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AI Infrastructure: Deployment Types

IntermediateSkill Level

Updated 07/2026

A guide to deploying, managing, and optimizing AI and high-performance computing (HPC) workloads on Google Cloud.

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1 hr 30 min

28 Exercises

1,400 XP

Statement of Accomplishment

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Course Description

This course provides a comprehensive guide to deploying, managing, and optimizing AI and high-performance computing (HPC) workloads on Google Cloud. Through a series of lessons and practical demonstrations, you’ll explore diverse deployment strategies, ranging from highly customizable environments using Google Compute Engine (GCE) to managed solutions like Google Kubernetes Engine (GKE). Specifically, you’ll learn how to create clusters and deploy GKE for inference.

Prerequisites

There are no prerequisites for this course

1

Course overview

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

What's in it for me?

2

Cluster creation process

This module details the AI Hypercomputer cluster creation process. It covers the key decisions required, including choosing a machine type, consumption option, deployment option, orchestrator, and cluster image.

Process overview

Choosing a machine type

Choosing the consumption option

Choosing the deployment options

Choosing an orchestrator

Choosing an image

Quiz Question 1

3

Creating a cluster with Compute Engine

This module identifies key configuration options and optimization techniques for deploying an AI Hypercomputer cluster on Google Compute Engine (GCE). It covers selecting machine types, accelerator OS images, deployment options, and strategies for optimizing network performance.

Choosing a GCE machine type

Choosing a GCE deployment option

Networking for GCE instances

Reference architecture

Quiz Question 1

Quiz Question 2

4

Building with Google Kubernetes Engine (GKE)

This module identifies configuration options for deploying an AI Hypercomputer cluster on Google Kubernetes Engine (GKE). It covers containerization, GKE modes of operation, networking configurations, and workload optimization techniques like distributed training and GPU sharing.

Containerizing an AI application

Networking for GKE cluster deployments

Optimizing an AI workload

GPU sharing strategies

Quiz Question 1

Quiz Question 2

Quiz Question 3

5

Deploying with GKE for inference

This module examines optimization techniques for architecting an inference workload on GKE. It covers the GKE inference workflow, key infrastructure, and model-level optimizations.

Architecting for inference on GKE

GKE inference reference architecture

Optimizing inference with GKE Inference Gateway

Congratulations and next steps

Quiz Question 1

Quiz Question 2

6

Course resources

Student PDF links to all modules

Course resources

AI Infrastructure: Deployment Types

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