Intuit, Inc.

Abhishek Jain

Caio Vinicius Soares

Frank Wisniewski

Sumanth Venkatasubbaiah

Qingbo Hu

Andreas Mavrommatis

Pankaj Rastogi

Karthik Prakash

Nicholas Jeffrey Hoh

Yuwen Ellen Wu

Joseph B Cessna

Grace Wu

Tristan Cooper Baker

Xiangling Meng

Ying Yang

Jinlong Liu

Certain aspects of the present disclosure provide techniques for operation of a feature management platform. A feature management platform is an end-to-end platform developed to manage the full lifecycle of data features. For example, to create a stateful feature, the feature management platform can receive a processing artifact from a computing device. The processing artifact defines the stateful feature, including the data source to retrieve event data from, when to retrieve the event data, the type of transform to apply, etc. Based on the processing artifact, the feature management system generates a processing job (e.g., the API defines a pipeline), which when initiated generates a vector that encapsulates the stateful feature. The vector is transmitted to the computing device that locally hosts a model, which generates a prediction that is transmitted to the feature management platform. Subsequently, the predication and stateful feature can be transmitted to other computing devices.

Real time fault tolerant stateful featurization

Certain aspects of the present disclosure provide techniques for operation of a feature management platform. A feature management platform is an end-to-end platform developed to manage the full lifecycle of data features. For example, to create a feature the feature management platform can receive a processing artifact (e.g., a configuration file and code fragment) from a computing device. The processing artifact defines the feature, including the data source to retrieve event data from, when to retrieve the event data, the type of transform to apply, etc. Based on the processing artifact, the feature management system generates a processing job, which when initiated generates a vector that encapsulates the feature data. The vector is transmitted to the computing device that locally hosts a model, which generates a prediction. The prediction is transmitted to the feature management platform and can be transmitted to other computing devices, upon request.

Feature management platform

A method may include receiving, from a software application, pre-signup data of a user, embedding the pre-signup data to obtain a pre-signup data vector, generating, by applying a topic model to the pre-signup data vector, a pre-signup topic vector, receiving action descriptions of actions of the software application, embedding the action descriptions to obtain action description vectors, generating, by applying the topic model to the action description vectors, action description topic vectors, generating scores by multiplying the pre-signup topic vector and the action description topic vectors, and recommending, to the user and using the scores, an action.

Action recommendation engine

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Mountain View, CA, United States of America

Abhishek Jain