resolved changes in tutorial

Author: pioneer1781990

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

@@ -14,16 +14,15 @@ In this tutorial, you will be predicting iron ore impurities as a % of Silica in
 
 ## Pre-requisite software to be installed 
 1. If you don't have an Azure subscription, create a [free account](https://azure.microsoft.com/free/).
-2. Install [Visual Studio Professions/Enterprise](https://visualstudio.microsoft.com/vs/)
-3. Install [PowerShell 3.6.8](https://www.python.org/downloads/release/python-368/)
+2. Install [PowerShell 3.6.8](https://www.python.org/downloads/release/python-368/)
       * Windows x86-x64 Executable Installer
       * Ensure to add python path to the PATH environment variables
-4. Install ["Microsoft Visual C++ 14.0" and build tools for Visual Studio](https://visualstudio.microsoft.com/downloads/) - Download can be located under "Tools For Visual Studio 2019"
-5. Install [Microsoft ODBC Driver 17 for SQL Server](https://www.microsoft.com/download/details.aspx?id=56567)
-6. Install [Azure Data Studio](/sql/azure-data-studio/download-azure-data-studio/)
-7. Open Azure Data Studio and configure Python for Notebooks. Details on how this can be accessed [here](/sql/azure-data-studio/sql-notebooks#configure-python-for-notebooks).This step can take several minutes.
-8. Install latest version of [Azure CLI](https://github.com/Azure/azure-powershell/releases/tag/v3.5.0-February2020)
-9. The below scripts require that the AZ PowerShell to be at the latest version (3.5.0, Feb 2020)
+3. Install ["Microsoft Visual C++ 14.0" and build tools for Visual Studio](https://visualstudio.microsoft.com/downloads/) - Download can be located under "Tools For Visual Studio 2019"
+4. Install [Microsoft ODBC Driver 17 for SQL Server](https://www.microsoft.com/download/details.aspx?id=56567)
+5. Install [Azure Data Studio](/sql/azure-data-studio/download-azure-data-studio/)
+6. Open Azure Data Studio and configure Python for Notebooks. Details on how this can be accessed [here](/sql/azure-data-studio/sql-notebooks#configure-python-for-notebooks).This step can take several minutes.
+7. Install latest version of [Azure CLI](https://github.com/Azure/azure-powershell/releases/tag/v3.5.0-February2020)
+8. The below scripts require that the AZ PowerShell to be at the latest version (3.5.0, Feb 2020)
 
 ## Deploying Azure resources using PowerShell Script
 
@@ -235,3 +234,6 @@ az vm run-command invoke -g $ResourceGroup -n $EdgeDeviceId  --command-id RunShe
 ```powershell
 az ml workspace create -w $MyWorkSpace -g $ResourceGroup
 ```
+## Next Steps
+
+* [Set up IoT Edge Modules](set-up-iot-edge-modules.md)

articles/azure-sql-edge/run-ml-model-on-sql-edge.md

@@ -35,7 +35,7 @@ In this tutorial we will,
 4. In the file section, open a new notebook or use the keyboard shortcut Alt + Windows + N. Set the kernel to Python 3 before executing the below section.
 
