Infrastructure as Code for AI: Engineering Kubernetes Orchestration for Deep Learning Workloads (The Cloud-Native AI Orchestration Series)

What if your AI infrastructure could be reproducible, version-controlled, and rebuilt without relying on manual configuration?

Infrastructure as Code for AI: Engineering Kubernetes Orchestration for Deep Learning Workloads explores how to automate the infrastructure behind modern machine learning and AI systems using Terraform, Kubernetes, GitOps, and cloud-native tooling.
Designed for AI engineers, DevOps professionals, platform engineers, and MLOps teams, this book focuses on the infrastructure layer that makes demanding AI workloads easier to provision, manage, secure, scale, and reproduce.
Inside, you'll explore:

• Provisioning GPU-accelerated Kubernetes clusters with Terraform
• Automating EKS, GKE, and AKS environments for AI workloads
• Managing Kubernetes add-ons, storage, networking, and GPU operators
• Deploying MLOps platforms such as Kubeflow through GitOps
• Automating vector databases, Kafka, feature stores, and cloud storage
• Building infrastructure for LLM training, serving, and RAG architectures
• Managing model deployment with ArgoCD, CI/CD, and automated rollbacks
• Applying policy as code, security controls, and AI cost-optimization strategies
• Designing multi-cloud and hybrid GPU infrastructure
• Testing infrastructure, detecting configuration drift, and automating recovery

Whether you're moving away from manual infrastructure or designing a reusable foundation for production AI, this book brings Infrastructure as Code, Kubernetes orchestration, and MLOps together into one practical architectural framework.
Build AI infrastructure that can be provisioned, managed, and scaled with confidence.