首页 > 科技 > 计算机 > 计算机科学

DeepSeek企业应用指南

作译者：高承实

出版时间：2025-11 千字数：485 版次：01-01 页数：336

开本：16开装帧： I S B N ：9787121515804

换版：

纸质书定价：￥88.0

库存:无

分享到：

共有图书评论0条【查看评论摘要】

看了又看

{{data.bookName}}

￥{{data.price}}

内容简介

前言

上架建议

作者简介

编辑推荐

音视频专区

第1 章 企业应用入门···············································································1
1.1 AI 的发展和DeepSeek 的横空出世····················································2
1.2 DeepSeek 的核心功能和独特优势······················································6
1.3 DeepSeek 在企业中的应用价值······················································.10
1.4 企业部署DeepSeek 需要考虑的因素···············································.12
1.5 企业部署DeepSeek 的主要环节·····················································.26
第2 章 环境搭建与配置·········································································.28
2.1 共性工作··················································································.28
2.2 不同部署方式下的环境搭建与配置·················································.33
2.3 本地部署方式下的环境准备与配置·················································.34
2.4 云端部署方式下的环境准备与配置·················································.40
2.5 企业级部署方式下的环境准备与配置··············································.49
第3 章 企业应用基础············································································.53
3.1 数据准备与预处理······································································.53
3.2 模型训练与评估·········································································.60
3.3 模型部署与应用·········································································.71
第4 章 提示语设计与优化······································································.82
4.1 企业应用环境下提示语设计与优化的作用········································.82
4.2 不同环节下的提示语设计与优化····················································.85
第5 章 模型微调与个性化定制································································.96
5.1 模型微调··················································································.96
5.2 个性化定制方法·········································································106
第6 章 多模态数据处理与融合······························································.112
6.1 数据采集与预处理·····································································.112
6.2 特征提取··················································································121
6.3 模态融合··················································································124
6.4 模型训练与优化·········································································131
第7 章 分布式训练·············································································.137
7.1 计算集群架构············································································137
7.2 并行策略··················································································139
7.3 关键技术优化············································································148
7.4 启动分布式训练·········································································161
第8 章 与企业级专用AI 的结合·····························································.165
8.1 与专用AI 结合的原因·································································165
8.2 与办公协作AI 工具钉钉的结合·····················································169
8.3 与知识管理工具HelpLook 的结合··················································175
8.4 智能客服与对话工具助小咖的结合·················································181
第9 章 与ERP 等企业业务管理系统的结合··············································.189
9.1 与企业业务管理系统结合的特殊性·················································189
9.2 制造业ERP 系统的特点和不足······················································190
9.3 DeepSeek 可以解决的制造业ERP 系统问题······································192
9.4 DeepSeek 与ERP 系统结合的方式、方法和步骤································196
9.5 案例························································································207
第10 章 创建企业应用AI Agent·····························································.209
10.1 什么是AI Agent ·······································································209
10.2 AI Agent 作用发挥的主要领域和适用场景·····································.211
10.3 AI Agent 的结构组成和创建方法··················································214
10.4 基于DeepSeek 创建企业应用AI Agent···········································224
10.5 多Agent 系统··········································································226
10.6 MCP、A2A 和ANP···································································233
第11 章 创建和集成工作流···································································.240
11.1 什么是工作流··········································································240
11.2 工作流的分类··········································································244
11.3 工作流的适用领域和场景···························································247
11.4 创建工作流的一般方法······························································249
11.5 基于DeepSeek 创建企业应用工作流··············································255
11.6 基于DeepSeek 集成工作流··························································263
第12 章 DeepSeek 应用驱动产业转型升级··············································.265
12.1 智能客服系统··········································································265
12.2 质量检测与预测·······································································270
12.3 内容推荐与个性化服务······························································274
12.4 疾病诊断与治疗方案推荐···························································279
12.5 供应链优化与智能调度······························································282
12.6 智能教学辅助系统····································································286
12.7 DeepSeek 及应用驱动产业转型升级··············································290
第13 章 企业应用DeepSeek 面临的挑战与应对·······································.303
13.1 数据安全与隐私保护策略···························································303
13.2 模型幻觉与准确性问题······························································307
13.3 可解释性与逻辑推理能力不足·····················································312
13.4 长文本处理和上下文理解的局限··················································314
13.5 领域适应性与多语言支持问题·····················································316

