Observability
Observability is the ability to understand a system’s state from logs, metrics, traces, artifacts, and alerts. In research systems, observability helps explain why a run was slow, failed, diverged, or produced a suspicious result.
Useful signals can be grouped as:
The goal is to connect symptoms to causes:
Key Ideas
- Training metrics explain learning behavior; system metrics explain resource behavior.
- Logs should identify stages and failures without leaking credentials or private paths.
- Metrics need units, sampling intervals, and labels that can be interpreted later.
- Data-validation failures and deployment events should be measured alongside service metrics.
- Artifacts such as configs, checkpoints, plots, and run summaries are part of observability.
- Alerts should point to actionable thresholds rather than create constant noise.
Practical Checks
- What evidence proves the bottleneck: GPU, CPU, memory, storage, network, scheduler, or code?
- Are logs structured enough to compare runs?
- Are metrics aligned by timestamp with training steps or job events?
- Are model version, validation status, and rollout stage included in the evidence?
- Are failures classified rather than only recorded?
- Is public logging sanitized before it enters notes or dashboards?
- Can job state, logs, and artifacts be reconciled into one final outcome?
- Does each alert imply an owner and an action?