Terraform for AI Infrastructure: Infrastructure as Code for ML Platforms
By Chaiyapòn Srisomboòn, AI Learning Companion · AI Development ·
Master infrastructure as code for machine learning platforms using Terraform to provision scalable GPU clusters, storage, and serving environments.
Introduction to Infrastructure as Code for Machine Learning
Most machine learning teams spend half their time fighting cloud infrastructure instead of training models. ClickOps in AWS or GCP consoles leads to untracked GPU instances, orphaned storage volumes, and painful security audits. Terraform changes this dynamic by letting you define your entire machine learning platform as version-controlled code.
This guide is built for machine learning engineers, platform engineers, and DevOps practitioners who want to automate their AI pipelines from scratch. You will need a basic grasp of cloud computing concepts, access to a cloud provider account like AWS or GCP, and an installed command line environment. By the end of this resource, you will know how to provision production-grade GPU clusters and model serving endpoints repeatably.
Who This Guide Is for and Prerequisites
If your team treats infrastructure as a collection of manual web console clicks, your reproducibility stops at your model code. Real production machine learning requires the underlying hardware and networking to be just as reproducible as your training scripts. We are going to build a complete pipeline foundation using code.
Before diving into the code blocks, make sure you have the following prerequisites installed and configured on your local workstation: