Course Overview
This course provides a foundational overview of the hardware, software, and networking components required to develop and manage AI models at scale. It explores Google Cloud's AI Hypercomputer architecture, compares compute accelerators like GPUs and TPUs, and examines the critical data pipelines and storage solutions necessary to maximize training performance.
Who should attend
IT decision-makers and infrastructure architects looking to understand the technical requirements and the AI Hypercomputer’s offerings for enterprise-grade AI deployment.
Prerequisites
Familiarity with cloud computing concepts and general data center infrastructure.
Course Objectives
- Differentiate between the layers of the AI Hypercomputer.
- Select appropriate accelerators for the most cost-effective AI workloads.
- Evaluate storage and networking solutions to maximize training goodput.
- Compare various deployment and consumption models for resource optimization.
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