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Models and Features

Training a model from a feature service​

AutoML can train directly on a feature service, so you never assemble the feature matrix by hand:

  1. Go to MLOps > AutoML and select New AutoML Job (or Train New Model in the Model Registry header).
  2. For the dataset source, choose Feature Service.
  3. Select the feature store and the feature service to train against.
  4. Upload the entity and label file: a CSV or Parquet frame of entity join keys, an event_timestamp per row, and your target column.
  5. Choose the target column and continue through the wizard as usual (see Create a job).

AutoML resolves point-in-time features for your labels, joins them to the target, and trains on the result. The features it trained on are recorded with the model.

Serving a model with live features​

A deployed model can resolve its features at inference, so callers send only entity ids, for example {"driver_id": 1002}.

A model trained on a feature service (an AutoML job trained from one) is bound automatically: its Features tab shows Bound from training, naming the store and the feature service version it trained on, and each prediction resolves exactly those features from that version. A newer version of the service is used only by a model retrained on it, and a registry version or rollback serves the features its own version was trained on. You can set a Max staleness there: a prediction whose features are older fails instead of being served stale.

Any other model (one you uploaded, for example) is bound on its Features tab: select the feature store and the feature service, turn on Resolve online features at inference, optionally set a maximum staleness, and click Save binding. Remove binding undoes it. Online resolution requires the store to have online serving on.

A value the request carries for a feature is used instead of the resolved one. When the features cannot be resolved, the prediction fails rather than running the model without them: with 422 when the request cannot be served as sent (no entity keys, or an entity the store has no current value for), with the store's own error when it refuses the lookup (for example a missing join key), and with 502 when the store fails or cannot be reached.