update intro

Signed-off-by: Eric Liang <ekhliang@gmail.com>

doc/source/data/data.rst

@@ -8,16 +8,14 @@ Ray Data: Scalable Datasets for ML
 
 .. _data-intro:
 
-Ray Data scales common ML data processing patterns that arise in batch inference
-and distributed training applications. These problems occur when it becomes necessary to
-combine data preprocessing and model computations in the same job. Ray Data does this by providing
+Ray Data scales common ML data processing patterns in batch inference
+and distributed training applications. Ray Data does this by providing
 streaming distributed transformations
 such as maps (:meth:`map_batches <ray.data.Dataset.map_batches>`),
 global and grouped aggregations (:class:`GroupedData <ray.data.grouped_data.GroupedData>`), and
 shuffling operations (:meth:`random_shuffle <ray.data.Dataset.random_shuffle>`,
 :meth:`sort <ray.data.Dataset.sort>`,
-:meth:`repartition <ray.data.Dataset.repartition>`),
-and is compatible with a variety of file formats, data sources, and distributed frameworks.
+:meth:`repartition <ray.data.Dataset.repartition>`).
 
 Read on for an overview of the main use cases and operations supported by Ray Data.