Peter Orenberg

Title: The Innovations of Peter Orenberg

Introduction

Peter Orenberg is an accomplished inventor based in Braintree, MA (US). He has made significant contributions to the field of machine learning, particularly in system deployments. With a total of 2 patents, Orenberg's work focuses on enhancing performance and reliability in technology systems.

Latest Patents

Orenberg's latest patents include a groundbreaking system and method for machine learning for system deployments without performance regressions. This innovative approach utilizes a performance safeguard system to design pre-production experiments that assess the readiness of learned models. By leveraging big data processing infrastructure, Orenberg's method deploys a large set of learned or optimized models for query optimization. The pipeline for learning and training differentiates the impact of query plans, selecting those likely to yield the most significant performance improvements. This process involves running constrained pre-production experiments to empirically observe runtime performance, ultimately selecting models that promise consistent performance enhancements for deployment.

Career Highlights

Peter Orenberg is currently employed at Microsoft Technology Licensing, LLC, where he continues to push the boundaries of machine learning applications. His work is instrumental in developing systems that ensure safe deployment of machine learning features, thereby enhancing the overall efficiency of technology systems.

Collaborations

Orenberg collaborates with notable colleagues, including Irene Rogan Shaffer and Remmelt Herbert Lieve Ammerlaan. Their combined expertise contributes to the innovative projects at Microsoft Technology Licensing, LLC.

Conclusion

Peter Orenberg's contributions to machine learning and system deployments are noteworthy. His innovative patents and collaborative efforts at Microsoft Technology Licensing, LLC, reflect his commitment to advancing technology. Orenberg's work not only enhances performance but also ensures the reliability of machine learning systems in various applications.