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. Inspect the schema
Request GET /api/autotrain/jobs/{job_id}/schema to review task, feature names, request format, and output fields.
Verify before continuingThe target, row meaning, and prediction timing are explicit.
GET https://api.autotrain.app/api/autotrain/jobs/JOB_ID/schema
2. Send tabular data
POST JSON to /api/autotrain/jobs/{job_id}/predict using the exact feature columns shown by the schema.
Verify before continuingTransformations are learned from training rows only and can be reproduced during testing.
curl -X POST "https://api.autotrain.app/api/autotrain/jobs/JOB_ID/predict" \
-H "Content-Type: application/json" \
-d '{"data":[{"feature_1":12.5,"feature_2":"A"}]}'3. Send image data
POST multipart form data to /api/autotrain/jobs/{job_id}/predict-image for supported classification, segmentation, or detection models.
Verify before continuingThe selected metric reflects the cost of errors and is compared on the same holdout.
4. Handle failures
Treat 4xx responses as request or schema problems and 5xx responses as engine failures. Use timeouts, structured logging, and bounded retries.
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