 ## Predict Iron Ore Impurities (% of Silica) with ONNX in Azure SQL Edge
-The following python code can be collated in jupyter notebook and run on Azure Data Studio. Before we begin with the experiment, we need to install and import the below packages.
+The following python code can be collated in Jupyter notebook and run on Azure Data Studio. Before we begin with the experiment, we need to install and import the below packages.
 ```python
 !pip install azureml.core -q
 !pip install azureml.train.automl -q
@@ -131,7 +131,7 @@ We proceed with loading the model in Azure SQL Edge database for local scoring
 ```python
 ## Load the Model into a SQL Database.
 ## Define the Connection string parameters. These connection strings will be used later also in the demo.
-server = '40.69.153.211,1600' # SQL Server IP address
+server = '<SQL Server IP address>'
 username = 'sa' # SQL Server username
 password = '<SQL Server password>'
 database = 'IronOreSilicaPrediction'
@@ -151,7 +151,7 @@ conn.close()
 Finally, we use the Azure SQL Edge model to perform prediction using the trained model
 ```python
 ## Define the Connection string parameters. These connection strings will be used later also in the demo.
-server = '40.69.153.211,1600' # SQL Server IP address
+server = '<SQL Server IP address>'
 username = 'sa' # SQL Server username
 password = '<SQL Server password>'
 database = 'IronOreSilicaPrediction'

articles/azure-sql-edge/set-up-iot-edge-modules.md

@@ -1,6 +1,6 @@
 ---
 title: Set up IoT Edge Modules
-description: In this section, we will set up IoT edge modules and connections
+description: In this section, we will set up IoT Edge modules and connections
 keywords: 
 services: sql-database-edge
 ms.service: sql-database-edge
@@ -14,9 +14,9 @@ ms.date: 05/19/2020
 
 In this tutorial, we will be setting up the below IoT Edge modules
 1.  Azure SQL Edge 
-2.  A data generator module 
+2.  Data generator IoT Edge module 
 
-Before proceeding, create an Azure Stream Analytics Module that will be used in the tutorial for predicting Iron Ore impurities. You can learn more about using streaming jobs with SQL Edge [here](https://docs.microsoft.com/en-us/azure/sql-database-edge/stream-analytics#using-streaming-jobs-with-sql-database-edge)
+Before proceeding, create an Azure Stream Analytics Module that will be used in the tutorial for predicting Iron Ore impurities. You can learn more about using streaming jobs with SQL Edge [here](https://docs.microsoft.com/azure/sql-database-edge/stream-analytics#using-streaming-jobs-with-sql-database-edge)
 
 The Azure Stream Analytics job is created with hosting environment set as Edge. Now, proceed with setting up the Inputs and Outputs specifically for the tutorial. 
 
@@ -28,14 +28,15 @@ Event Serialization format|JSON
 Encoding|UTF-8
 Event compression type|None
 
-2. For creating the **output**, click '+Add' and choose SQL Database. Fill the details section as indicated below, 
-
+2. For creating the **output**, click '+Add' and choose SQL Database. For the purpose of this tutorial, fill the details section as indicated below, 
+> [!NOTE]
+> The password specified in this sections need to be specified for SQL SA password when deploying the SQL Edge module in section **"Deploying the Azure SQL Edge module"**
 Field|Value
 -----|-----
 Database|IronOreSilicaPrediction
 Server name|tcp:.,1433
 Username|sa
-Password|YourStrongPasswrod
+Password|Specify a strong password 
 Table|IronOreMeasurements1
 
 3. Navigate to the **Query** Section and ensure that the query is set up as below 
@@ -81,7 +82,7 @@ ASEdemocontregistry.azurecr.io/silicaprediction:amd64
 
 ## Deploying the Azure SQL Edge module
 
-1. First, deploy the Azure SQL Edge module by following the steps listed [here](https://docs.microsoft.com/en-us/azure/sql-database-edge/deploy-portal#deploy-sql-database-edge)
+1. First, deploy the Azure SQL Edge module by following the steps listed [here](https://docs.microsoft.com/azure/sql-database-edge/deploy-portal#deploy-sql-database-edge)
 
 2. On the **Specify Route** of the **Set Modules** page, specify the routes for module to IoT Edge Hub communication as below 
 ```
@@ -103,10 +104,7 @@ FROM /messages/modules/ASEDataGenerator/outputs/IronOreMeasures INTO BrokeredEnd
          }
        }
    ```
-Example 
-```json
-      {
-        "SqlPackage": "https://ASEstorage.blob.core.windows.net/sqldatabasedacpac/SQLDatabasedacpac.zip?sp=r&st=2020-04-  01T20:01:49Z&se=2021-01-01T05:01:49Z&spr=https&sv=2019-02-02&sr=b&sig=AH71glRTfULcyzJZKlgezfa0Epjfe7zv10mF%2BNzEIn0%3D",
-        "ASAJobInfo": "https://ASEstorage.blob.core.windows.net/bootdiagnostics-ironorepr-6984a2b8-6901-41a8-b3a6-49f86276e63d/ASAEdgeJobs/1278e9b9-94af-41ec-90b3-8172560340c1/e9bb25a6-d0a1-422b-9e29-4305a89e92f2/ASAEdgeJobDefinition.zip?sv=2018-03-28&sr=b&sig=KQZqR7diisByoUkCK6pEA3VJm%2FI2wHWEPDL1OwjMTJ8%3D&st=2020-04-01T20%3A21%3A20Z&se=2023-04-01T20%3A31%3A20Z&sp=r"
-      }
-```
+
+## Next Steps
+
+* [Deploying ML model on Azure SQL Edge using ONNX ](run-ml-model-on-sql-edge.md)