tutorial-run-ml-model-on-sql-edge.md

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Deploy ML model on Azure SQL Edge using ONNX	In part three of this three-part Azure SQL Edge tutorial for predicting iron ore impurities, you'll run the ONNX machine learning models on SQL Edge.		sql-edge	sql-edge	tutorial	kendalvandyke	kendalv	jroth	05/19/2020

Deploy ML model on Azure SQL Edge using ONNX

In part three of this three-part tutorial for predicting iron ore impurities in Azure SQL Edge, you'll:

1. Use Azure Data Studio to connect to SQL Database in the Azure SQL Edge instance.
2. Predict iron ore impurities with ONNX in Azure SQL Edge.

Key Components

1. The solution uses a default 500 milliseconds between each message sent to the Edge Hub. This can be changed in the Program.cs file

   TimeSpan messageDelay = configuration.GetValue("MessageDelay", TimeSpan.FromMilliseconds(500));
2. The solution generated a message, with the following attributes. Add or remove the attributes as per requirements.

{
    timestamp 
    cur_Iron_Feed
    cur_Silica_Feed 
    cur_Starch_Flow 
    cur_Amina_Flow 
    cur_Ore_Pulp_pH
    cur_Flotation_Column_01_Air_Flow
    cur_Flotation_Column_02_Air_Flow
    cur_Flotation_Column_03_Air_Flow
    cur_Flotation_Column_04_Air_Flow
    cur_Flotation_Column_01_Level
    cur_Flotation_Column_02_Level
    cur_Flotation_Column_03_Level
    cur_Flotation_Column_04_Level
    cur_Iron_Concentrate
}

Connect to the SQL Database in the Azure SQL Edge instance to train, deploy, and test the ML model

1. Open Azure Data Studio.

2. In the Welcome tab, start a new connection with the following details:

   Field	Value
   Connection type	Microsoft SQL Server
   Server	Public IP address mentioned in the VM that was created for this demo
   Username	sa
   Password	The strong password that was used while creating the Azure SQL Edge instance
   Database	Default
   Server group	Default
   Name (optional)	Provide an optional name

3. Click Connect

4. In the File section, open /DeploymentScripts/MiningProcess_ONNX.jpynb from the folder where you have cloned the project files on your machine.

5. Set the kernel to Python 3.

Next steps

For more information on using ONNX models in Azure SQL Edge, see Machine learning and AI with ONNX in SQL Edge.