1. A method performed by a storage system having one or more processors, comprising: collecting storage system performance data using a plurality of counters; filtering by an analytics engine of the storage system the collected storage system performance data by eliminating any counter value that is constant over time and any counter that has a zero value, wherein the one or more processors invoke the analytics engine and the filtering generates a first subset of counters with associated performance data that affects performance of the storage system relative to a service level objective; identifying by the analytics engine which of the first subset of counters are monotonic with respect to a performance parameter that is used for predicting performance of the storage system relative to the service level objective; selecting by the analytics engine a second subset of counters from the first subset of counters based on a degree of monotonicity where the degree of monotonicity selects lower deviation monotonic counters over higher deviation monotonic counters from among the first subset of counters; determining by the analytics engine a third subset of counters from the second subset of counters by using an initial regression model with a first threshold value and a second regression model with a second threshold value that is less than the first threshold value to determine whether the third subset of counters are a reliable indicator of performance of the storage system relative to the service level objective; using by the analytics engine recursive partitioning for iteratively determining regression error of the second regression model for the third subset of counters to identify a fourth subset of counters with minimum regression error; creating a workload signature of the storage system relative to the service level objective and by using recursive partitioning of performance data associated with the fourth subset of counters and representing a plurality of measures of actual live storage system performance, wherein the workload signature is based on the fourth subset of counters and the minimum regression error associated with the fourth subset of counters; creating, by the analytics engine, a storage system performance model by using the workload signature and an uncertainty measurement technique, wherein the uncertainty measurement technique applies a Gaussian process model to each leaf node of the recursive partitioning and computes a confidence band indicating a certain confidence level for a likelihood of actual performance to be within a specified deviation from a prediction of storage system performance; and predicting, by the analytics engine, performance of the storage system under a sample load by applying the sample load to the storage system performance model.