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AssigneeMicrosoft Technology Licensing, LLC
InventorsGary Shon Katzenberger, Thomas William Finley, Pete Luferenko, Mohammad Zeeshan Siddiqui, Costin Eseanu, Eric Anthony Erhardt, Yael Dekel, Ivan Matantsev
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An efficient, streaming-based, lazily-evaluated machine learning (ML) framework is provided. An ML pipeline of operators produce and consume a chain of dataviews representing a computation over data. Non-materialized (e.g., virtual) views of data in dataviews permit efficient, lazy evaluation of data on demand regardless of size (e.g., in excess of main memory). Data may be materialized by DataView cursors (e.g., movable windows over rows of an input dataset or DataView). Computation and data movement may be limited to rows for active columns without processing or materializing unnecessary data. A chain of dataviews may comprise a chain of delegates that reference a chain of functions. Assembled pipelines of schematized compositions of operators may be validated and optimized with efficient execution plans. A compiled chain of functions may be optimized and executed in a single call. Dataview based ML pipelines may be developed, trained, evaluated and integrated into applications.
A method for lazy evaluation of input data by a machine learning (ML) pipeline comprising a chain of dataviews, where a dataview represents a computation over data as a non-materialized view of the data, the method comprising:
A computing device, comprising:
A computer-readable storage medium having program instructions recorded thereon that, when executed by a processing circuit, perform a method comprising:
Dependencies are inferred from the claim text.