In this scenario, we illustrate the example of using machine learning and IoT data pipeline to support predictive maintenance.
We use ML Units for BTS Prediction for:
- create ML services by deploying ML models into a service
- the service accepts requests from messaging systems (currently using AMQP)
- using other units/services for emulating sensors
- ML clients can take data from sensors, preparing data for ML requests and sending the requests to the ML service
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Currently a ML client just reads pre-processing data from files. A developer can change the code to read data from MQTT (e.g., using our IoTCloudUnits) and to perform the data preprocessing
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