Home > Storage > PowerScale (Isilon) > Product Documentation > Storage (general) > Dell PowerScale: Key Performance Prediction using Artificial Intelligence for IT operations (AIOps) > Training the model
Over a period of two months, more than 50 different AI models were trained using a variety of different time series architectures, varying model architecture parameters, hyperparameters, and data engineering techniques to maximize performance, without overfitting to existing data. When these training pipelines were created in NeoPulse, they could be reused easily as new data arrived from the client every week, to rerun training and testing to quantify the performance of the model. At the end of the two-month period we had built a model that could predict whether this one performance metric (NFS3 latency) would be above a threshold of 10ms, correctly for 70% of each one of the next 48 five-minute intervals (4 hours total).