Abstract: In an example embodiment, a fully automated process is provided for frequent model retraining and redeployment of a machine learned model trained to output a prediction of how likely it is that a candidate is qualified for a particular job posting. Model quality verification is provided by maintaining a snapshot of a baseline model and automatically comparing it to a proposed model by performing various metrics on the models by testing the models using a holdout data set that includes only data that was not used during the training process. Overlap between data in the holdout set used during retraining and the training set used during initial training is prevented by splitting each dataset using a hash on certain fields of the data.

Abstract: Techniques for reducing electronic resource consumption using search data are disclosed herein. In some embodiments, a computer-implemented method comprises: identifying a cohort of profiles from profiles based on a determination that at least one attribute is shared among the profile data of the cohort; receiving corresponding search appearance data including an impression count for the cohort of profiles; selecting reference profiles from the cohort based on the impression counts of the reference profiles; selecting a target profile from the cohort based on the impression count of the target profile; identifying a trend corresponding to at least one feature among the reference profiles; and causing an indication of the feature(s) to be displayed on a computing device of the user of the target profile based on the identifying of the trend.

Abstract: Techniques for reducing electronic resource consumption using search data are disclosed herein. In some embodiments, a computer-implemented method comprises: identifying a cohort of profiles from profiles based on a determination that at least one attribute is shared among the profile data of the cohort; receiving corresponding search appearance data including an impression count for the cohort of profiles; selecting reference profiles from the cohort based on the impression counts of the reference profiles; selecting a target profile from the cohort based on the impression count of the target profile; identifying a trend corresponding to at least one feature among the reference profiles; and causing an indication of the feature(s) to be displayed on a computing device of the user of the target profile based on the identifying of the trend.