Template-Driven AI Platforms: Using Infrastructure as Code and GitOps to Standardize and Scale AI/ML Environments
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Template-Driven AI Platforms: Using Infrastructure as Code and GitOps to Standardize and Scale AI/ML Environments
Santosh Pashikanti
Abstract
Enterprise AI and ML initiatives are often slowed down not by algorithms, but by fragmented, snowflake environments that are hard to reproduce, scale, and govern. Each new project tends to reinvent its own cloud setup, Kubernetes cluster, data access pattern, and ML toolchain, creating a long tail of operational toil and “hidden technical debt” in production systems [3]. In this paper, I present a template-driven approach for building AI platforms using Infrastructure as Code (IaC) and GitOps, with Terraform, Helm, and Kubernetes as the core building blocks. Drawing on cloud-native principles and lessons from real-world transformations, I describe how standardized templates, modular Terraform and Helm packages, and Git-centric workflows can deliver repeatable, compliant, and scalable AI/ML environments across teams, environments, and clouds. I outline the architecture of a template-driven AI platform, walk through a concrete multi-cloud implementation, and evaluate the approach along dimensions such as time-to-environment, deployment frequency, drift reduction, and compliance. I close with a discussion of trade-offs, organizational challenges, and practical recommendations for platform teams that want to industrialize AI without sacrificing flexibility for data scientists and ML engineers.
Index Terms
Infrastructure as Code, GitOps, Terraform, Helm, Kubernetes, MLOps, AI Platforms, Multi-Cloud, DevOps, Continuous Delivery.