Machine learning lifecycle management
AssigneeSap SE
InventorSiar Sarferaz
Life of this patent
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Abstract
Systems, methods, and computer program products are described herein for managing a lifecycle of a machine learning (ML) application from a provider point of view. Within a data intelligence platform, a package having ML scenarios and a training pipeline is generated. The training pipeline includes training logic associated with a defined workflow for training the ML application. The data intelligence platform is synchronized with a first database via an application programming interface. The first database generates a transport request containing the package. The transport request facilitates publication of content from the ML application. The ML application is assembled from the transport request within a second database. ML content is displayed on a graphical user interface associated with the second database.
Claims20
A method for managing a lifecycle of a machine learning (ML) application from a provider point of view, the method implemented by one or more data processors forming part of at least one computing device and comprising:
- generating, within a data intelligence platform, a package comprising ML scenarios and a training pipeline, wherein the training pipeline comprises training logic associated with a defined workflow for training the ML application, wherein the training logic comprises logic to execute the ML scenarios, wherein the package comprises an inference pipeline that defines logic for transformations associated with an inference generated by the ML scenarios;
- synchronizing, via an application programming interface, the data intelligence platform with a first database;
- generating, by the first database, a transport request comprising the package, the transport request facilitating publication of content from the ML application;
- assembling the ML application from the transport request within a second database;
- and
- displaying ML content on a graphical user interface associated with the second database.
A system for managing a lifecycle of a machine learning (ML) application from a provider point of view, the system comprising:
- one or more data processors;
- and
- memory storing instructions stored on at least one of the one or more data processors, which when executed, results in operations comprising:
- generating, within a data intelligence platform, a package comprising ML scenarios and a training pipeline, wherein the training pipeline comprises training logic associated with a defined workflow for training the ML application, wherein the training logic comprises logic to execute the ML scenarios, wherein the package comprises an inference pipeline that defines logic for transformations associated with an inference generated by the ML scenarios;
- synchronizing, via an application programming interface, the data intelligence platform with a first database;
- generating, by the first database, a transport request comprising the package, the transport request facilitating publication of content from the ML application;
- assembling the ML application from the transport request within a second database;
- and
- displaying ML content on a graphical user interface associated with the second database.
A non-transitory computer program product storing instructions which, when executed by at least one data processor forming part of at least one computing device, implement operations comprising:
- generating, within a data intelligence platform, a package comprising ML scenarios and a training pipeline, wherein the training pipeline comprises training logic associated with a defined workflow for training an ML application, wherein the training logic comprises logic to execute the ML scenarios, wherein the package comprises an inference pipeline that defines logic for transformations associated with an inference generated by the ML scenarios;
- synchronizing, via an application programming interface, the data intelligence platform with a first database;
- generating, by the first database, a transport request comprising the package, the transport request facilitating publication of content from the ML application;
- assembling the ML application from the transport request within a second database;
- and
- displaying ML content on a graphical user interface associated with the second database;
- wherein the training pipeline and the inference pipeline are exposed by application services wrapped by a high-level programming language class in a side-by-side machine learning architecture.
Dependencies are inferred from the claim text.