Microsoft Technology Licensing, LLC

Ivan Matantsev

Gary Shon Katzenberger

Costin I Eseanu

Eric Anthony Erhardt

Thomas William Finley

Mohammad Zeeshan Siddiqui

Petro Luferenko

Yael Dekel

Pete Luferenko

Sparse data handling and/or buffer sharing are implemented. Data may be buffered in reusable buffer arrays. Data may comprise fixed or variable length vectors, which may be represented as sparse or dense vectors in a values array and indices array. Data may be materialized from a dataview comprising a non-materialized view of data in a machine-learning pipeline by cursoring over rows of the dataview and calling delegate functions to compute data for rows in an active column. A buffer and/or its set of arrays storing a first vector may be reused for a second and additional vectors, for example, when the length of buffer arrays is equal to or greater than the length of the second and additional vectors, which may be selectively stored as sparse or dense vectors to fit the array set. Shared buffers may be passed as references between delegate functions for reuse.

Sparse data handling and buffer sharing to reduce memory allocation and reclamation

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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Bothell, WA, United States of America

Ivan Matantsev