Build an ML model

Your data needs gold for ML with status ready. A sample needs only a signed-in account.

Open the wizard

1. Data Intelligence → Machine Learning (/ml).
2. Click New model.

Train from gold (your data)

1. Step Data source → under Published dataset (Gold), select your artifact.
2. Wait for schema load (target column pre-filled from gold metadata).
3. Step Target → confirm column to predict.
4. Step Train → name model → optional Advanced (search effort) → Train.
5. Training queue tab → open the model when status is complete.

The wizard loads the gold artifact. It does not accept a CSV. Gold already passed the 100-row and target checks.

Train from sample (explore only)

1. Step Data source → scroll below gold picker → Sample datasets.
2. Housing (regression, MEDV) or Iris (classification, Species).
3. Confirm target → name → Train.

After training

1. Open model at /ml/{id}.
2. Deploy → Generate API key.
3. Monitor on model page and Usage.

Troubleshooting

Symptom	Fix
Dataset missing from gold list	Data Warehouse → Lake → Gold: ML publish, status ready
Direct uploads are disabled	Use Lake → gold; only samples bypass Lake
Training failed	Check target column; republish gold if schema changed
Used zip ingest?	Landed in bronze: still need gold publish

Next steps

• How training works
• Deployment
• API integration guide
• User journeys

A wrong target fails training or produces useless metrics. Change it on the gold artifact and publish again.