Google Cloud

AI 基础设施:网络组建和管理技术

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Google Cloud

AI 基础设施:网络组建和管理技术

Included with Coursera Plus

Gain insight into a topic and learn the fundamentals.
Intermediate level
Some related experience required
4 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level
Some related experience required
4 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • 确定 AI 流水线各个阶段的网络组建和管理需求,并找出适合的 Google Cloud 产品。

  • 区分不同 GPU 和 TPU 机器类型的网络组建和管理特性。

  • 确定适用于数据注入、AI 训练和生成式 AI 推理的网络组建和管理选项

  • 了解 AI 网络组建和管理最佳实践。

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Recently updated!

April 2026

Assessments

4 assignments

Taught in Chinese (Simplified)

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There are 6 modules in this course

本单元概述了课程内容并列出了学习目标。

What's included

1 plugin

本单元详细介绍了 AI 工作负载相较于传统 Web 应用所具有的特殊网络组建和管理需求。内容涵盖了从数据注入到推理的各个流水线阶段在带宽和延迟时间方面的具体要求,并分析了 Google Cloud A3 和 A4 GPU 机型如何通过“轨道对齐”的网络架构,最大程度提高“有效吞吐量”。

What's included

1 assignment3 plugins

本单元详细介绍了将海量数据集高效迁移到云端的策略。内容涵盖了如何利用跨云网络和 Cloud Interconnect 构建高带宽流水线,并总结了相关的配置最佳实践(例如启用 Jumbo Frames [MTU]),以减少协议开销并优化吞吐量。

What's included

1 assignment2 plugins

本单元详细介绍了低延迟网络组建和管理在分布式模型训练中的关键作用。内容涵盖了 RDMA(远程直接内存访问)对于梯度同步的必要性、Google Titanium 分流架构在释放 CPU 资源方面的优势,以及在不产生瓶颈的情况下扩展集群所需的拓扑方案。

What's included

1 assignment3 plugins

本单元详细介绍了生成式 AI 推理所面临的特定网络组建和管理挑战,例如突发流量和长期有效的连接。内容涵盖了如何通过 GKE Inference Gateway 和“队列深度”路由来优化首 token 延迟,同时还介绍了在网络可靠性以及 Identity and Access Management (IAM) 方面的最佳实践。

What's included

1 assignment5 plugins

所有单元的学员 PDF 链接

What's included

1 reading

Instructor

Google Cloud Training
Google Cloud
2,153 Courses4,158,304 learners

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Google Cloud

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