AutoTrain MLBefore you begin: use a dataset you are allowed to process, keep a holdout that reflects the real prediction setting, and write down the decision the model is intended to support.
1. Choose a completed run
Export only after checking metrics and testing representative new data.
Verify before continuingThe target, row meaning, and prediction timing are explicit.
2. Keep preprocessing attached
A model requires the same feature order, encoders, scalers, and missing-value logic used during training.
Verify before continuingTransformations are learned from training rows only and can be reproduced during testing.
3. Record compatibility
Keep the framework and library versions alongside the artifact, especially for Pickle and Joblib files.
Verify before continuingThe selected metric reflects the cost of errors and is compared on the same holdout.
4. Validate after download
Load the artifact in a clean environment and compare predictions against the AutoTrain testing result before production use.
Verify before continuingRepresentative new inputs produce valid outputs and failures are handled clearly.
Final checklist
- Dataset version and intended use are documented.
- Preprocessing and validation settings match the saved experiment.
- Metrics are interpreted with class balance, error cost, and limitations in mind.
- New-data testing succeeds with the exact production schema.
- A fallback and monitoring plan exist before deployment.
Next step
Open the dashboard to apply this guide to a saved dataset and experiment.
Open dashboard