6 docs tagged with "infrastructure-as-code"

Solve the dev/staging/prod parity problem for ML - feature skew, infrastructure differences, data drift, and environment promotion pipelines that prevent production surprises.

Apply GitOps principles to ML infrastructure - Flux CD, ArgoCD, image update automation, secrets management, and PR-gated model deployments with Argo Rollouts.

Production IaC patterns for ML platform engineering - golden paths, blue-green infrastructure, self-destructing experiment environments, OPA policies, GPU quota management, and the internal developer platform model.

Write ML infrastructure in real Python - Pulumi's code-first approach, component resources, Automation API, and testing with pytest for reproducible ML platforms.

Build complete ML platforms with Terraform - GPU clusters, MLflow, EKS, feature stores, and model registries using production-grade HCL modules.

Master Terraform core concepts - providers, resources, state management, modules, and the plan/apply lifecycle for building reproducible ML infrastructure.