February 2026
Intermediate to advanced
406 pages
4h 57m
Chinese
Overview
本作品已使用人工智能进行翻译。欢迎您提供反馈和意见：translation-feedback@oreilly.com
生成式人工智能正在彻底改变各行各业，而 Kubernetes 已迅速成为部署和管理这些资源密集型工作负载的支柱。本书是 MLOps 工程师、软件开发人员、Kubernetes 管理员以及准备将人工智能创新与云原生基础设施的强大功能相结合的人工智能专业人士的实用实践指南。作者罗兰-胡斯（Roland Huß）和丹尼尔-宗卡（Daniele Zonca）为在 Kubernetes 上训练、微调、部署和扩展 GenAI 模型提供了清晰的路线图，并一路解决了资源优化、自动化和安全等挑战。
读者将通过实际案例获得可行的见解，学会在生产环境中应对管理 GenAI 应用程序的机遇和复杂性。无论您是在尝试使用大规模语言模型，还是在面对大规模人工智能部署的细微差别，您都将了解到有效操作这一令人兴奋的技术所需的专业知识。
- 了解如何通过优化推理运行时间更高效地部署 LLMs
- 亲身体验 GPU 调度，包括硬件检测和多节点扩展
- 监控和了解 LLM 的特定指标，如到第一个令牌的时间和令牌吞吐量
- 了解何时对模型进行微调或何时检索增强是更好的选择
- 了解如何在投入 GPU 资源之前使用标准化基准对模型进行评估
- 了解如何通过安全工具集成、身份管理和持久状态运行代理应用程序
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