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AI Infrastructure: Storage Options

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AI Infrastructure: Storage Options

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

Exercise 1: Course introduction

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.

Exercise 1: AI data pipeline and storage solutions Exercise 2: Key storage metrics Exercise 3: Quiz Question 1

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.

Exercise 1: Data preparation Exercise 2: Data loading Exercise 3: Checkpointing Exercise 4: Cloud Storage FUSE Exercise 5: Managed Lustre Exercise 6: Quiz Question 1 Exercise 7: Quiz Question 2 Exercise 8: Quiz Question 3

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.

Exercise 1: Model serving or inference Exercise 2: Accelerating inference with GKE Image Streaming Exercise 3: Optimizing data retrieval with Cloud Storage Fuse and Hyperdisk ML Exercise 4: Data archiving Exercise 5: Reference architectures Exercise 6: Congratulations and next steps Exercise 7: Quiz Question 1 Exercise 8: Quiz Question 2 Exercise 9: Quiz Question 3 Exercise 10: Quiz Question 4

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Exercise 1: Course resources

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