LLM Observability in Production: Monitoring, Tracing, and Evaluating AI Systems - Couverture souple
Livre 2 sur 19: Production AI Engineering Series
Synopsis
Master LLM Observability: Monitor, Trace, and Evaluate Your AI Systems in Production
As large language models move from research prototypes to business-critical production systems, the ability to observe, understand, and continuously improve their behavior has become a core engineering competency. This comprehensive guide delivers everything you need to build world-class observability for LLM systems—from foundational instrumentation to advanced evaluation automation.
- Instrument LLM pipelines with OpenTelemetry and semantic conventions for vendor-neutral tracing
- Deploy Langfuse for full-stack observability including prompt version management and A/B testing
- Implement RAGAS and DeepEval for automated faithfulness, relevance, and hallucination evaluation
- Monitor multi-agent and agentic workflows with trajectory quality assessment
- Use Arize Phoenix for embedding drift detection and local debugging
- Build evaluation datasets, human feedback loops, and fine-tuning data pipelines
- Design production infrastructure for scalability, security, and compliance
Whether you are an ML engineer building your first production LLM system or a senior architect designing observability infrastructure for a large AI platform, this book provides the practical frameworks, code patterns, and organizational practices that separate high-performing AI teams from those flying blind. Written for working engineers in the AI and software engineering field.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Résultats de recherche pour LLM Observability in Production: Monitoring, Tracing,...
Image d'archives
LLM Observability in Production: Monitoring, Tracing, and Evaluating AI Systems (Production AI Engineering Series)
impression à la demandeVendeur : California Books, Miami, FL, Etats-Unis
Évaluation du vendeur 4 sur 5 étoiles
Etat : New. Print on Demand. N° de réf. du vendeur I-9798197071774
Acheter neuf
EUR 13,51
Livraison gratuite
Expédition nationale : Etats-Unis
Expédition nationale : Etats-Unis
Quantité disponible : Plus de 20 disponibles
Image d'archives
LLM Observability in Production
impression à la demandeVendeur : PBShop.store US, Wood Dale, IL, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. N° de réf. du vendeur L0-9798197071774
Acheter neuf
EUR 13,86
Livraison gratuite
Expédition nationale : Etats-Unis
Expédition nationale : Etats-Unis
Quantité disponible : Plus de 20 disponibles
Image d'archives
LLM Observability in Production
impression à la demandeVendeur : PBShop.store UK, Fairford, GLOS, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
PAP. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. N° de réf. du vendeur L0-9798197071774
Acheter neuf
EUR 13,46
Expédition à EUR 3,84
Expédition depuis Royaume-Uni vers Etats-Unis
Expédition depuis Royaume-Uni vers Etats-Unis
Quantité disponible : Plus de 20 disponibles
Image d'archives
LLM Observability in Production (Paperback)
impression à la demandeVendeur : CitiRetail, Stevenage, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Paperback. Etat : new. Paperback. Master LLM Observability: Monitor, Trace, and Evaluate Your AI Systems in ProductionAs large language models move from research prototypes to business-critical production systems, the ability to observe, understand, and continuously improve their behavior has become a core engineering competency. This comprehensive guide delivers everything you need to build world-class observability for LLM systems-from foundational instrumentation to advanced evaluation automation.Instrument LLM pipelines with OpenTelemetry and semantic conventions for vendor-neutral tracingDeploy Langfuse for full-stack observability including prompt version management and A/B testingImplement RAGAS and DeepEval for automated faithfulness, relevance, and hallucination evaluationMonitor multi-agent and agentic workflows with trajectory quality assessmentUse Arize Phoenix for embedding drift detection and local debuggingBuild evaluation datasets, human feedback loops, and fine-tuning data pipelinesDesign production infrastructure for scalability, security, and complianceWhether you are an ML engineer building your first production LLM system or a senior architect designing observability infrastructure for a large AI platform, this book provides the practical frameworks, code patterns, and organizational practices that separate high-performing AI teams from those flying blind. Written for working engineers in the AI and software engineering field. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9798197071774
Acheter neuf
EUR 16,82
Expédition à EUR 43,20
Expédition depuis Royaume-Uni vers Etats-Unis
Expédition depuis Royaume-Uni vers Etats-Unis
Quantité disponible : 1 disponible(s)
Image fournie par le vendeur
LLM Observability in Production : Monitoring, Tracing, and Evaluating AI Systems
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. Neuware - Master LLM Observability: Monitor, Trace, and Evaluate Your AI Systems in ProductionAs large language models move from research prototypes to business-critical production systems, the ability to observe, understand, and continuously improve their behavior has become a core engineering competency. This comprehensive guide delivers everything you need to build world-class observability for LLM systems-from foundational instrumentation to advanced evaluation automation.- Instrument LLM pipelines with OpenTelemetry and semantic conventions for vendor-neutral tracing- Deploy Langfuse for full-stack observability including prompt version management and A/B testing- Implement RAGAS and DeepEval for automated faithfulness, relevance, and hallucination evaluation- Monitor multi-agent and agentic workflows with trajectory quality assessment- Use Arize Phoenix for embedding drift detection and local debugging- Build evaluation datasets, human feedback loops, and fine-tuning data pipelines- Design production infrastructure for scalability, security, and complianceWhether you are an ML engineer building your first production LLM system or a senior architect designing observability infrastructure for a large AI platform, this book provides the practical frameworks, code patterns, and organizational practices that separate high-performing AI teams from those flying blind. Written for working engineers in the AI and software engineering field. N° de réf. du vendeur 9798197071774
Acheter neuf
EUR 15,33
Expédition à EUR 60,67
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : 2 disponible(s)