Kalyan K Veeramachaneni

20220207391, Jun 30, 2022

Dec 30, 2020

17/137720

- Irvine CA, US
James Max Kanter - Boston MA, US
Kalyan Kumar Veeramachaneni - Watertown MA, US

G06N 5/04
G06N 20/00

A feature engineering application receives a plurality of data sets from different data sources for training a model for making a prediction based on new data. The feature engineering application generates primitives based on the data sets. A primitive is to be applied to a variable in the data sets to synthesize a feature. The feature engineering application also receives a temporal parameter that specifies a temporal value for generating time-based features. After the primitives are generated and the temporal parameter is received, the feature engineering application aggregates the plurality of data entities based on primary variables in the plurality of data entities and generate an entity set based on the aggregation. The feature engineering application then synthesize features, including the time-based features, based on the entity set, at least some of the primitives, and the temporal parameter.

20160132787, May 12, 2016

Jan 16, 2015

14/598628

Will D. Drevo - Cambridge MA, US
Kalyan K. Veeramachaneni - Brighton MA, US
Una-May O'Reilly - Weston MA, US

Massachusetts Institute of Technology - Cambridge MA

G06N 99/00

A system is provided for multi-methodology, multi-user, self-optimizing Machine Learning as a Service for that automates and optimizes the model training process. The system uses a large-scale distributed architecture and is compatible with cloud services. The system uses a hybrid optimization technique to select between multiple machine learning approaches for a given dataset. The system can also use datasets to transferring knowledge of how one modeling methodology has previously worked over to a new problem.