Query MLflow metrics

Reuse server, experiment IDs and requested window. Select the exact metric, view, aggregation and grouping needed; token counts are not currency cost.

Prerequisites

For aggregate queries, read the metrics API reference. If the task also needs individual traces, read trace retrieval before searching; if unavailable, use the search guide.

Step 1: Check the client

Resolve scripts/fetch_metrics.py relative to this skill; use --help for flags. The metrics endpoint and helper are not supported for Databricks-managed tracking. For OSS they accept HTTP(S), not databricks:// profiles, and add no authentication headers. Use an existing supported authenticated client or report the blocker; never convert a Databricks profile to a workspace URL or request pasted tokens.

Step 2: Choose metrics

Select TRACES (default), SPANS or ASSESSMENTS using the API reference. Use COUNT for entities, SUM for tokens, AVG/P95 for distributions. Do not silently substitute metrics.

Step 3: Query the time window

Query explicit bounds: epoch milliseconds, timezone-qualified ISO timestamps, now, or relative values like -24h. Buckets are seconds and require both bounds.