Databricks Mlflow Tracing skill

Databricks MLflow Tracing

Use this skill for MLflow observability and tracing workflows on Databricks. Read databricks-core first for CLI authentication, profile selection, and workspace discovery. On Databricks, reuse the existing experiment ID or choose an absolute workspace path such as /Users/<email>/my-experiment or /Shared/my-experiment; relative names in the bundled onboarding examples are for non-Databricks tracking servers.

Read the matching guide directly:

Request	Read this guide
Choose a use case or get started with MLflow	MLflow onboarding
Instrument an app or agent and verify tracing	MLflow tracing
Retrieve traces by ID, time, status, or metadata	Retrieve traces
Inspect one trace or explain an agent failure	Analyze a trace
Reconstruct or debug a multi-turn chat session	Analyze a chat session
Query token, latency, or quality metrics on a supported OSS MLflow server	Query MLflow metrics

For agent evaluation, scorer creation, or implementing a fix after diagnosis, use databricks-mlflow-evaluation.

For work that spans multiple guides, use this order:

1. Determine the use case with MLflow onboarding.
2. Add or verify tracing with MLflow tracing.
3. Retrieve traces before trace or chat-session analysis.
4. Use Query MLflow metrics for aggregate trends.

For Databricks trace storage, read the tracing guide's Databricks reference before claiming that traces use Unity Catalog storage.

For a Unity Catalog trace location, verify the deployed identity rather than a local user. It needs experiment access, CAN USE on the SQL warehouse, USE CATALOG and USE SCHEMA, and explicit MODIFY, SELECT on each generated spans, logs, metrics, and annotations table. ALL PRIVILEGES alone does not satisfy those table grants. Confirm persistence by reading back a fresh trace written under the deployed identity.