Using machine learning to predict infrastructure health
Abstract:
Metric data may be received, where the metric data may include one or more time series, where each time series may include a set of metric datapoints corresponding to a set of time slices, and where each metric datapoint may be a measurement of a metric of a monitored object in an infrastructure. An infrastructure health score may be computed for each time slice in the set of time slices based on the metric data. A machine learning (ML) model may be trained based on metric datapoints corresponding to at least a current time slice and the infrastructure health score corresponding to a future time slice. The trained ML model may then be used to predict a future infrastructure health score based on at least current metric datapoints.
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