ODSC-36246: fix the wording

Author: z7ye

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

@@ -172,28 +172,6 @@ Here is a more realistic  example using CatBoost model.
     catboost_model.delete_deployment(wait_for_completion=True)
     ModelCatalog(compartment_id=os.environ['NB_SESSION_COMPARTMENT_OCID']).delete_model(model_id)
 
-You can also use the shortcut ``.prepare_save_deploy()`` instead of calling ``.prepare()``, ``.save()`` and ``.deploy()`` seperately.
-
-.. code-block:: python3
-
-    import tempfile
-    from ads.catalog.model import ModelCatalog
-    from ads.model.generic_model import GenericModel
-
-    class Toy:
-        def predict(self, x):
-            return x ** 2
-    estimator = Toy()
-
-    model = GenericModel(estimator=estimator)
-    model.summary_status()
-    # If you are running the code inside a notebook session and using a service pack, `inference_conda_env` can be omitted.
-    model.prepare_save_deploy(inference_conda_env="dataexpl_p37_cpu_v3")
-    model.verify(2)
-    model.predict(2)
-    model.delete_deployment(wait_for_completion=True)
-    ModelCatalog(compartment_id=os.environ['NB_SESSION_COMPARTMENT_OCID']).delete_model(model.model_id)
-
 
 Example -- Save Your Own Model
 ==============================
@@ -244,11 +222,11 @@ The example is illustrated using an AutoMLx model.
     model = GenericModel(est, artifact_dir = "model_artifact_folder", serialize=False)
     model.prepare(inference_conda_env="automlx_p38_cpu_v1",force_overwrite=True, model_file_name="model.pkl", X_sample=X_test)
 
-Now copy the model.pkl file and paste into the ``model_artifact_folder`` folder. And open the score.py in the ``model_artifact_folder`` folder and add implement the ``load_model`` function. You can also edit ``pre_inference`` and ``post_inference`` function. Below is an example implementation of the score.py.
+Now copy the model.pkl file and paste into the ``model_artifact_folder`` folder. And open the score.py in the ``model_artifact_folder`` folder to add implementation of the ``load_model`` function. You can also add your preprocessing steps in ``pre_inference`` function and postprocessing steps in ``post_inference`` function. Below is an example implementation of the score.py.
 Replace your score.py with the code below.
 
 .. code-block:: python3
-    :emphasize-lines: 28, 29, 30, 31, 122
+    :emphasize-lines: 28, 29, 30, 31, 123
 
     # score.py 1.0 generated by ADS 2.8.2 on 20230301_065458
     import os
@@ -396,7 +374,7 @@ Replace your score.py with the code below.
         )
         return {'prediction': yhat}
 
-Save the score.py and now call verify to check if it works locally.
+Save the score.py and now call ``.verify()`` to check if it works locally.
 
 .. code-block:: python3
 
@@ -409,3 +387,25 @@ After verify run successfully, you can save the model to model catalog, deploy a
     model_id = model.save(display_name='Demo AutoMLModel model')
     deploy = model.deploy(display_name='Demo AutoMLModel deployment')
     model.predict(X_test.iloc[:2].to_json())
+
+You can also use the shortcut ``.prepare_save_deploy()`` instead of calling ``.prepare()``, ``.save()`` and ``.deploy()`` seperately.
+
+.. code-block:: python3
+
+    import tempfile
+    from ads.catalog.model import ModelCatalog
+    from ads.model.generic_model import GenericModel
+
+    class Toy:
+        def predict(self, x):
+            return x ** 2
+    estimator = Toy()
+
+    model = GenericModel(estimator=estimator)
+    model.summary_status()
+    # If you are running the code inside a notebook session and using a service pack, `inference_conda_env` can be omitted.
+    model.prepare_save_deploy(inference_conda_env="dataexpl_p37_cpu_v3")
+    model.verify(2)
+    model.predict(2)
+    model.delete_deployment(wait_for_completion=True)
+    ModelCatalog(compartment_id=os.environ['NB_SESSION_COMPARTMENT_OCID']).delete_model(model.model_id)