Traditionally, pinpointing the best possible model for a given prediction problem requires optimizing an entire parameter space—and even taking this step involves choosing between a number of different methodologies. Auto Tune Models (ATM), our cloud-based modeling system, provides an easier, faster way to narrow down to the best choice. ATM performs bandit-based and Gaussian process learning to decide among methodologies, as well as to pinpoint which parameters and hyperparameters should be used for modeling. The result is a vastly more efficient end-to-end process.

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hdi

Thomas Swearingen, Will Drevo, Bennett Cyphers, Alfredo Cuesta-Infante, Kalyan Veeramachaneni. IEEE International Conference on Big Data, Boston, 2017.

Thomas Swearingen, Bennett Cyphers, Arun Ross, Gaurav Sheni, Kalyan Veeramachaneni

Bennett Cyphers
Thomas Swearingen
Alfredo Cuesta-Infante
Will Drevo