Merge pull request #93504 from nibaccam/aniththa-p13

Automl | Concept update for featurization

Author: PRMerger17

articles/machine-learning/service/concept-automated-ml.md

@@ -93,9 +93,7 @@ In every automated machine learning experiment, your data is automatically scale
 
 Additional advanced preprocessing and featurization are also available, such as missing values imputation, encoding, and transforms. [Learn more about what featurization is included](how-to-create-portal-experiments.md#preprocess). Enable this setting with:
 
-+ Azure portal: Selecting the **Preprocess** checkbox in the **Advanced settings** [with these steps](how-to-create-portal-experiments.md).
-
-+ Python SDK: Specifying `"preprocess": True` for the [`AutoMLConfig` class](https://docs.microsoft.com/python/api/azureml-train-automl/azureml.train.automl.automlconfig?view=azure-ml-py).
++ Python SDK: Specifying `"feauturization": auto' / 'off' / FeaturizationConfig` for the [`AutoMLConfig` class](https://docs.microsoft.com/python/api/azureml-train-automl/azureml.train.automl.automlconfig?view=azure-ml-py).
 
 
 ## Time-series forecasting

articles/machine-learning/service/how-to-set-up-training-targets.md

@@ -3,8 +3,8 @@ title: Create and use compute targets for model training
 titleSuffix: Azure Machine Learning
 description: Configure the training environments (compute targets) for machine learning model training. You can easily switch between training environments. Start training locally. If you need to scale out, switch to a cloud-based compute target.
 services: machine-learning
-author: heatherbshapiro
-ms.author: hshapiro
+author: rastala
+ms.author: roastala
 ms.reviewer: sgilley
 ms.service: machine-learning
 ms.subservice: core