Issaquah, WA, United States of America

Pedrito Uriah Maynard-Zhang


Average Co-Inventor Count = 7.8

ph-index = 2

Forward Citations = 91(Granted Patents)


Company Filing History:


Years Active: 2011-2024

where 'Filed Patents' based on already Granted Patents

3 patents (USPTO):

Title: Innovations of Pedrito Uriah Maynard-Zhang

Introduction

Pedrito Uriah Maynard-Zhang is an accomplished inventor based in Issaquah, WA (US). He has made significant contributions to the field of machine learning and data processing, holding a total of three patents. His work focuses on enhancing the efficiency and effectiveness of ETL (Extract, Transform, Load) jobs through innovative machine learning techniques.

Latest Patents

One of his latest patents is titled "Using specified performance attributes to configure machine learning pipeline stages for an ETL job." This invention outlines how specified performance attributes can be utilized to configure machine learning transformations for ETL jobs. The performance attributes for a machine learning pipeline that applies a model as part of a transformation for an ETL job can be used to configure a parameter in a stage of the machine learning pipeline. The configured stage is then employed during the model training process. Once trained, the machine learning pipeline can be applied as part of a transformation operation included in an ETL job performed by the ETL system.

Career Highlights

Pedrito currently works at Amazon Technologies, Inc., where he continues to innovate and develop new solutions in the realm of machine learning and data processing. His expertise in configuring machine learning pipelines has positioned him as a valuable asset to his team and the company.

Collaborations

Some of his notable coworkers include Timothy Jones and Andrew Borthwick, who collaborate with him on various projects within the company.

Conclusion

Pedrito Uriah Maynard-Zhang's contributions to machine learning and ETL processes demonstrate his innovative spirit and commitment to advancing technology. His patents reflect a deep understanding of performance attributes and their application in real-world scenarios.

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