Lilly Tech Systems
Terraform for AI Infrastructure
Provision and manage GPU cloud infrastructure for AI workloads using Terraform. Learn to deploy GPU instances, configure networking and storage, build reusable modules, and implement production IaC patterns for ML platforms.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
Why Infrastructure as Code matters for AI, Terraform fundamentals, and the AI infrastructure landscape.
2. GPU Instances
Provision GPU VMs and Kubernetes clusters on AWS, GCP, and Azure with the right instance types for AI.
3. Networking
Configure VPCs, subnets, security groups, and high-bandwidth networking for distributed GPU training.
4. Storage
Set up object storage, shared file systems, and high-performance storage for datasets and model artifacts.
5. Modules
Build reusable Terraform modules for ML platforms with composable GPU clusters, storage, and networking.
6. Best Practices
Production IaC patterns: state management, CI/CD, cost controls, drift detection, and multi-environment strategies.
What You'll Learn
By the end of this course, you'll be able to:
Provision GPU Infrastructure
Deploy GPU instances, K8s clusters, and managed ML services across AWS, GCP, and Azure with Terraform.
Build Reusable Modules
Create composable Terraform modules for standard AI infrastructure patterns your team can reuse.
Manage State Safely
Configure remote state, locking, workspaces, and import existing infrastructure without downtime.
Automate with CI/CD
Integrate Terraform with GitHub Actions, Atlantis, or Terraform Cloud for automated infrastructure deployment.