How to Set Up Automated Sensor Data Calibration with Azure ML

Hello djoshi,

Welcome to the Microsoft Q&A and thank you for posting your questions here.

I understand that you would like to Set Up Automated Sensor Data Calibration with Azure ML.

To automate sensor data calibration with Azure ML, start by preprocessing your raw sensor data—segmenting it into variables like Sensor Part A/B, Temperature, and Humidity. You can handle this either in an Azure Function App for real-time transformation or directly within Azure ML Designer** using data transformation modules - https://free.blessedness.top/en-us/azure/machine-learning/how-to-retrain-designer?view=azureml-api-1

Next, use Azure ML Designer pipelines to train and retrain your calibration model. You can configure pipeline inputs to dynamically update datasets and parameters, allowing retraining every few days with new sensor and reference data - https://free.blessedness.top/en-us/azure/machine-learning/how-to-retrain-designer?view=azureml-api-1 Once your pipeline is published, it becomes accessible via a REST API, enabling automated retraining and model invocation.

For deployment, Azure ML supports online endpoints that expose your model as a secure REST API. You can register your model, environment, and scoring script, then deploy and invoke it programmatically using standard HTTP requests - https://www.youtube.com/watch?v=hCPkrihZiDg

To manage the full lifecycle training, deployment, monitoring, and retraining. Microsoft provides robust MLOps tools. These include model versioning, data drift detection, and automated CI/CD pipelines for continuous improvement - https://github.com/PeakIndicators/Getting-Started-On-Azure-ML/blob/main/Documents/retrain-model-productionize.md and https://microsoft.github.io/azureml-ops-accelerator/2-Design/2-ModelManagement.html

I hope this is helpful! Do not hesitate to let me know if you have any other questions or clarifications.

Please don't forget to close up the thread here by upvoting and accept it as an answer if it is helpful.

Hi djoshi

Here is the proposed flow on accumulating the data from devices/sensors for calibration (Guidelines on AutoML already provided from Sina Salam).

You can enroll devices in IOT Hub using individual device registration or DPS (Device provisioning systems). Their telemetry data can be routed to endpoints like

• Built-in endpoint
• Storage containers
• Service Bus queues
• Service Bus topics
• Event Hubs
• Cosmos DB

You can connect to those data with respective Clients (Storage clients, Eventhub etc) to create Data store in Azure ML which can be used input for model training.

IOT hub also provide option to export to Power BI. Power BI has inherent model training feature.

But, we need to pre-process the data prior model training in AutoML / PowerBI inbuilt models

Reference used

1. https://free.blessedness.top/en-us/azure/architecture/guide/iot/machine-learning-inference-iot-edge
2. https://free.blessedness.top/en-us/azure/iot/iot-overview-analyze-visualize?tabs=edge
3. https://techcommunity.microsoft.com/blog/azurearchitectureblog/end-to-end-iot-analytics-and-machine-learning-with-azure-data-and-ai-services/2365938
4. https://free.blessedness.top/en-us/azure/iot-hub/iot-hub-devguide-messages-d2c

Hope it helps.

Thank you.