Merge pull request #202307 from WilliamDAssafMSFT/20220602-reassing-dphansen

20220621 reassign dphansen

Author: PRMerger17

articles/azure-sql-edge/deploy-onnx.md

@@ -6,9 +6,10 @@ keywords: deploy SQL Edge
 ms.prod: sql
 ms.technology: machine-learning
 ms.topic: quickstart
-author: dphansen
-ms.author: davidph
-ms.date: 05/06/2021
+author: WilliamDAssafMSFT
+ms.author: wiassaf
+ms.reviewer: hudequei
+ms.date: 06/21/2022
 ms.custom: mode-other
 ---
 
@@ -27,8 +28,8 @@ This quickstart is based on **scikit-learn** and uses the [Boston Housing datase
 * Install Python packages needed for this quickstart:
 
   1. Open [New Notebook](/sql/azure-data-studio/sql-notebooks) connected to the Python 3 Kernel. 
-  1. Click **Manage Packages**
-  1. In the **Installed** tab, look for the following Python packages in the list of installed packages. If any of these packages are not installed, select the **Add New** tab, search for the package, and click **Install**.
+  1. Select **Manage Packages**
+  1. In the **Installed** tab, look for the following Python packages in the list of installed packages. If any of these packages are not installed, select the **Add New** tab, search for the package, and select **Install**.
      - **scikit-learn**
      - **numpy**
      - **onnxmltools**

articles/azure-sql-edge/onnx-overview.md

@@ -6,9 +6,10 @@ services: sql-edge
 ms.service: sql-edge
 ms.subservice: machine-learning
 ms.topic: conceptual
-author: dphansen
-ms.author: davidph
-ms.date: 05/19/2020
+author: WilliamDAssafMSFT
+ms.author: wiassaf
+ms.reviewer: hudequei
+ms.date: 06/21/2022
 ---
 
 # Machine learning and AI with ONNX in SQL Edge
@@ -25,7 +26,7 @@ To obtain a model in the ONNX format:
 
 - **Model Building Services**: Services such as the [automated Machine Learning feature in Azure Machine Learning](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb) and [Azure Custom Vision Service](../cognitive-services/custom-vision-service/getting-started-build-a-classifier.md) support directly exporting the trained model in the ONNX format.
 
-- [**Convert and/or export existing models**](https://github.com/onnx/tutorials#converting-to-onnx-format): Several training frameworks (e.g. [PyTorch](https://pytorch.org/docs/stable/onnx.html), Chainer, and Caffe2) support native export functionality to ONNX, which allows you to save your trained model to a specific version of the ONNX format. For frameworks that do not support native export, there are standalone ONNX Converter installable packages that enable you to convert models trained from different machine learning frameworks to the ONNX format.
+- [**Convert and/or export existing models**](https://github.com/onnx/tutorials#converting-to-onnx-format): Several training frameworks (for example, [PyTorch](https://pytorch.org/docs/stable/onnx.html), Chainer, and Caffe2) support native export functionality to ONNX, which allows you to save your trained model to a specific version of the ONNX format. For frameworks that do not support native export, there are standalone ONNX Converter installable packages that enable you to convert models trained from different machine learning frameworks to the ONNX format.
 
      **Supported frameworks**
    * [PyTorch](http://pytorch.org/docs/master/onnx.html)