Stormveld Erik (23 results)

Taschenbuch. Condition: Neu. Neuware - 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 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.…

Taschenbuch. Condition: Neu. Neuware - What happens when AI experimentation needs to become reliable enterprise infrastructure Building an LLM application is one challenge. Building the platform that allows AI teams to deploy, scale, secure, observe, and continuously improve those applications is another.Platform Engineering for Artificial Intelligence: Mastering Kubernetes Orchestration for Natural Language Processing with LLM explores the architecture behind modern enterprise AI platforms, bringing together Kubernetes, large language models, RAG, vector databases, GPUs, agentic workflows, and LLMOps.Designed for engineers and architects working at the intersection of cloud infrastructure and AI, this book explores how to create standardized, scalable foundations for production LLM workloads.Inside, you'll explore: - Designing internal AI platforms and developer-friendly 'golden paths'- Deploying and serving LLMs with vLLM and TGI on Kubernetes- Scaling vector databases and persistent embedding infrastructure- Building and operating RAG pipelines with event-driven workloads- Orchestrating MCP gateways, tools, and multi-agent systems- Running LoRA, QLoRA, and distributed fine-tuning workflows- Managing LLM gateways, token budgets, caching, and model routing- Implementing security, guardrails, RBAC, PII protection, and compliance- Establishing observability and LLMOps across the model lifecycleIf you're ready to move beyond isolated AI prototypes and understand the infrastructure required to support scalable, governed, production-ready enterprise AI, this book provides a practical architectural foundation for the journey.Build the platform your AI teams can depend on.…

Taschenbuch. Condition: Neu. Neuware - AI workloads are no longer confined to a single server or development environment. Building them for production means coordinating containers, GPUs, data pipelines, APIs, and distributed services without sacrificing reliability.Cloud-Native AI Infrastructure: Kubernetes Orchestration for Distributed Deep Learning and Node.js Backends explores how to design and operate cloud-native infrastructure for modern AI applications using Kubernetes, Node.js, microservices, and distributed deep learning architectures.Rather than treating the AI model as an isolated component, this book examines the complete infrastructure surrounding it-from API gateways and asynchronous workflows to model-serving services, event-driven pipelines, security, observability, and scaling.Inside, you'll explore: - Designing resilient Node.js AI backends and microservices- Deploying deep learning models as Kubernetes services- Connecting Node.js applications with Python-based inference workloads through gRPC- Building event-driven AI workflows with Kafka, RabbitMQ, and NATS- Managing distributed preprocessing, caching, and high-volume data flows- Supporting multi-model deployments, A/B testing, and shadow inference- Extending Kubernetes to edge environments with K3s- Securing AI workloads with zero-trust architecture, mTLS, and network policies- Tracing requests from Node.js gateways through GPU inference- Monitoring and scaling distributed AI infrastructureFor software engineers, AI engineers, platform engineers, and architects working with Kubernetes, Node.js, machine learning, and cloud-native AI, this book provides an architectural perspective on connecting these technologies into a cohesive production environment.Build AI infrastructure that can scale beyond the prototype. …

Paperback. Condition: new. Paperback. What happens when AI experimentation needs to become reliable enterprise infrastructure? Building an LLM application is one challenge. Building the platform that allows AI teams to deploy, scale, secure, observe, and continuously improve those applications is another.Platform Engineering for Artificial Intelligence: Mastering Kubernetes Orchestration for Natural Language Processing with LLM explores the architecture behind modern enterprise AI platforms, bringing together Kubernetes, large language models, RAG, vector databases, GPUs, agentic workflows, and LLMOps.Designed for engineers and architects working at the intersection of cloud infrastructure and AI, this book explores how to create standardized, scalable foundations for production LLM workloads.Inside, you'll explore: Designing internal AI platforms and developer-friendly "golden paths"Deploying and serving LLMs with vLLM and TGI on KubernetesScaling vector databases and persistent embedding infrastructureBuilding and operating RAG pipelines with event-driven workloadsOrchestrating MCP gateways, tools, and multi-agent systemsRunning LoRA, QLoRA, and distributed fine-tuning workflowsManaging LLM gateways, token budgets, caching, and model routingImplementing security, guardrails, RBAC, PII protection, and complianceEstablishing observability and LLMOps across the model lifecycleIf you're ready to move beyond isolated AI prototypes and understand the infrastructure required to support scalable, governed, production-ready enterprise AI, this book provides a practical architectural foundation for the journey.Build the platform your AI teams can depend on. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condition: New. Print on Demand.

Paperback. Condition: new. Paperback. AI workloads are no longer confined to a single server or development environment. Building them for production means coordinating containers, GPUs, data pipelines, APIs, and distributed services without sacrificing reliability.Cloud-Native AI Infrastructure: Kubernetes Orchestration for Distributed Deep Learning and Node.js Backends explores how to design and operate cloud-native infrastructure for modern AI applications using Kubernetes, Node.js, microservices, and distributed deep learning architectures.Rather than treating the AI model as an isolated component, this book examines the complete infrastructure surrounding it-from API gateways and asynchronous workflows to model-serving services, event-driven pipelines, security, observability, and scaling.Inside, you'll explore: Designing resilient Node.js AI backends and microservicesDeploying deep learning models as Kubernetes servicesConnecting Node.js applications with Python-based inference workloads through gRPCBuilding event-driven AI workflows with Kafka, RabbitMQ, and NATSManaging distributed preprocessing, caching, and high-volume data flowsSupporting multi-model deployments, A/B testing, and shadow inferenceExtending Kubernetes to edge environments with K3sSecuring AI workloads with zero-trust architecture, mTLS, and network policiesTracing requests from Node.js gateways through GPU inferenceMonitoring and scaling distributed AI infrastructureFor software engineers, AI engineers, platform engineers, and architects working with Kubernetes, Node.js, machine learning, and cloud-native AI, this book provides an architectural perspective on connecting these technologies into a cohesive production environment.Build AI infrastructure that can scale beyond the prototype. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

Condition: New. Print on Demand.

