courseraПрограммирование

AI Infrastructure: Networking Techniques

Курс от Google Cloud

Средний≈ 3.9 чАнглийский

О курсе

Welcome to the ""AI Infrastructure: Networking Techniques"" course. While AI Hypercomputer is renowned for its massive computational power using GPUs and TPUs, the secret to unlocking its full potential lies within the network. High-performance computing and large-scale model training demand incredibly fast, low-latency connections to continuously feed processors with data. In this course, you will 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 will 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.

Навыки, которые вы освоите

Network Architecture Network Performance Management Network Infrastructure Identity and Access Management Load Balancing Network Protocols Distributed Computing Cloud Infrastructure AI Workflows System Configuration Data Import/Export Multi-Cloud Generative AI Google Cloud Platform Network Security Virtual Networking Network Planning And Design

Программа курса

6 модулей · 19 учебных материалов

01Course overview1 материалов

Course overview

Course introductionPLUGIN

02Introduction4 материалов

Introduction

Role of networking in AI pipelinePLUGIN AI Hypercomputer networking for ML workloadsPLUGIN Networking for GPU machine typesPLUGIN

Учитесь у экспертов

Google Cloud Training

Преподаватель курса