AI Observability and Tracing in Production

Name: AI Observability and Tracing in Production
SKU: 4a5dd1d01c308fce0ac33e66ca537c53
Price: 20.99 EUR
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Bol 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 do- Instrument 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.

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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 do- Instrument 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.

Lees meer