resolve comments

Author: z7ye

docs/source/user_guide/model_registration/frameworks/automlmodel.rst

@@ -5,8 +5,7 @@ AutoMLModel
 
 .. note::
 
-The ``ads.model.framework.automl_model.AutoMLModel`` class is deprecated. See this :ref:`link <Oralce_AutoMlx>` for more detailed information.
-
+Working with AutoML has moved from within ADS to working directly with the automlx library.
 To deploy an AutoMlx model, use `GenericModel <../../../ads.model.html#ads.model.generic_model.GenericModel>`__ class.
 
 The following example take your trained ``AutoML`` model using ``GenericModel`` and deploy it into production with a few lines of code.

docs/source/user_guide/model_training/automl/index.rst

@@ -3,12 +3,14 @@ Oracle AutoMLx
 ==============
 
 .. note::
-    The ADS `AutoML <https://accelerated-data-science.readthedocs.io/en/v2.8.1/ads.automl.html#module-ads.automl.driver>`__, `AutoMLProvider <https://accelerated-data-science.readthedocs.io/en/v2.8.1/ads.automl.html#ads.automl.provider.AutoMLProvider>`__, `MLXGlobalExplainer <https://accelerated-data-science.readthedocs.io/en/v2.8.1/ads.explanations.html>`__, `MLXLocalExplainer <https://accelerated-data-science.readthedocs.io/en/v2.8.1/ads.explanations.html>`__ and `MLXWhatIfExplainer <https://accelerated-data-science.readthedocs.io/en/v2.8.1/ads.explanations.html>`__, `AutoMLModel <https://accelerated-data-science.readthedocs.io/en/v2.8.1/ads.explanations.html>`__, `AutoMLExtractor <https://accelerated-data-science.readthedocs.io/en/v2.8.1/ads.explanations.html>`__ are deprecated in 2.8.2 and will be removed in 3.0.0. This is to in favor of working directly with Oracle AutoMLx library.
+    Working with AutoML has moved from within ADS to working directly with the AutoMLx library. 
     With AutoMLx moving into the open source domain, working directly with the library has many benefits.
     One of these is that ADS and AutoMLx can now have independent version cadences. 
     AutoMLx is under active development, and will be expanding into areas of explainability, fairness and deep learning. 
     To offer this functionality, the best way is for the ADS to work together as separate libraries.
-    The AutoMlx library can be found in `automlx_p38_cpu_v#` conda pack. The documentation can be found at this `link <https://docs.oracle.com/en-us/iaas/tools/automlx/latest/html/multiversion/v23.1.1/index.html>`__. Notebook examples can be found at this `link <https://github.com/oracle-samples/automlx>`__.
+    AutoMLx are preinstalled in conda pack automlx_p38_cpu_v2 and later, and can now be updated independently of ADS. AutoMLx documentation may be found at `this link <https://docs.oracle.com/en-us/iaas/tools/automlx/latest/html/multiversion/v23.1.1/index.html>`__. 
+    Notebook examples are in `Oracle's samples repository <https://github.com/oracle-samples/oci-data-science-ai-samples/tree/master/notebook_examples`__ and a migration tutorial can be found at :doc:`../../../user_guide/model_training/automl/quick_start.html>`__.
+    
 
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