DeepSeek 企业应用开启智能商业新时代
在当今数字化浪潮席卷全球的商业环境下，AI 技术正以前所未有的速度重塑各个行业的竞争格局。企业若想在激烈的市场竞争中脱颖而出，实现可持续发展与创新突破，就必须抓住这股科技革命的浪潮，将AI 深度融入自身的核心业务流程与战略规划之中。DeepSeek 作为一种先进且强大的AI 技术，迅速成为众多企业开启智能商业新时代的关键引擎，为各个领域的企业发展注入全新的活力与智慧。
“企业智能化”不是遥远的愿景，而是现实中的竞争门槛。
从办公自动化、内容生成，到智能客服、商业洞察，AI 正在成为企业日常运转的底层能力；随着大模型推理能力、自主性和行业定制化水平的不断提高，它也正在扮演越来越多过去依赖人力完成的决策与执行角色。然而，在技术变革
的浪潮中，企业容易陷入两种常见的误区，一是认为AI 只是“用起来很炫”的小工具，忽视了它对生产力系统性重构的本质；二是急于部署，缺乏系统的落地路径和清晰的方法论，导致“雷声大雨点小”。
于是，我便有了写这本《DeepSeek 企业应用指南》的初衷。
一、AI 发展与DeepSeek 的时代价值
过去十年，AI 经历了三个重要阶段。
1.0 阶段是算法驱动，以图像识别、语音识别、推荐算法为代表的浅层学习模型，解决了“能看、能听、能学”的问题；2.0 阶段是数据驱动，以大数据训练为基础，提高了AI 对复杂场景的理解能力；3.0 阶段是以大模型为核心的通用智能跃迁。3.0 阶段的核心是LLM 具备了强泛化能力、上下文记忆能力、任务泛适应能力，能够与人进行近似自然语言的交流，甚至完成复杂逻辑链条的推理与决策。
DeepSeek 正是在这一背景下迅速崛起的。作为国产大模型中的佼佼者，其不仅具备通用模型的性能指标优势（如语言理解、代码生成、知识问答等），还具有本地化部署、行业定制能力和开放生态策略，为中国企业提供了一个更可
控、更易接入的大模型解决方案。
2025 年春节后，DeepSeek 在三个维度上，逐步确立了在企业端的优势地位。
一是技术适配度高，支持私有化部署、国产算力适配、支持中文长文本处理等，符合国内企业对数据安全与性能的双重需求；二是工具链完备，围绕模型应用程序接口（Application Program Interface，API）、开发框架、智能体调度器等形成完整的应用链条；三是积极共建生态，与软件厂商、行业软件即服务（Software as aService，SaaS）系统、地方政府及高校合作，持续推进“模型+行业”深度融合。
DeepSeek 不仅是一个大模型，还是企业智能化升级过程中的“基础设施”。
二、DeepSeek 在企业中的应用价值和部署的关键考量
在企业实际运营中，DeepSeek 的应用价值体现在多个关键环节。它能够优化客户服务体验，通过智能客服系统实现7×24 小时的全天候服务，精准解答客户咨询，提高客户满意度与忠诚度；在生产运营方面，DeepSeek 能够协助企业优化生产流程，检测与预测质量，降低生产成本，提高生产效率与产品质量；在市场营销方面，DeepSeek 能够助力企业进行精准的市场分析、内容创作与个性化推荐，增强品牌影响力，提升营销效果与转化率；在企业管理决策层面，DeepSeek 能够整合企业内外部数据，进行深度分析与预测，为管理层提供全面、准确的信息，辅助制定科学合理的发展战略，推动企业实现高质量、可持续发展。
