Deploy ML model on Azure SQL Edge using ONNX
Important
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:
- Use Azure Data Studio to connect to SQL Database in the Azure SQL Edge instance.
- Predict iron ore impurities with ONNX in Azure SQL Edge.
Key components
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));
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
Open Azure Data Studio.
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 Select Connect.
In the File section, open
/DeploymentScripts/MiningProcess_ONNX.jpynb
from the folder in which you cloned the project files on your machine.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.