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: The disclosed embodiments provide a system for processing a query for a ranking of candidates for an opportunity. During operation, the system obtains parameters associated with a query for a ranking of candidates for an opportunity, wherein the parameters include a candidate and the opportunity. Next, the system matches one or more of the parameters to a fixed number of quantile thresholds calculated from a distribution of scores for the candidates. The system then estimates, based on the fixed number of quantile thresholds, a quantile for a score of the candidate. Finally, the system outputs a position of the candidate within the ranking based on the estimated quantile.

Abstract: The disclosed embodiments provide a system for processing a query for a ranking of candidates for an opportunity. During operation, the system obtains a set of parameters associated with a query for a ranking of candidates for an opportunity, wherein the set of parameters include a candidate, the opportunity, and a ranking category. Next, the system uses the set of parameters to obtain a set of pre-computed scores for use in generating the ranking and determine a number of online scores required to complete the ranking. The system then generates a set of calls to produce the determined number of the online scores. Finally, the system uses the pre-computed scores and the online scores to generate a result of the query.

Abstract: The disclosed embodiments provide a system for processing a query for a ranking of candidates for an opportunity. During operation, the system obtains parameters associated with a query for a ranking of candidates for an opportunity, wherein the parameters include a candidate and the opportunity. Next, the system matches one or more of the parameters to a fixed number of quantile thresholds calculated from a distribution of scores for the candidates. The system then estimates, based on the fixed number of quantile thresholds, a quantile for a score of the candidate. Finally, the system outputs a position of the candidate within the ranking based on the estimated quantile.

Abstract: The disclosed embodiments provide a system for processing a query for a ranking of candidates for an opportunity. During operation, the system obtains a set of parameters associated with a query for a ranking of candidates for an opportunity, wherein the set of parameters include a candidate, the opportunity, and a ranking category. Next, the system uses the set of parameters to obtain a set of pre-computed scores for use in generating the ranking and determine a number of online scores required to complete the ranking. The system then generates a set of calls to produce the determined number of the online scores. Finally, the system uses the pre-computed scores and the online scores to generate a result of the query.