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Machine Learning and SLAs: Staying on Track by Predicting Usage Trends
Machine Learning (ML) algorithms spot patterns and trends from past data. Typically, the more data that can be fed into the algorithm, the better the prediction.
One example application is how ML can be used to assist IT in more efficiently resourcing and anticipating trends in usage to meet agreed upon service level agreements (SLAs).
ML can be used to analyze large amounts of usage and performance data. The algorithms can identify and anticipate patterns and spikes where usage levels may be at their highest. ML algorithms can automate the task of monitoring status across resources.
ML reduces the possibility of human error, can be used against real time data, and is typically more accurate in prediction than humans. While algorithms aren’t 100 percent accurate, they can provide you information about expected trends.
Marko Djapic wrote for Rubik’s Code that “when it comes to reaching the targeted levels, staying above, or analyzing anomalies, Machine Learning can help your company and your clients get the results to be right where they need to be, and also to have better insight into the future of your business, so you can schedule, and plan, accordingly.”
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