AI Infrastructure: Deployment Types

AI Infrastructure: Deployment Types

Instructor: Google Cloud Training

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6 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Some related experience required

5 hours to complete

Flexible schedule

Learn at your own pace

6 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Some related experience required

5 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Describe the process of creating a GPU-accelerated cluster.
Identify how to provision a GPU-accelerated cluster on GCE.
Identify how to provision a GPU-accelerated cluster on GKE.
Identify how to deploy AI inference workloads on GKE.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

4 assignments

Taught in English

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There are 6 modules in this course

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.

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

What's included

1 plugin

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.

What's included

1 assignment6 plugins

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.

What's included

1 assignment4 plugins

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.

What's included

1 assignment4 plugins

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