing where block, file, and object storage each excel.Operating principles for streaming data flows, including real-time pipeline mechanics, buffering, and event management.Proven approaches to system scaling, with frameworks for vertical and horizontal growth and clear diagrams illustrating partitioning and replication.Visual concept maps that tie together architecture choices, making system relationships easy to understand.Decision checklists for evaluating durability, consistency, throughput, and scaling capacity at critical design milestones.Failure pattern tables and comparative diagrams to quickly identify and address common bottlenecks or design missteps.Diagnostic templates and reference charts for live evaluation and troubleshooting, so you can adapt strategies as data volumes increase.Explanations of common bottlenecks and clear guides to preventing the most frequent system slowdowns and breakdowns. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.