LLM Observability in Production: Monitoring, Tracing, and Evaluating AI Systems (Production AI Engineering Series) - Softcover
Book 2 of 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.
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Paperback. Condition: 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. Seller Inventory # 9798197071774
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Taschenbuch. Condition: 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. Seller Inventory # 9798197071774