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Based on the title, this piece explains how to treat AI/ML systems as Infrastructure as Code: declaring data pipelines, feature stores, training/serving clusters, and dependencies in versioned code. It highlights GitOps and CI/CD for models, reproducibility, security and policy as code, GPU and cost orchestration, and monitoring—using tools like Terraform, Kubernetes and Helm to reduce drift and speed delivery.