Microsoft Technology Licensing, LLC

Gilbert Antonius

Marc Todd Friedman

Alekh Jindal

Hiren Shantilal Patel

Shi Qiao

Irene Rogan Shaffer

Markus Weimer

Abhishek Roy

Lucas Rosenblatt

Vijay Kumar Ramani

Remmelt Herbert Lieve Ammerlaan

H M Sajjad Hossain

Peter Orenberg

Soundararajan Srinivasan

Sheng Yi

Biyi Fang

Jenna Hong

Tra My Nguyen

Ryan Serrao

Apurva Sandeep Gandhi

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

A computer system is provided that includes one or more processors configured to receive user input for inked content to a digital canvas, and process the inked content to determine one or more writing regions. Each writing region includes recognized text and one or more document layout features associated with that writing region. The one or more processors are further configured to tokenize a target writing region of the one or more writing regions into a sequence of tokens, process the sequence of tokens of the target writing region using a task extraction subsystem that operates on tokens representing both the recognized text and the one or more document layout features of the target writing region, segment the target writing region into one or more sentence segments, and classify each of the one or more sentence segments as a task sentence or a non-task sentence.

Sequence labeling task extraction from inked content

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Gilbert Antonius