MLflow

GigaFlow reads MLflow traces over the MLflow REST API (OSS or Databricks). A bundled transform (mlflow.yml) ships as a starting point.

What you'll need

• MLflow server base URL: http://host:5000 (OSS) or your Databricks workspace URL.
• Auth: a Databricks PAT for Databricks; nothing for anonymous OSS.

Connect

Run the setup wizard. The first time, it signs you in with your waitlist email (same as gigaflow login), then walks you through the connection above:

gigaflow setup

Pick MLflow when prompted and enter your server URL (and a Databricks PAT if needed). The wizard creates a GigaFlow project, applies a transform, registers the datasource, and runs the first sync — you never set an API key or backend URL by hand.

Transform

MLflow spans arrive as OTLP proto-JSON, normalized to nested dicts (ns→ms, base64 ids decoded, mlflow.experiment_id injected). The bundled mlflow.yml is a starting point; if your spans use non-standard attributes.* / name paths, copy it, edit, and give the wizard the path to your edited file when it asks for a transform.

After the first sync

gigaflow sync                                    # re-pull new traces anytime
gigaflow compute "SELECT trace_id FROM trace_metrics WHERE run_id IS NULL"
gigaflow inspect <trace_id>