Microsoft Technology Licensing, LLC

Markus Weimer

Marc Todd Friedman

Alekh Jindal

Hiren Shantilal Patel

Shi Qiao

Matteo Interlandi

Irene Rogan Shaffer

Gilbert Antonius

Lucas Rosenblatt

Abhishek Roy

Remmelt Herbert Lieve Ammerlaan

H M Sajjad Hossain

Peter Orenberg

Soundararajan Srinivasan

Vijay Kumar Ramani

Yuan Yu

Carlo Aldo Curino

Rathijit Sen

Subramaniam Venkatraman Krishnan

Raghunath Ramakrishnan

Konstantinos Karanasos

Kwanghyun Park

Saeed Amizadeh

Fotios Psallidas

Ivan Popivanov

Doris Suiyi Xin

Karla Jean Saur

Byung-Gon Chun

Gyeongin Yu

The description relates to executing an inference query relative to a database management system, such as a relational database management system. In one example a trained machine learning model can be stored within the database management system. An inference query can be received that applies the trained machine learning model on data local to the database management system. Analysis can be performed on the inference query and the trained machine learning model to generate a unified intermediate representation of the inference query and the trained model. Cross optimization can be performed on the unified intermediate representation. Based upon the cross-optimization, a first portion of the unified intermediate representation to be executed by a database engine of the database management system can be determined, and, a second portion of the unified intermediate representation to be executed by a machine learning runtime can be determined.

Query processing with machine learning

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

Solutions for adapting machine learning (ML) models to neural networks (NNs) include receiving an ML pipeline comprising a plurality of operators; determining operator dependencies within the ML pipeline; determining recognized operators; for each of at least two recognized operators, selecting a corresponding NN module from a translation dictionary; and wiring the selected NN modules in accordance with the operator dependencies to generate a translated NN. Some examples determine a starting operator for translation, which is the earliest recognized operator having parameters. Some examples connect inputs of the translated NN to upstream operators of the ML pipeline that had not been translated. Some examples further tune the translated NN using backpropagation. Some examples determine whether an operator is trainable or non-trainable and flag related parameters accordingly for later training. Some examples determine whether an operator has multiple corresponding NN modules within the translation dictionary and make an optimized selection.

Neural adapter for classical machine learning (ML) models

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Kirkland, WA, United States of America

Markus Weimer