Reseña del libro "PRODUCTION AI OBSERVABILITY (en Inglés)"
Production AI systems do not fail like ordinary software. An API can return HTTP 200 while the answer is wrong. A RAG pipeline can be fast while retrieving bad evidence. An AI agent can complete a run while wasting tokens, looping through tools, or failing the actual task.Production AI Observability is a practical engineering guide to monitoring, evaluating, debugging, and optimizing LLM, RAG, and AI agent systems in real production environments.Built around the evolving SignalForge application, this book shows you how to move beyond basic logs and dashboards and build an observability system that explains what happened, why it happened, what changed, and whether the AI output can be trusted. PRODUCTION AI OBSERVABILITYYou will learn how to: Instrument AI applications with OpenTelemetry, distributed tracing, metrics, and structured logsMonitor LLM latency, token usage, model behavior, errors, retries, and costMeasure RAG retrieval quality, Recall@K, ranking quality, groundedness, and faithfulnessDiagnose missing documents, bad chunking, embedding mismatch, stale knowledge, reranking failures, and context truncationTrace AI agents and tool calls from planning through task completionDetect agent loops, runaway token usage, quality regressions, and model or retrieval driftBuild continuous LLM evaluation and regression-testing pipelinesConnect prompts, models, datasets, embeddings, retrievers, and deployments through version lineageDesign production dashboards, alerts, SLIs, SLOs, error budgets, and incident-response workflowsProtect sensitive AI telemetry with practical privacy, security, redaction, and retention controlsThe book is designed for AI engineers, Python developers, ML engineers, backend developers, platform engineers, SREs, DevOps professionals, and developers building production RAG or agentic AI applications. PRODUCTION AI OBSERVABILITYEvery major capability is reinforced through realistic failure injection, trace analysis, evaluations, production gates, and debugging exercises. All core commands, code, configuration, tests, Docker assets, OpenTelemetry setup, dashboards, and evaluation examples are included-no external GitHub repository required. PRODUCTION AI OBSERVABILITY PRODUCTION AI OBSERVABILITYIf you want to build AI systems that are not only deployed, but observable, measurable, diagnosable, and production-ready, this book gives you the engineering workflow to do it.
