vault / mlops / traces.md

Distributed traces

ML platform 55 words 2 outgoing 2 backlinks

A trace follows a request through the services and steps it touches. It lets a slow or failed prediction be inspected as one chain rather than unrelated log lines. In a serving interface, tracing is useful as a shared platform default, while model-specific behaviour still needs its own checks.

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[[ Observability ]] For feedback investigation, record enough of the request to reconstruct what happened: the question, retrieval, response and relevant configuration. A trace helps connect those steps. Dashboards can show a pattern, but a concrete replay is often what tells us which... [[ Model serving ]] A trained model is not yet a service. A shared serving path handles the repeated work around it: authentication, tracing, deployment conventions and orchestration. Keep the supported path narrow and explain failures clearly. The post distinguishes the interface I built...