‘We have been working on advanced data handling and Machine Learning algorithms for over a year, focusing predominantly on enhancing our solution to learn and become as proficient as our human data scientists are today at identifying anomalies, and tracking down the cause behind the symptom. This capability provides powerful operational and business insight into data centre systems and component level performance,’ said Liam Newcombe, Romonet’s cofounder and CTO.
Having modelled, collected data and analysed hundreds of data centres in the past eight years, Romonet’s platform has an incredibly detailed and expansive data archive on how facilities of every size perform under different climate, environmental, energy, IT and commercial factors.
While the use of Machine Learning applications is not new, Romonet’s platform combines metered data, Machine Learning, simulation and predictive analytics.
‘In our case, teaching the machine is much faster as we feed it pre-cleansed and calibrated data to recognise and learn patterns, incorporate additional data from outside sources, and teach the software to suggest causes and recommended actions from previously learned results,’ added Newcombe.