Microsoft Technology Licensing, LLC

Yael Dekel

Gary Shon Katzenberger

Thomas William Finley

Costin I Eseanu

Eric Anthony Erhardt

Mohammad Zeeshan Siddiqui

Ivan Matantsev

Petro Luferenko

Pete Luferenko

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

Embodiments provide a machine learning framework that enables developers to author and deploy machine learning pipelines into their applications regardless of the programming language in which the applications are structured. The framework may provide a programming language-specific API that enables the application to call a plurality of operators provided by the framework. The framework provides any number of APIs, each for a different programming language. The pipeline generated via the application is represented as an execution graph comprising node(s), where each node represents a particular operator. When a pipeline is submitted for execution, calls to the operators are detected, and nodes corresponding to the operators are generated for the execution graph. Once the execution graph is complete, the execution graph is provided to an execution engine, which reconstructs a corresponding execution graph that is structured in accordance with the operators' programming language and executes the reconstructed execution graph.

Optimized machine learning pipeline execution based on execution graphs

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Yael Dekel