Name: AI Infrastructure: Networking Techniques
Rating: 4.818181818181818 (11 reviews)

AI Infrastructure: Networking Techniques

Design and deploy high-performance AI/ML solutions using Google Cloud's AI Hypercomputer, GPUs, TPUs, Compute, and Google Kubernetes Engine.

Google CloudCloud1 ч{count, plural, one {# упражнение} few {# упражнения} many {# упражнений} other {# упражнения}}1,150 XP42Заявление об успешном завершении

R1 ч{count, plural, one {# упражнение} few {# упражнения} many {# упражнений} other {# упражнения}}1,150 XP42Заявление об успешном завершении

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Описание курса

Welcome to the "AI Infrastructure: Networking Techniques" course. In this course, you'll learn to leverage Google Cloud's high-bandwidth, low-latency infrastructure to optimize data transfer and communication between all the components of your AI system. By the end, you'll grasp the critical role networking plays across the entire AI pipeline from data ingestion and training to inference and be able to apply best practices to ensure your workloads run at maximum speed.

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Структура курса

Course overview

This module offers an overview of the course and outlines the learning objectives.

Course introduction50 XP

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Introduction

This module details the specialized networking requirements for AI workloads compared to traditional web applications. It covers the specific bandwidth and latency demands of each pipeline stage—from ingestion to inference—and analyzes the "rail-aligned" network architectures of Google Cloud's A3 and A4 GPU machine types designed to maximize "Goodput."

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Networking for data ingestion

This module details strategies for efficiently moving massive datasets into the cloud. It covers the use of the Cross-Cloud Network and Cloud Interconnect to establish high-bandwidth pipelines, and outlines configuration best practices—such as enabling Jumbo Frames (MTU)—to reduce protocol overhead and optimize throughput.

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Networking for AI training

This module details the critical role of low-latency networking in distributed model training. It covers the necessity of Remote Direct Memory Access (RDMA) for gradient synchronization, the benefits of Google's Titanium offload architecture in freeing up CPU resources, and the topology choices required to scale clusters without bottlenecks.

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Networking for inference

This module details the networking challenges specific to Generative AI inference, such as bursty traffic and long-lived connections. It covers optimizing Time-to-First-Token using the GKE Inference Gateway and "Queue Depth" routing, while also addressing best practices for network reliability and Identity and Access Management (IAM).