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

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.…

Condition: New. Print on Demand.

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condition: new. Paperback. In the rapidly evolving SaaS ecosystem, seamless integration of artificial intelligence has become a core competitive advantage for delivering intelligent, context-aware, and highly scalable applications. Artificial Intelligence Integration in Application Stacks delivers the exact implementation methods used by professional developers and engineering teams to embed large language models and vector databases directly into production application architectures. This professional guide moves far beyond introductory concepts. It examines battle-tested architectural patterns, advanced retrieval pipelines, inference optimization strategies, security controls, observability frameworks, and multi-tenant scaling techniques drawn from real-world high-growth SaaS platforms. Every chapter provides precise, production-grade solutions for achieving reliable performance, cost efficiency, and maintainability when working with LLMs and vector stores in complex, distributed environments. Written exclusively for experienced fullstack developers, backend engineers, AI architects, and technical leads who already ship production SaaS applications, this book equips you with the actionable knowledge required to integrate large language models and vector databases without sacrificing scalability, security, or developer velocity. Ready to move beyond basic AI prototypes and implement the same enterprise-grade integration methods used by top-tier SaaS engineering teams? Order your copy today and transform your application stacks with production-ready AI capabilities powered by large language models and vector databases. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condition: New. Print on Demand.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condition: new. Paperback. What happens when AI experimentation needs to become reliable enterprise infrastructure? Building an LLM application is one challenge. Building the platform that allows AI teams to deploy, scale, secure, observe, and continuously improve those applications is another.Platform Engineering for Artificial Intelligence: Mastering Kubernetes Orchestration for Natural Language Processing with LLM explores the architecture behind modern enterprise AI platforms, bringing together Kubernetes, large language models, RAG, vector databases, GPUs, agentic workflows, and LLMOps.Designed for engineers and architects working at the intersection of cloud infrastructure and AI, this book explores how to create standardized, scalable foundations for production LLM workloads.Inside, you'll explore: Designing internal AI platforms and developer-friendly "golden paths"Deploying and serving LLMs with vLLM and TGI on KubernetesScaling vector databases and persistent embedding infrastructureBuilding and operating RAG pipelines with event-driven workloadsOrchestrating MCP gateways, tools, and multi-agent systemsRunning LoRA, QLoRA, and distributed fine-tuning workflowsManaging LLM gateways, token budgets, caching, and model routingImplementing security, guardrails, RBAC, PII protection, and complianceEstablishing observability and LLMOps across the model lifecycleIf you're ready to move beyond isolated AI prototypes and understand the infrastructure required to support scalable, governed, production-ready enterprise AI, this book provides a practical architectural foundation for the journey.Build the platform your AI teams can depend on. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condition: new. Paperback. AI workloads are no longer confined to a single server or development environment. Building them for production means coordinating containers, GPUs, data pipelines, APIs, and distributed services without sacrificing reliability.Cloud-Native AI Infrastructure: Kubernetes Orchestration for Distributed Deep Learning and Node.js Backends explores how to design and operate cloud-native infrastructure for modern AI applications using Kubernetes, Node.js, microservices, and distributed deep learning architectures.Rather than treating the AI model as an isolated component, this book examines the complete infrastructure surrounding it-from API gateways and asynchronous workflows to model-serving services, event-driven pipelines, security, observability, and scaling.Inside, you'll explore: Designing resilient Node.js AI backends and microservicesDeploying deep learning models as Kubernetes servicesConnecting Node.js applications with Python-based inference workloads through gRPCBuilding event-driven AI workflows with Kafka, RabbitMQ, and NATSManaging distributed preprocessing, caching, and high-volume data flowsSupporting multi-model deployments, A/B testing, and shadow inferenceExtending Kubernetes to edge environments with K3sSecuring AI workloads with zero-trust architecture, mTLS, and network policiesTracing requests from Node.js gateways through GPU inferenceMonitoring and scaling distributed AI infrastructureFor software engineers, AI engineers, platform engineers, and architects working with Kubernetes, Node.js, machine learning, and cloud-native AI, this book provides an architectural perspective on connecting these technologies into a cohesive production environment.Build AI infrastructure that can scale beyond the prototype. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, 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.…

Paperback. Condition: new. Paperback. In the rapidly evolving SaaS ecosystem, seamless integration of artificial intelligence has become a core competitive advantage for delivering intelligent, context-aware, and highly scalable applications. Artificial Intelligence Integration in Application Stacks delivers the exact implementation methods used by professional developers and engineering teams to embed large language models and vector databases directly into production application architectures. This professional guide moves far beyond introductory concepts. It examines battle-tested architectural patterns, advanced retrieval pipelines, inference optimization strategies, security controls, observability frameworks, and multi-tenant scaling techniques drawn from real-world high-growth SaaS platforms. Every chapter provides precise, production-grade solutions for achieving reliable performance, cost efficiency, and maintainability when working with LLMs and vector stores in complex, distributed environments. Written exclusively for experienced fullstack developers, backend engineers, AI architects, and technical leads who already ship production SaaS applications, this book equips you with the actionable knowledge required to integrate large language models and vector databases without sacrificing scalability, security, or developer velocity. Ready to move beyond basic AI prototypes and implement the same enterprise-grade integration methods used by top-tier SaaS engineering teams? Order your copy today and transform your application stacks with production-ready AI capabilities powered by large language models and vector databases. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…