Mark datasets as still in alpha for now (ray-project#18321)

README.rst

@@ -23,7 +23,7 @@ Ray is packaged with the following libraries for accelerating machine learning w
 - `RLlib`_: Scalable Reinforcement Learning
 - `RaySGD <https://docs.ray.io/en/master/raysgd/raysgd.html>`__: Distributed Training Wrappers
 - `Serve`_: Scalable and Programmable Serving
-- `Datasets`_: Flexible Distributed Data Loading (beta)
+- `Datasets`_: Flexible Distributed Data Loading (alpha)
 - `Workflows`_: Fast, Durable Application Flows (alpha)
 
 There are also many `community integrations <https://docs.ray.io/en/master/ray-libraries.html>`_ with Ray, including `Dask`_, `MARS`_, `Modin`_, `Horovod`_, `Hugging Face`_, `Scikit-learn`_, and others. Check out the `full list of Ray distributed libraries here <https://docs.ray.io/en/master/ray-libraries.html>`_.

doc/source/data/dataset.rst

@@ -5,7 +5,7 @@ Datasets: Flexible Distributed Data Loading
 
 .. tip::
 
- Datasets is available as **beta** in Ray 1.7+ (alpha release available in Ray 1.6). Please file feature requests and bug reports on GitHub Issues or join the discussion on the `Ray Slack <https://forms.gle/9TSdDYUgxYs8SA9e8>`__.
+ Datasets is available as **alpha** in Ray 1.7+ (experimental release available in Ray 1.6). Please file feature requests and bug reports on GitHub Issues or join the discussion on the `Ray Slack <https://forms.gle/9TSdDYUgxYs8SA9e8>`__.
 
 Ray Datasets are the standard way to load and exchange data in Ray libraries and applications. Datasets provide basic distributed data transformations such as ``map``, ``filter``, and ``repartition``, and are compatible with a variety of file formats, datasources, and distributed frameworks.
 

doc/source/ray-libraries.rst

@@ -11,7 +11,7 @@ Ray also comes packaged with several libraries solving problems in the machine l
 - :ref:`rllib-index`
 - :ref:`sgd-index`
 - :ref:`rayserve`
-- :ref:`datasets` (beta)
+- :ref:`datasets` (alpha)
 - :ref:`workflows` (alpha)
 
 Airflow |airflow|