Weather Forecast from sensor data , Azure IOT Hub, Stream analytics and ML Studio
Machine learning is a technique of data science that helps computers learn from existing data to forecast future behaviors, outcomes, and trends. ML Studio (classic) is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions. In this article, you learn how to use ML Studio (classic) to do weather forecasting (chance of rain) using the temperature and humidity data from your Azure IoT hub. The chance of rain is the output of a prepared weather prediction model. The model is built upon historic data to forecast chance of rain based on temperature and humidity.
Complete the Raspberry Pi online simulator tutorial or one of the device tutorials. For example, you can go to Raspberry Pi with Node.js or to one of the Send telemetry quickstarts. These articles cover the following requirements:
An active Azure subscription.
An Azure IoT hub under your subscription.
A client application that sends messages to your Azure IoT hub.
An ML Studio (classic) account.
An Azure Storage account, A General-purpose v2 account is preferred, but any Azure Storage account that supports Azure Blob storage will also work.
- Introduction to Weather Forecasting using azure services
- Use case of Weather Forecasting using azure services
- Create IOT Hub In Azure Portal
- Create Devices in Azure Iot Hub
- Azure Iot Hub Explorer
- Raspberry Pi Simulator
- Connect Device to Azure Iot Hub
- Create Azure Stream Analytics Job
- Connect IOT to Azure Stream Analytics
- Create Predictive Experiment in Azure Machine Learning Studio
- Add Machine Learning web service as function in Azure Stream Analytics
- Run Azure Stream Analytics Job
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