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I am working on a recurrent neural network (LSTM) to predict a time series. since the training takes time, I have some concerns about the real-world situation when the model is going to implement in a hardware (a chip), it should predict the next state based on the input, but since the environment changes rapidly, the model must retrain (frequently) based on the environment. how should I take care of this re-trainig process? I also want it to train very fast, I am mostly concern about the strategies which I can use for it, like taining with less epochs or less layers, but since I am not an expert in Neural Networks, I have many confusion about it.

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