Sandy, UT, United States of America

Chris K Knoch

USPTO Granted Patents = 1 

Average Co-Inventor Count = 8.0

ph-index = 1


Company Filing History:


Years Active: 2024

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1 patent (USPTO):Explore Patents

Title: Chris K Knoch: Innovator in Automated Machine Learning Systems

Introduction

Chris K Knoch is a notable inventor based in Sandy, UT (US). He has made significant contributions to the field of machine learning, particularly through his innovative patent that focuses on automating the deployment of machine learning models. His work is essential in streamlining processes that involve data analysis and predictive modeling.

Latest Patents

Chris K Knoch holds a patent for an "Automated Machine Learning System." This technology automates the deployment of machine learning models by allowing users to connect to data stores and select target metrics through a graphical user interface. The system identifies datasets that correlate with the target metric and trains the machine learning model to generate accurate predictions. This innovation enhances the efficiency of machine learning applications in various industries.

Career Highlights

Chris is currently employed at Qliktech International AB, where he continues to develop and refine technologies that leverage machine learning. His expertise in this area has positioned him as a valuable asset to his company and the broader tech community. With a focus on practical applications of machine learning, Chris has contributed to advancements that benefit businesses and researchers alike.

Collaborations

Some of Chris's coworkers include Killian B Dent and James M Friedman. Their collaborative efforts contribute to the innovative environment at Qliktech International AB, fostering the development of cutting-edge technologies.

Conclusion

Chris K Knoch is a prominent figure in the realm of automated machine learning systems. His patent and ongoing work at Qliktech International AB highlight his commitment to advancing technology in this critical field. His contributions are paving the way for more efficient data analysis and predictive modeling solutions.

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