# US 2021/0241168 A1 · Machine Learning Lifecycle Management > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0241168 A1 - Document ID: US20210241168A1 - Status: Published application - Kind: A1 - Application number: 16/782,512 - Filed: 2020-02-05 - Published: 2021-08-05 - Earliest claimed: 2020-02-05 - Assignees: Sap SE - Applicants: Sap SE - Inventors: Siar Sarferaz - Web page: https://patentgrep.com/patent/US20210241168A1 ## CPC - G06N 20/00: Machine learning - G06F 9/5061: {Partitioning or combining of resources} - G06F 9/54: Interprogram communication - G06F 16/27: Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## 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. ## Claims ### Claim 1 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; 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. ### Claim 2 (depends on 1) The method of claim 1, wherein the transport request is deployed using a transport repository of a database system that publishes ML content from the ML application. ### Claim 3 (depends on 1) The method of claim 1, further comprising deploying, by the second database, the ML application. ### Claim 4 (depends on 1) The method of claim 1, further comprising selecting relevant objects for the transport request. ### Claim 5 (depends on 1) The method of claim 1, wherein the transport request comprises the ML application, an intelligent scenario, a core data services (CDS) view, and a high-level programming language class. ### Claim 6 (depends on 1) The method of claim 1, wherein the package is stored in a data intelligence development repository. ### Claim 7 (depends on 1) The method of claim 1, wherein the first database is a provider database and the second database is a consumer database. ### Claim 8 (depends on 1) The method of claim 1, wherein the second database is an in-memory database and wherein the data intelligence platform and the in-memory database form a side-by-side ML architecture. ### Claim 9 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; 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. ### Claim 10 (depends on 9) The system of claim 9, wherein the transport request is deployed using a transport repository of a database system that publishes ML content from the ML application. ### Claim 11 (depends on 9) The system of claim 9, wherein the operations further comprise deploying, by the second database, the ML application. ### Claim 12 (depends on 9) The system of claim 9, wherein the operations further comprise selecting relevant objects for the transport request. ### Claim 13 (depends on 9) The system of claim 9, wherein the transport request comprises the ML application, an intelligent scenario, a core data services (CDS) view, and a high-level programming language class. ### Claim 14 (depends on 9) The system of claim 9, wherein the package is stored in a data intelligence development repository. ### Claim 15 (depends on 9) The system of claim 9, wherein the first database is a provider database and the second database is a consumer database. ### Claim 16 (depends on 9) The system of claim 9, wherein the second database is an in-memory database and wherein the data intelligence platform and the in-memory database form a side-by-side ML architecture. ### Claim 17 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 the ML application; 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. ### Claim 18 (depends on 17) The non-transitory computer program product of claim 17, wherein (i) the transport request is deployed using a transport repository of a database system that publishes ML content from the ML application, (ii) the transport request comprises the ML application, an intelligent scenario, a core data services (CDS) view, and a high-level programming language class, (iii) the package is stored in a data intelligence development repository, and (iv) the first database is a provider database and the second database is a consumer database. ### Claim 19 (depends on 17) The non-transitory computer program product of claim 17, further comprising: deploying, by the second database, the ML application; and selecting relevant objects for the transport request. ### Claim 20 (depends on 17) The non-transitory computer program product of claim 17, wherein the second database is an in-memory database and wherein the data intelligence platform and the in-memory database form a side-by-side ML architecture.