AI Observability and Tracing in Production: Tools, Patterns, and Best Practices
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Fuera de stock
Fuera de stock
Peso con empaque:
0.33 kg
0.33 kg
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No
No
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Sobre este producto
- 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.
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