Lanza Ezequiel (15 results)

Paperback or Softback. Condition: New. The Ultimate AI Guide for Linux Engineers: A Hands-On Guide to Agentic AI, LLMs, and Cloud-Native Automation for Linux Infrastructure Teams. Book.

Condition: New.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condition: New. Apply AI to Linux environments with practical guidance on automation, LLM integration, and RAG workflows. Learn to deploy and scale intelligent systems using Docker, Kubernetes, and modern cloud-native architectures.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Condition: New.

Paperback. Condition: New. Apply AI to Linux environments with practical guidance on automation, LLM integration, and RAG workflows. Learn to deploy and scale intelligent systems using Docker, Kubernetes, and modern cloud-native architectures.

Paperback. Condition: new. Paperback. Learn how to integrate AI into Linux environments with real-world automation, observability, and scalable deployment techniques for modern infrastructure teamsKey FeaturesApply AI to Linux, from core concepts to production-ready deployments at scaleBuild intelligent automation using LLMs, RAG, and AI agents for monitoring, troubleshooting, and system administrationDeploy secure, scalable AI workloads with Docker, Kubernetes, and cloud-native best practicesBook DescriptionUnlock the power of artificial intelligence to transform Linux infrastructure and operations.The Ultimate AI Guide for Linux Engineers is a practical, hands-on handbook for applying AI to real-world Linux systems. You will demystify AI, machine learning, and large language models (LLMs) in practice, prepare AI-ready Linux environments for CPU and GPU workloads, and work with containers and essential open-source frameworks such as PyTorch, Hugging Face Transformers, LangChain, and OpenVINO.Moving into real operational use cases, you will build AI agents and agentic workflows to automate system administration, integrate LLMs into monitoring and troubleshooting pipelines, and apply Retrieval-Augmented Generation (RAG) to query logs, documentation, and internal knowledge bases. You will also enhance observability and incident response with intelligent automation.Finally, you will learn how to deploy and scale AI services using Docker, Kubernetes, and cloud-native architectures, implement security and privacy guardrails, and design reliable AI-driven workflows for enterprise Linux environments.By the end, you will have a practical framework to integrate AI into Linux workflows securely and at scale.What you will learnOptimize Linux kernels and GPUs for AI workloadsOrchestrate LLM pipelines across distributed systemsDesign agentic workflows for autonomous operationsImplement RAG over logs and internal knowledge graphsEmbed AI into observability and incident triageDeploy scalable AI microservices on KubernetesEnforce security, isolation, and model guardrailsWho this book is forThis book is for Linux engineers, system administrators, DevOps professionals, SREs, and platform engineers who want to integrate AI into real-world infrastructure and operations. Prior hands-on experience with Linux, the command line, and basic system administration is expected. Some familiarity with containers (Docker), Kubernetes, and scripting (Bash or Python) would be helpful. Prior AI or machine learning knowledge is beneficial but not required, as core concepts are explained in practical Linux terms. Apply AI to Linux environments with practical guidance on automation, LLM integration, and RAG workflows. Learn to deploy and scale intelligent systems using Docker, Kubernetes, and modern cloud-native architectures. 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.

Condition: New. PRINT ON DEMAND.

