Home > Storage > PowerScale (Isilon) > Product Documentation > Storage (general) > Dell PowerScale: Key Performance Prediction using Artificial Intelligence for IT operations (AIOps) > Data preparation
Dell Secure Remote Services (SRS) collects the raw performance data from 12 different all-flash PowerScale clusters on-site at a semiconductor design customer. Data from these clusters, which was used to support electronic design automation (EDA) workloads, was collected every week. We identify and extract 26 performance key metrics from the raw data, most of which are logged and updated every five minutes. NeoPulse is used to extract some additional fields, such as day of week and time of day from the UNIX timestamp fields, to allow the model to make better predictions. Every week new data was collected from the PowerScale cluster to increase the size of the training dataset and to improve the AI model. During each training run we also withheld 10% of the data, which was used to test the AI model in the testing phase. This was separate from the 10% of training data withheld for validation.