Deploy ML model on Azure SQL Edge using ONNX

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Important

Azure SQL Edge will be retired on September 30, 2025. For more information and migration options, see the Retirement notice.

Note

Azure SQL Edge no longer supports the ARM64 platform.

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. Select Connect.

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

  5. Set the kernel to Python 3.

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