Paperback. Condition: new. Paperback. Learn how to integrate AI into Linux environments with real-world automation, observability, and scalable deployment techniques for modern infrastructure teamsKey FeaturesApply AI to Linux, from core concepts to production-ready deployments at scaleBuild intelligent automation using LLMs, RAG, and AI agents for monitoring, troubleshooting, and system administrationDeploy secure, scalable AI workloads with Docker, Kubernetes, and cloud-native best practicesBook DescriptionUnlock the power of artificial intelligence to transform Linux infrastructure and operations.The Ultimate AI Guide for Linux Engineers is a practical, hands-on handbook for applying AI to real-world Linux systems. You will demystify AI, machine learning, and large language models (LLMs) in practice, prepare AI-ready Linux environments for CPU and GPU workloads, and work with containers and essential open-source frameworks such as PyTorch, Hugging Face Transformers, LangChain, and OpenVINO.Moving into real operational use cases, you will build AI agents and agentic workflows to automate system administration, integrate LLMs into monitoring and troubleshooting pipelines, and apply Retrieval-Augmented Generation (RAG) to query logs, documentation, and internal knowledge bases. You will also enhance observability and incident response with intelligent automation.Finally, you will learn how to deploy and scale AI services using Docker, Kubernetes, and cloud-native architectures, implement security and privacy guardrails, and design reliable AI-driven workflows for enterprise Linux environments.By the end, you will have a practical framework to integrate AI into Linux workflows securely and at scale.What you will learnOptimize Linux kernels and GPUs for AI workloadsOrchestrate LLM pipelines across distributed systemsDesign agentic workflows for autonomous operationsImplement RAG over logs and internal knowledge graphsEmbed AI into observability and incident triageDeploy scalable AI microservices on KubernetesEnforce security, isolation, and model guardrailsWho this book is forThis book is for Linux engineers, system administrators, DevOps professionals, SREs, and platform engineers who want to integrate AI into real-world infrastructure and operations. Prior hands-on experience with Linux, the command line, and basic system administration is expected. Some familiarity with containers (Docker), Kubernetes, and scripting (Bash or Python) would be helpful. Prior AI or machine learning knowledge is beneficial but not required, as core concepts are explained in practical Linux terms. Apply AI to Linux environments with practical guidance on automation, LLM integration, and RAG workflows. Learn to deploy and scale intelligent systems using Docker, Kubernetes, and modern cloud-native architectures. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Paperback. Condition: new. Paperback. Learn how to integrate AI into Linux environments with real-world automation, observability, and scalable deployment techniques for modern infrastructure teamsKey FeaturesApply AI to Linux, from core concepts to production-ready deployments at scaleBuild intelligent automation using LLMs, RAG, and AI agents for monitoring, troubleshooting, and system administrationDeploy secure, scalable AI workloads with Docker, Kubernetes, and cloud-native best practicesBook DescriptionUnlock the power of artificial intelligence to transform Linux infrastructure and operations.The Ultimate AI Guide for Linux Engineers is a practical, hands-on handbook for applying AI to real-world Linux systems. You will demystify AI, machine learning, and large language models (LLMs) in practice, prepare AI-ready Linux environments for CPU and GPU workloads, and work with containers and essential open-source frameworks such as PyTorch, Hugging Face Transformers, LangChain, and OpenVINO.Moving into real operational use cases, you will build AI agents and agentic workflows to automate system administration, integrate LLMs into monitoring and troubleshooting pipelines, and apply Retrieval-Augmented Generation (RAG) to query logs, documentation, and internal knowledge bases. You will also enhance observability and incident response with intelligent automation.Finally, you will learn how to deploy and scale AI services using Docker, Kubernetes, and cloud-native architectures, implement security and privacy guardrails, and design reliable AI-driven workflows for enterprise Linux environments.By the end, you will have a practical framework to integrate AI into Linux workflows securely and at scale.What you will learnOptimize Linux kernels and GPUs for AI workloadsOrchestrate LLM pipelines across distributed systemsDesign agentic workflows for autonomous operationsImplement RAG over logs and internal knowledge graphsEmbed AI into observability and incident triageDeploy scalable AI microservices on KubernetesEnforce security, isolation, and model guardrailsWho this book is forThis book is for Linux engineers, system administrators, DevOps professionals, SREs, and platform engineers who want to integrate AI into real-world infrastructure and operations. Prior hands-on experience with Linux, the command line, and basic system administration is expected. Some familiarity with containers (Docker), Kubernetes, and scripting (Bash or Python) would be helpful. Prior AI or machine learning knowledge is beneficial but not required, as core concepts are explained in practical Linux terms. Apply AI to Linux environments with practical guidance on automation, LLM integration, and RAG workflows. Learn to deploy and scale intelligent systems using Docker, Kubernetes, and modern cloud-native architectures. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Apply AI to Linux environments with practical guidance on automation, LLM integration, and RAG workflows. Learn to deploy and scale intelligent systems using Docker, Kubernetes, and modern cloud-native architectures.…

Taschenbuch. Condition: Neu. The Ultimate AI Guide for Linux Engineers | A Hands-On Guide to Agentic AI, LLMs, and Cloud-Native Automation for Linux Infrastructure Teams | Ezequiel Lanza (u. a.) | Taschenbuch | Englisch | 2026 | Packt Publishing | EAN 9781806664238 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…