I have some sensors which fetch data from cement factory and sends data to AWS IoT. The data is then tested on pre-trained model and the model predicts quality of cement based on some parameters. The data is coming in one second interval.
Since the data is coming in real-time, I want to train the model incrementally in real time.
Can anybody suggest how train model continuously?
You could aggregate certain numbers of training data and then use .partial_fit()
to update your model.
.partial_fit()
is the incremental learning option, which is available in Sklearn.
If your incremental data would not fit in RAM, then its worth trying dask-ml wrapper for incremental learning .
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