AI Infrastructure: Storage Options

AI Infrastructure: Storage Options

位教师：Google Cloud Training

访问权限由 Coursera Learning Team 提供

5个模块

深入了解一个主题并学习基础知识。

中级等级

需要一些相关经验

4 小时完成

灵活的计划

自行安排学习进度

5个模块

深入了解一个主题并学习基础知识。

中级等级

需要一些相关经验

4 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Determine the appropriate storage options and storage best practices for each phase of the AI data pipeline.
Determine the right storage solutions within each phase of the AI data pipeline.
Identify storage options and techniques for data preparation, model training, model serving, and data archiving.
Explore example storage architectures for model training and serving.

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作业

3 项作业

授课语言：英语（English）

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该课程共有5个模块

In this course, you’ll take a comprehensive journey through the storage solutions available on Google Cloud, specifically tailored for AI and high-performance computing (HPC) workloads. You’ll learn how to choose the right storage for each stage of the ML lifecycle. You’ll explore how to optimize for I/O performance during training, manage massive datasets for data preparation, and serve model artifacts with low latency. Through practical examples and demonstrations, you’ll gain the expertise to design robust storage solutions that accelerate your AI innovation.

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

涵盖的内容

1个插件

This module details the role of storage infrastructure in the AI data pipeline. It covers performance demands, key Google Cloud solutions, and the decision criteria for selecting a service based on capacity, throughput, and latency.

涵盖的内容

1个作业2个插件

This module details the critical phases of data preparation and model training within the AI workflow. It covers optimizing data loading using Cloud Storage, Anywhere Cache, and the Dataflux Dataset tool, while comparing high-performance file systems like Cloud Storage FUSE and Managed Lustre. Additionally, it outlines decision criteria for efficient checkpointing strategies to ensure fault tolerance and minimize GPU idle time.

涵盖的内容

1个作业5个插件

This module details strategies for AI model serving and data archiving. It covers selecting storage—Managed Lustre, Cloud Storage, or Hyperdisk ML—based on scale and latency, and optimization techniques, like GKE Image Streaming and Cloud Storage FUSE, to minimize costs and load times.