Infrastructure as Code for ML: Terraform + SageMaker

ML systems do not run on code alone; they need infrastructure, training clusters, GPU instances, model endpoints, storage buckets, networking, and provisioning that by hand through a cloud console is slow, error-prone, undocumented, and impossible to reproduce. Infrastructure as Code, IaC, solves this by defining infrastructure in declarative configuration files that a tool reads to create, update, and destroy real resources, so your environment becomes versioned, reviewable, and reproducible just like application code. Terraform is the dominant cloud-agnostic IaC tool, describing resources in declarative configuration and reconciling reality to match it, and SageMaker is AWS's managed ML platform whose training jobs, models, and endpoints are themselves resources Terraform can provision. The problem this solves is the fragile, manual, snowflake infrastructure that plagues ML teams, an endpoint someone clicked into existence months ago that nobody can recreate, environments that drift apart, and no record of what exists or why. IaC replaces clicking with code: the same configuration creates identical dev, staging, and production environments, every change is a reviewable diff, and tearing down and rebuilding the entire ML stack becomes a single command rather than a week of careful clicking.