I have a question about RNN's based on LSTM cells. Currently i'm trying to predict anomalies in time series data, based on the prediction error. Is it reasonable to run RNN's distributed in production? For example to scale it with Google Cloud ML Engine. I want to be able to scale the model in case it has to compute to many requests during inference.
But when I distribute the model what will happen to the memory cell? The data is split and distributed over multiple nodes, will it still recognize the pattern of the time series data?