Facebook, Inc.

Salesforce Com, Inc.

Tappingstone Inc.

Yue Kwen Justin Yip

Kit Pang Szeto

Simon Chan

Ka Hou Chan

Yunkai Zhou

Youzhong Liu

Jian Huang

Systems and methods are disclosed for providing weighted evaluation. The system may comprise one or more processors and a memory storing instructions that, when executed by the one or more processors, cause the system to obtain a plurality of data point groups, obtain an inbound rank of each data point group based on one or more inbound ranks of one or more data groups linking to the data point group, obtain an outbound rank of the each data point group based on one or more outbound ranks of one or more data groups linked from the each data point group, obtain a group rank of the each data point group based on the inbound rank and the outbound rank of the each data point group, and process the data point groups according to the corresponding group ranks.

Method and system for providing weighted evaluation

In accordance with disclosed embodiments, there are provided systems, methods, and apparatuses for implementing predictive engine evaluation and replay of engine performance. An exemplary system may include, for example: means selecting a first set of one or more algorithms for a machine learning model; tuning a first group of predictive engine parameters for the machine learning model; training the machine learning model with one or more sources of data using the selected first set of one or more algorithms and the first group of tuned predictive engine parameters to generate a first predictive engine variant from the trained machine learning model; selecting a second set of one or more algorithms for a machine learning model which are different than the first set; tuning a second group of predictive engine parameters for the machine learning model which are different than the first group; training the machine learning model with the one or more sources of data using the selected second set of one or more algorithms and the second group of tuned predictive engine parameters to generate a second predictive engine variant from the trained machine learning model; performing multiple experiments using the first and second predictive engine variants; comparing results from the multiple experiments; and deploying either the first predictive engine variant or the second predictive engine variant based on the comparison of the results of the multiple experiments. Other related embodiments are disclosed.

Methods and systems for predictive engine evaluation and replay of engine performance

Disclosed are methods and systems of creating, evaluating, and tuning a predictive engine for machine learning, including steps to deploy the predictive engine with an initial parameter set; receive queries to the deployed engine variant and in response, generate predicted results; receive corresponding actual results; associate the queries, the predicted results, and the actual results with a replay tag; evaluate the performance of the deployed engine variant; generate a new engine parameter set based on tuning of one or more parameters of the initial engine parameter set, according to the evaluation results; deploy the new engine variant to replace the initial engine variant; receive a replay request from an operator specifying the currently or a previously deployed engine variant; and in response to the replay request, replay at least one of the queries, the corresponding predicted results, the actual results, and the evaluation results.

Methods and systems for predictive engine evaluation, tuning, and replay of engine performance

Growing community of inventors

Sunnyvale, CA, United States of America

Yue Kwen Justin Yip