Support For Externally Trained Machine Learning Model In A Relational Database
AssigneeOracle International Corporation
InventorsLaurent Phillipe Daynes, Lukas Gelbmann
Life of this patent
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Abstract
A database system includes functionality to import externally trained models, which may be converted into a standard interchange format as first-class objects in a relational database. The database system also provides support for integrating inference generation using externally trained machine learning models with database queries or relational statements. The database system includes a machine learning runtime engine for invoking the imported ML models to generate inferences without exporting database data between the database system and an external inference engine.
Claims20
A method comprising:
- defining a trained machine learning (ML) model in a database dictionary of a database system, wherein defining the trained ML model results in creating a named database object that is a first-class citizen of the database system;
- compiling, by a database system, a statement comprising an operator, wherein:
- the operator references an identifier of a trained machine learning (ML) model, and
- the operator includes one or more input attributes that correspond to the trained ML model;
- executing, by the database system, the statement, wherein executing the statement comprises:
- running, by a machine learning runtime engine within the database system, at least one inference of the trained ML model using the one or more input attributes as inputs to the trained ML model;
- passing at least one result returned by the at least one inference of the trained ML model to the operator;
- and
- executing, by the database system, the operator using the at least one result returned by the at least one inference of the trained ML model,
- wherein the method is performed by one or more computing devices.
One or more non-transitory computer-readable media storing instructions which, when executed by one or more processors, cause performance of:
- defining a trained machine learning (ML) model in a database dictionary of a database system, wherein defining the trained ML model results in creating a named database object that is a first-class citizen of the database system;
- compiling, by a database system, a statement comprising an operator, wherein:
- the operator references an identifier of a trained machine learning (ML) model, and
- the operator includes one or more input attributes that correspond to the trained ML model;
- executing, by the database system, the statement, wherein executing the statement comprises:
- running, by a machine learning runtime engine within the database system, at least one inference of the trained ML model using the one or more input attributes as inputs to the trained ML model;
- passing at least one result returned by the at least one inference of the trained ML model to the operator;
- and
- executing, by the database system, the operator using the at least one result returned by the at least one inference of the trained ML model,
- wherein the method is performed by one or more computing devices.
Dependencies are inferred from the claim text.