Abstract: Various embodiments herein each include at least one of systems, methods, and software for instantiating, executing, and operating dynamic hybrid computing environments, such as in cloud computing. Some such embodiments include allocating computing resources of a first server cluster to instantiate a first cluster and to establish a computing session. This embodiment may then initiate execution of a program within the first cluster that offloads at least one computing task to a second cluster, when the second cluster is instantiated, to leverage high-computing speed performance capabilities of the second cluster with regard to certain computing operations. Upon completion of program execution, the second cluster is then deallocated.

Abstract: Various embodiments herein each include at least one of systems, methods, and software for instantiating, executing, and operating dynamic hybrid computing environments, such as in cloud computing. Some such embodiments include allocating computing resources of a first server cluster to instantiate a first cluster and to establish a computing session. This embodiment may then initiate execution of a program within the first cluster that offloads at least one computing task to a second cluster, when the second cluster is instantiated, to leverage high-computing speed performance capabilities of the second cluster with regard to certain computing operations. Upon completion of program execution, the second cluster is then deallocated.

Abstract: An interoperable platform that provides a way to automatically compose and execute even complex workflows without writing code is described. A set of pre-built functional building blocks can be provided. The building blocks perform data transformation and machine learning functions. The functional blocks have few well known plug types. The building blocks can be composed to build complex compositions. Interoperability between data formats, metadata schema and interfaces to machine learning (ML) functions and trained machine learning models can be provided with no loss of information. A cloud runtime environment can be provided in which the composed workflows can be hosted as REST API to run in production.

Abstract: An interoperable platform that provides a way to automatically compose and execute even complex workflows without writing code is described. A set of pre-built functional building blocks can be provided. The building blocks perform data transformation and machine learning functions. The functional blocks have few well known plug types. The building blocks can be composed to build complex compositions. Interoperability between data formats, metadata schema and interfaces to machine learning (ML) functions and trained machine learning models can be provided with no loss of information. A cloud runtime environment can be provided in which the composed workflows can be hosted as REST API to run in production.