Isbn: 9798190029277 - mlops llmops and deployment (ai and ml reference handbooks) (4 resultados)

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  • Idioma: Inglés

    Editorial: Independently published, 2026

    9798190029277

    Serie: Libro 16 de 18 - AI and ML Reference handbooks

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    Librería: PBShop.store UK, Fairford, GLOS, Reino UnidoPBShop.store UK

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    Condición: Nuevo

    EUR 25,55

    Envío por EUR 4,84 
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    Cantidad disponible: Más de 20 disponibles

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

  • Idioma: Inglés

    Editorial: Amazon Digital Services LLC - Kdp Aug 2026, 2026

    9798190029277

    Serie: Libro 16 de 18 - AI and ML Reference handbooks

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    Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH

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    EUR 49,17

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    Taschenbuch. Condición: Neu. Neuware - Master the complete production lifecycle of Machine Learning and Large Language Models-from experimentation to enterprise deployment.MLOps, LLMOps and Deployment is a practical, engineer-focused guide that takes you beyond model training and into the real-world challenges of deploying, monitoring, scaling, and maintaining AI systems in production.Designed for AI Engineers, Machine Learning Engineers, Data Scientists, MLOps Engineers, DevOps Professionals, Cloud Architects, and students preparing for technical interviews, this book explains every major concept using simple language, production examples, architecture diagrams, troubleshooting scenarios, interview questions, and hands-on workflows.Inside this book, you'll learn: - Complete end-to-end MLOps lifecycle- CI, CD, and Continuous Training (CT)- Model versioning and Model Registry- Drift detection and production monitoring- Docker and Kubernetes for ML deployment- Blue-Green, Canary, Rolling, Shadow, and A/B deployments- LLMOps fundamentals, prompt versioning, and RAG operations- Feature Stores and production data pipelines- Observability, alerting, and incident response- AI governance, compliance, and model security- GitOps with ArgoCD, Flux, and Terraform- AWS SageMaker, Azure ML, and Google Vertex AI implementations- GPU inference optimization using TensorRT, Triton, CUDA, and vLLM- Agentic AI workflows, MCP, Guardrails, and Prompt Registries- Production troubleshooting and enterprise readiness checklistsEvery chapter includes: - Simple explanations- Real production challenges- Architecture diagrams- Practical code examples- Interview Q&A- Knowledge checks- Hands-on exercisesWhether you're deploying your first ML model or building enterprise-scale AI platforms, this book provides the practical knowledge needed to confidently design, deploy, monitor, and operate modern AI systems.

  • Idioma: Inglés

    Editorial: Independently published, 2026

    9798190029277

    Serie: Libro 16 de 18 - AI and ML Reference handbooks

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    • Impresión bajo demanda

    Librería: California Books, Miami, FL, Estados Unidos de AmericaCalifornia Books

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    Condición: Nuevo

    EUR 26,03

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    Cantidad disponible: Más de 20 disponibles

    Condición: New. Print on Demand.

  • Idioma: Inglés

    Editorial: Independently Published, 2026

    9798190029277

    Serie: Libro 16 de 18 - AI and ML Reference handbooks

    • Tapa blanda
    • Impresión bajo demanda

    Librería: CitiRetail, Stevenage, Reino UnidoCitiRetail

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    Condición: Nuevo

    EUR 29,35

    Envío por EUR 43,06 
    Se envía de Reino Unido a Estados Unidos de America

    Cantidad disponible: 1 disponibles

    Paperback. Condición: new. Paperback. Master the complete production lifecycle of Machine Learning and Large Language Models-from experimentation to enterprise deployment.MLOps, LLMOps and Deployment is a practical, engineer-focused guide that takes you beyond model training and into the real-world challenges of deploying, monitoring, scaling, and maintaining AI systems in production.Designed for AI Engineers, Machine Learning Engineers, Data Scientists, MLOps Engineers, DevOps Professionals, Cloud Architects, and students preparing for technical interviews, this book explains every major concept using simple language, production examples, architecture diagrams, troubleshooting scenarios, interview questions, and hands-on workflows.Inside this book, you'll learn: Complete end-to-end MLOps lifecycleCI, CD, and Continuous Training (CT)Model versioning and Model RegistryDrift detection and production monitoringDocker and Kubernetes for ML deploymentBlue-Green, Canary, Rolling, Shadow, and A/B deploymentsLLMOps fundamentals, prompt versioning, and RAG operationsFeature Stores and production data pipelinesObservability, alerting, and incident responseAI governance, compliance, and model securityGitOps with ArgoCD, Flux, and TerraformAWS SageMaker, Azure ML, and Google Vertex AI implementationsGPU inference optimization using TensorRT, Triton, CUDA, and vLLMAgentic AI workflows, MCP, Guardrails, and Prompt RegistriesProduction troubleshooting and enterprise readiness checklistsEvery chapter includes: Simple explanationsReal production challengesArchitecture diagramsPractical code examplesInterview Q&AKnowledge checksHands-on exercisesWhether you're deploying your first ML model or building enterprise-scale AI platforms, this book provides the practical knowledge needed to confidently design, deploy, monitor, and operate modern AI systems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.