Invention Grant
- Patent Title: Efficient streaming based lazily-evaluated machine learning framework
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Application No.: US16661131Application Date: 2019-10-23
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Publication No.: US11609746B2Publication Date: 2023-03-21
- Inventor: Gary Shon Katzenberger , Thomas William Finley , Pete Luferenko , Mohammad Zeeshan Siddiqui , Costin Eseanu , Eric Anthony Erhardt , Yael Dekel , Ivan Matantsev
- Applicant: Microsoft Technology Licensing, LLC
- Applicant Address: US WA Redmond
- Assignee: Microsoft Technology Licensing, LLC
- Current Assignee: Microsoft Technology Licensing, LLC
- Current Assignee Address: US WA Redmond
- Agency: Weaver IP L.L.C.
- Main IPC: G06F16/00
- IPC: G06F16/00 ; G06F8/30 ; G06N20/00 ; G06F16/2455 ; G06F16/2453 ; G06F9/30 ; G06F9/50 ; G06F9/54

Abstract:
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.
Public/Granted literature
- US20200349469A1 EFFICIENT STREAMING BASED LAZILY-EVALUATED MACHINE LEARNING FRAMEWORK Public/Granted day:2020-11-05
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