Microsoft Technology Licensing, LLC

Lucas Rosenblatt

Marc Todd Friedman

Alekh Jindal

Hiren Shantilal Patel

Shi Qiao

Markus Weimer

Irene Rogan Shaffer

Gilbert Antonius

Abhishek Roy

Peter Orenberg

Soundararajan Srinivasan

Vijay Kumar Ramani

Remmelt Herbert Lieve Ammerlaan

H M Sajjad Hossain

Benjamin Gustav Wilde

Ransom Lloyd Richardson

Aleksandr Polyakov

Michael Adam Scarpati

Natalie Ann Mionis

Jenna Hong

Courtney Sarah Cochrane

Jichen Yang

Yang He

Sean Gormley T Kelley

Anish Yatin Pimpley

Abhijith Asok

Yiquan Xu

Amit Dinesh Gupte

The disclosed technology is generally directed to the conversion of text to tables. In one example of the technology, input text that includes at least three rows is received. A plurality of characteristics of the input text is determined. Each characteristic of the plurality of characteristics is associated with a uniformity between the rows of the input text. The plurality of characteristics includes at least one characteristic that is associated with a delimiter count. A determination is made as to whether the input text is suitable for conversion to table based on the plurality of characteristics. Upon determining that the input text is suitable for conversion to a table, a machine learning model is used to convert the input text into a table.

Intelligent table suggestion and conversion for text

Methods of machine learning for system deployments without performance regressions are performed by systems and devices. A performance safeguard system is used to design pre-production experiments for determining the production readiness of learned models based on a pre-production budget by leveraging big data processing infrastructure and deploying a large set of learned or optimized models for its query optimizer. A pipeline for learning and training differentiates the impact of query plans with and without the learned or optimized models, selects plan differences that are likely to lead to most dramatic performance difference, runs a constrained set of pre-production experiments to empirically observe the runtime performance, and finally picks the models that are expected to lead to consistently improved performance for deployment. The performance safeguard system enables safe deployment not just for learned or optimized models but also for additional of other ML-for-Systems features.

System and method for machine learning for system deployments without performance regressions

Growing community of inventors

Somerville, MA, United States of America

Lucas Rosenblatt