AI Infrastructure: Orchestration and Automation

Overview

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This course covers the automation and operational management of GKE clusters optimized for distributed AI and machine learning workloads. You will learn to provision accelerator resources, configure advanced workload queueing, mitigate capacity constraints, and orchestrate large-scale distributed jobs.

Syllabus

Module 0: Introduction
- This module defines the purpose of the course.
AI Workloads on Google Kubernetes Engine
- Examine AI infrastructure challenges and explore core Kubernetes components to learn how GKE optimizes the full AI lifecycle. Additionally, explore various tools available to automate cluster creation.
Optimize Scheduling and Resource Allocation
- Optimize multi-tenant AI workloads on GKE using Kueue for job queuing, Dynamic Workload Scheduler (DWS) for GPU/TPU provisioning, Topology-Aware Scheduling (TAS) for low latency, and custom Compute Classes (CCC) for automated compute fallbacks.
Orchestrate Distributed Training
- The module dives into the KubeRay operator architecture and its custom resources (RayCluster, RayJob, and RayService). We will also explore how to orchestrate distributed Ray workloads on GKE.
Summary
- Student PDF links to all modules