Abstract: A method for exposing artificial intelligence content as a cloud service may include onboarding, by a service broker of a core platform hosting an artificial intelligence (AI) resource, a service provider tenant providing the artificial intelligence resource. The onboarding of the first service provider tenant includes creating, at the core platform, a function specific service broker associated with the artificial intelligence resource. The function specific service broker may then onboard one or more service consumer tenants for accessing the artificial intelligence resource associated with the first provider tenant. Moreover, in response to the one or more service consumer tenants accessing the artificial intelligence resource, the function specific service broker may authenticate the one or more service consumer tenants and meter a usage of the artificial intelligence resource by the one or more service consumer tenants. Related methods and computer program products are also disclosed.

Abstract: An AI platform mesh includes a machine learning control plane hosted in a container orchestration cluster. The machine learning control plane includes a worker plane manager and a set of machine learning services. One or more remote runtime environments configured to execute machine learning workloads can be registered with the machine learning control plane via the worker plane manager. The remote runtime environments can communicate with the set of machine learning services via the worker plane manager.

Abstract: Methods, systems, and computer-readable storage media for providing a DLS-enriched view on data stored in a database system, the DLS-enriched view including a set of annotations indicating at least one application table and at least one field of the application table, storing a staging table corresponding to the application table field and a database trigger that is configured to write data from the field to the staging table in response to a change in the at least one application table, providing at least one IR artifact based on the DLS-enriched view, generating at least one DLS-specific entity and chaincode based on the IR artifact and a configuration for a DLS, to which the chaincode is to be deployed, deploying the chaincode to the DLS, and monitoring the one or more staging tables for changes and, in response to a change, recording data of the staging table to the DLS.

Abstract: Various embodiments of systems and methods to provide a knowledge driven solution inference are described herein. In one aspect, unstructured data is retrieved from one or more information sources. Data segments corresponding to a plurality of categories are identified in the extracted unstructured data by natural language processing. Further, the data segments are grouped into a plurality of data clusters based on scores between the data segments. The structured knowledge base is generated by linking the associated plurality of data clusters. The knowledge driven solution inference is provided based on the generated knowledge base.

Abstract: Various embodiments of systems and methods to provide a knowledge driven solution inference are described herein. In one aspect, unstructured data are retrieved from one or more information sources. Data segments corresponding to a plurality of categories are identified in the extracted unstructured data by natural language processing. Further, the data segments are grouped into a plurality of data clusters based on scores between the data segments. The structured knowledge base is generated by linking the associated plurality of data clusters. The knowledge driven solution inference is provided based on the generated knowledge base.