ISBN: 9798266787674 - ai observability and tracing in production: tools, patterns, and best practices (11 risultati)

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Condizione: New.

Condizione: As New. Unread book in perfect condition.

Paperback. Condizione: new. Paperback. AI Observability and Tracing in Production: Tools, Patterns, and Best PracticesWhen your LLM misbehaves, latency spikes, or an agent goes off-script, can you see why in seconds-or do you guess for hours? Teams shipping AI at scale share the same goal: reliable, explainable, cost-efficient systems. The obstacle is visibility.This book shows a practical, end-to-end approach to AI observability and tracing in production. You'll learn how to instrument LLMs, RAG pipelines, and multi-agent workflows; compare model versions safely; measure quality and drift; and investigate incidents with confidence. The guidance is concrete: metrics, logs, traces, spans, sessions, feedback loops, plus real patterns using OpenTelemetry, specialized AI platforms (e.g., Langfuse, Helicone, Vellum, Arize), and APM extensions from Datadog and New Relic.What you'll learn and doInstrument prompts, completions, embeddings, retrievals, and tool calls with the right granularity.Build traceable execution graphs for agents and RAG, with model/version, context lineage, and cost/latency attribution.Select and integrate tooling (self-hosted or SaaS) while meeting privacy, compliance, and audit needs.Design dashboards and alerts that catch drift, anomalies, and regressions-not noise.Run A/B and canary releases with observability comparisons and fast rollback criteria.Reduce spend with token accounting, caching, sampling, and smart retention policies.Investigate production issues methodically: correlate spans, isolate root causes, and write durable runbooks.Apply safety guardrails, policy enforcement, and explainability traces to show "why this output."Prepare CI/CD pipelines so every release ships with tested observability, not hope.Clear, direct, and hands-on, this guide blends SRE rigor with ML realities. It speaks the language of AI monitoring, tracing, RAG observability, drift detection, incident response, and cost optimization-so you can scale LLMs and agents without losing control. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

Paperback. Condizione: New.

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

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PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condizione: new. Paperback. AI Observability and Tracing in Production: Tools, Patterns, and Best PracticesWhen your LLM misbehaves, latency spikes, or an agent goes off-script, can you see why in seconds-or do you guess for hours? Teams shipping AI at scale share the same goal: reliable, explainable, cost-efficient systems. The obstacle is visibility.This book shows a practical, end-to-end approach to AI observability and tracing in production. You'll learn how to instrument LLMs, RAG pipelines, and multi-agent workflows; compare model versions safely; measure quality and drift; and investigate incidents with confidence. The guidance is concrete: metrics, logs, traces, spans, sessions, feedback loops, plus real patterns using OpenTelemetry, specialized AI platforms (e.g., Langfuse, Helicone, Vellum, Arize), and APM extensions from Datadog and New Relic.What you'll learn and doInstrument prompts, completions, embeddings, retrievals, and tool calls with the right granularity.Build traceable execution graphs for agents and RAG, with model/version, context lineage, and cost/latency attribution.Select and integrate tooling (self-hosted or SaaS) while meeting privacy, compliance, and audit needs.Design dashboards and alerts that catch drift, anomalies, and regressions-not noise.Run A/B and canary releases with observability comparisons and fast rollback criteria.Reduce spend with token accounting, caching, sampling, and smart retention policies.Investigate production issues methodically: correlate spans, isolate root causes, and write durable runbooks.Apply safety guardrails, policy enforcement, and explainability traces to show "why this output."Prepare CI/CD pipelines so every release ships with tested observability, not hope.Clear, direct, and hands-on, this guide blends SRE rigor with ML realities. It speaks the language of AI monitoring, tracing, RAG observability, drift detection, incident response, and cost optimization-so you can scale LLMs and agents without losing control. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …