At&t Intellectual Property I, L.p.

Capital One Services, LLC

Christopher Kim

Prince Paulraj

Paul Ireifej

Heather Wighton

Mohammad Omar Khalid Mirza

Stephen Grandinetti

Mger Babayan

Parul Sethi

Wayne Zhen Yann Wong

Min Fang

Cuong Vo

Prathiba Sugumaran

Dimitrios Dimitropoulos

Venkata Ramaswamy Bayya

A method performed by a processing system including at least one processor includes detecting that new data has been added to a repository of reusable machine learning models and machine learning model features, applying data protection to the new data, testing the new data for bias, merging at least a portion of the new data with stored data from the repository to build a new machine learning model in which the data protection is preserved, and publishing the new machine learning model in the repository.

Governance mechanisms for reuse of machine learning models and features

Systems and methods for generating deployment databases based on preloaded data from heterogeneous sources are disclosed herein. The system may receive first structured data. The system may extract first data of a first format and second data of a second format. The system may determine that the first data has a first update rate. The system may determine that the second data has a second update rate. The system may preload the first data by retrieving a first identifier and by storing a first preloaded representation of a first parent dataset for the first data. The system may receive a first request for a deployment database. The system, based on the first request, may retrieve the first preloaded representation. The system may generate the deployment database.

Systems and methods for generating integrated dependency data and metadata artifacts for structured data

The disclosed technology relates to analyzing report infrastructure health to determine based on lineage the downstream tables and reports that may be impacted by an infrastructure failure to thereby improve reporting accuracy. An exemplary report management device may extract database table names identified in SQL queries. The SQL queries and report metadata are analyzed to identify driver and required tables, respectively. The database table names are stored in a table inventory and the database table names associated with one of the driver or required tables is marked. For each of the SQL queries comprising a target table, elements and source table names are extracted for source tables of the target table and an association is stored. Lineage data is generated based on the association when the report was not executed successfully. A graphical representation of the report lineage including a failed table and associated source table(s) is then output.

Systems and methods for analyzing report infrastructure health

Aspects of the subject disclosure may include, for example, receiving input data via a transformation UI, generating transformation configuration data, causing the transformation UI to present transformation object data per the transformation configuration data, where the transformation object data identifies data objects each including an input and output field name and a data type, detecting, from the transformation UI, an instruction defining a mapping for the input data, including a modification to the output field name of a data object such that the input field name of the data object is mapped to the modified output field name, based on the detecting the instruction, modifying the first transformation configuration data per the mapping to derive second transformation configuration data, performing a transformation of the input data based on the second transformation configuration data, and causing the transformation UI to present a transformation output. Other embodiments are disclosed.

Transformation as a service

Growing community of inventors

Plano, TX, United States of America

Christopher Kim