Microsoft Technology Licensing, LLC

Pete Luferenko

Gary Shon Katzenberger

Thomas William Finley

Eric Anthony Erhardt

Mohammad Zeeshan Siddiqui

Costin I Eseanu

Ivan Matantsev

Yael Dekel

Methods, systems, and computer products are herein provided for lazy evaluation of input data by a machine learning (ML) framework. An ML pipeline receives input data and compiles a chain of operators into a chain of dataviews configured for lazy evaluation of the input data. Each dataview in the chain represents a computation over data as a non-materialized view of the data. The ML pipeline receives a request for column data and selects a chain of delegates comprising one or more delegates for one or more dataviews in the chain to fulfill the request. The ML pipeline processes the input data with the selected chain of delegates. The ML pipeline performs delegate chaining on a dataview. A feature value for a feature column of the dataview is determined based on the delegate chaining and provided to an ML algorithm to predict column data.

Efficient streaming based lazily-evaluated machine learning framework

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Redmond, WA, United States of America

Pete Luferenko