3 hours EN / DE Max 50

AI Infrastructure Essentials

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. It is designed for IT decision-makers and infrastructure architects seeking to understand enterprise-grade AI deployment.

€295,00 excl. VAT

Individual scheduling

The courses are held as dedicated group sessions. Once you've booked, we'll coordinate a date that works for your team and send invitations to all participants.

Prerequisites

  • Familiarity with cloud computing concepts.
  • Understanding of general data center infrastructure.

What you'll learn

  • 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.

Course modules
Definition of AI infrastructure The evolution of computing demands The need for new computing power
The AI Hypercomputer The 3 layers of the AI Hypercomputer: Overview
Graphics Processing Units (GPU architecture, Google Cloud GPU family, Selecting GPUs) Tensor Processing Units (TPU architecture, Google Cloud TPU family, Best practices and considerations)
Maximizing goodput Networking for data ingestion and training Storage for data preparation and training Architecture for inference.
Deployment options Flexible consumption
Course summary Q&A Quiz
AI Infrastructure Essentials