尽管DeepSeek 为企业带来了诸多潜在价值，但企业在部署过程中仍需综合考量多个关键因素，确保其能与企业现有业务体系深度融合并发挥最大效能。首先，企业需明确自身的业务需求与应用场景，精准定位DeepSeek 在企业中的应用方向，避免盲目跟风与资源浪费；其次，企业需根据规模、业务复杂度、预算等因素合理选择DeepSeek 的版本、数值精度及部署方式，如本地部署、云端部署或混合部署等，满足企业对数据安全、性能、成本等方面的个性化需求；再次，企业需关注与DeepSeek 部署相关的配套基础设施建设，包括硬件设备、软件环境、网络带宽等，确保运行环境稳定可靠，同时建立专业的技术团队或与专业的技术服务商合作，负责DeepSeek 的安装、调试、维护与优化工作，保障系统的持续稳定运行；最后，企业需制定完善的数据管理策略，确保数据的质量、安全与合规性，为DeepSeek 的有效应用提供高质量的数据基础。
三、DeepSeek 企业应用的全方位探索
随着DeepSeek 在企业中应用的不断深入，企业需积极探索DeepSeek 在不同业务领域与场景的创新应用模式。在智能制造领域，将DeepSeek 与工业物联网（Internet of Things）、机器人技术结合，实现生产设备的智能化监控、故障预测与自动化运维，打造智能化工厂；在金融领域，利用DeepSeek 的数据分析与风险评估能力，构建智能风控模型，优化投资决策流程，提高金融服务的质量与效率；在医疗健康领域，借助DeepSeek 的知识整合与推理能力，辅助医生进行疾病诊断、治疗方案推荐与医学研究，推动智慧医疗的发展；在教育领域，通过DeepSeek 为学生提供个性化学习辅导、智能答疑与学习路径规划，提升教育教学效果，提高人才培养质量。同时，企业还需不断探索DeepSeek 与其他新兴技术的融合创新，如大数据、云计算、区块链等，充分发挥技术协同效应，开拓更广阔的应用空间与商业价值。
本书将视角聚焦于三个方面。一是部署路径明确，从接入模型、构建智能体和工作流程到与现有系统集成、建立治理机制，每一步应如何推进？二是风险与合规意识强，如何规避大模型在企业应用中可能带来的幻觉、误导、安全隐患等？三是行业视野开放，哪些行业已率先应用？成功与失败的案例分别体现了什么逻辑？
我们希望通过一套结构化、工程化、面向未来的AI 企业应用地图，帮助企业以更加从容、系统化的方式拥抱大模型技术。
本书将深入探讨DeepSeek 在企业中的应用，从技术基础、部署实施、业务场景应用到多模态数据处理、与其他系统集成及行业解决方案等多方面进行详细的阐述。通过阅读本书，读者将全面了解DeepSeek 的技术特点与优势，掌握企业部署与应用DeepSeek 的关键，探索其在不同领域的创新应用模式，为企业智能化转型提供有力支持与实践指导，助力企业在智能商业时代的浪潮中乘风破浪，实现可持续发展的宏伟目标。
DeepSeek 代表的大模型力量，是一次从“辅助工具”到“工作伙伴”，再到“流程主导者”的角色跃迁。这意味着，未来企业的核心竞争力，不只是人才或技术，还是“人与AI 如何协作”的系统能力。
我们正在经历的不仅是技术进步，还是“人—机—组织”关系的重构。本书希望为这次重构提供清晰的坐标与行动指南。
愿你在阅读本书的过程中获得启发，在实践中收获成果。

查看更多 > 图书评论

暂无评论

发表图书评论

评论标题：
评论内容：
验证码：: 看不清楚
点击刷新
: 您还没有登录，请登录后再评论。
点击“登录”

购买过本书的顾客还买过

{{data.bookName}}

￥{{data.price}}