Google Cloud AI Infrastructure
Google Skills
Last updated January 21, 2026
A focused, roughly six-hour path for technical learners who want to design and deploy high-performance AI and ML workloads on Google Cloud's infrastructure. It covers the AI Hypercomputer, Cloud GPUs and TPUs, deployment types, storage options, and networking — the layers underneath an AI system, from hardware up through orchestration and best practices. Concept material is free; any hands-on labs run on Google Skills and need a subscription or credits. It is pitched at intermediate-to-advanced technical practitioners.
What you'll learn
- The AI Hypercomputer architecture on Google Cloud
- Cloud GPUs and Cloud TPUs for AI and ML workloads
- Deployment types for AI/ML solutions
- Storage options for high-performance workloads
- Networking techniques for AI infrastructure
Frequently asked questions about Google Cloud AI Infrastructure
Who is Google Cloud AI Infrastructure for?
For intermediate-to-advanced technical practitioners who want to design, deploy, and optimize AI and ML infrastructure on Google Cloud.
What are the prerequisites for Google Cloud AI Infrastructure?
Intermediate-to-advanced technical background; aimed at practitioners deploying AI/ML workloads.
Why we suggest this course
This fills a part of the stack most AI courses skip — the hardware and infrastructure that AI workloads actually run on — and does it through Google's specific story of AI Hypercomputer, TPUs, and GPUs, which is genuinely distinct from other cloud providers. One thing to know: it is a specialized, infrastructure-focused path aimed at technical practitioners deploying AI workloads, not a general introduction to AI.