Databricks MLflow evaluation

Read databricks-core for authentication and workspace discovery. For tracing, trace retrieval, observability, and trace-based debugging, use the separate databricks-mlflow-tracing skill. Verify tracing when the selected evaluation mode or scorers require it; reuse precomputed outputs or existing traces without rerunning the agent.

The guides below are references, not separately installed skills. When a guide names an upstream MLflow skill, use the matching guide here or the tracing skill.

For MLflow API details, consult the existing databricks-docs skill and https://mlflow.org/docs/latest/llms.txt.

For Databricks judge models, prefer databricks:/system.ai.<model> with a mlflow[databricks]>=3.16.0. A 404 commonly indicates an older client that does not understand the system model URI; a 403 on a legacy endpoint name can mean legacy serving endpoints are disabled. Verify the installed client and available system.ai model before changing credentials or permissions.

For a new evaluation, establish a baseline with a fixed dataset and scorers, change one thing, then run the same evaluation again. Use databricks-mlflow-tracing first if traces are missing or a failure needs a trace-grounded diagnosis.