Sari la conținutul principal

Curs

AI Infrastructure: Storage Options

Intermediar1 h

Journey through the storage solutions available on Google Cloud, specifically tailored for AI and high-performance computing (HPC) workloads.

R1 h23 exerciții1,150 XP69Certificat de realizare

Creează-ți contul gratuit

Continuă cu Google
sau
Continuând, accepți Termenii de utilizare, Politica de confidențialitate și faptul că datele tale sunt stocate în SUA.

Apreciat de cursanți din mii de companii

Instruiești o echipă?

Încearcă pentru companii

Descrierea cursului

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.

Cerințe prealabile

Nu există cerințe prealabile pentru acest curs

Programă

Planul cursului

1

Course overview

This module offers an overview of the course and outlines the learning objectives.
Începe capitolul
2

Foundations of AI storage

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.
Începe capitolul
3

Prepare and train

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.
Începe capitolul
4

Serve and archive

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.
Începe capitolul
5

Course resources

Student PDF links to all modules
Începe capitolul
R

AI Infrastructure: Storage Options

Curs
finalizat

Obține diploma de absolvire

Înscrie-te acum

Dezvoltă-ți competențele în date cu DataCamp for Mobile

Fă progrese din mers cu cursurile noastre pentru mobil și provocările zilnice de programare de 5 minute.