:ref:`Ray Datasets <datasets>` are the standard way to load and exchange data in Ray AIR. In AIR, Datasets are used extensively for data loading, preprocessing, and batch inference.
Preprocessors are primitives that can be used to transform input data into features.
A preprocessor can be fitted during Training, and applied at runtime in both Training and Serving on data batches in the same way. AIR comes with a collection of built-in preprocessors, and you can also define your own with simple templates.
Trainers are wrapper classes around third-party training frameworks like XGBoost and Pytorch. They are built to help integrate with core Ray actors (for distribution), Ray Tune, and Ray Datasets.
See the documentation on :ref:`Trainers <air-trainer-ref>`.
Trainer objects will produce a :ref:`Result <air-results-ref>` object after calling ``.fit()``. These objects will contain training metrics as long as checkpoints to retrieve the best model.
