Isbn: 9798172037726 - Infrastructure as Code for Ai: Engineering Kubernetes Orchestration for Deep Learning Workloads (the Cloud-native Ai Orchestration Series) (3 results)

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Paperback. Condition: new. Paperback. 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 TerraformAutomating EKS, GKE, and AKS environments for AI workloadsManaging Kubernetes add-ons, storage, networking, and GPU operatorsDeploying MLOps platforms such as Kubeflow through GitOpsAutomating vector databases, Kafka, feature stores, and cloud storageBuilding infrastructure for LLM training, serving, and RAG architecturesManaging model deployment with ArgoCD, CI/CD, and automated rollbacksApplying policy as code, security controls, and AI cost-optimization strategiesDesigning multi-cloud and hybrid GPU infrastructureTesting infrastructure, detecting configuration drift, and automating recoveryWhether 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condition: new. Paperback. 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 TerraformAutomating EKS, GKE, and AKS environments for AI workloadsManaging Kubernetes add-ons, storage, networking, and GPU operatorsDeploying MLOps platforms such as Kubeflow through GitOpsAutomating vector databases, Kafka, feature stores, and cloud storageBuilding infrastructure for LLM training, serving, and RAG architecturesManaging model deployment with ArgoCD, CI/CD, and automated rollbacksApplying policy as code, security controls, and AI cost-optimization strategiesDesigning multi-cloud and hybrid GPU infrastructureTesting infrastructure, detecting configuration drift, and automating recoveryWhether 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…