Abstract: Methods, systems, and computer-readable storage media for automatic creation of distributed ledger networks. A request to deploy a distributed ledger network is received by an automation framework. The request comprising a configuration file and is associated with a platform type for one or more platforms for deployment of the distributed ledger network. Configuration prerequisites are determined for performing installations of software components on the one or more platforms. In response to installing the software components, the software components are configured and executed to set up an environment for deploying the distributed ledger network by creating a build folder, copying the configuration file and key files for accessing a source code repository into the build folder, and updating the configuration file according to criteria associated with the platform type.

Abstract: Methods, systems, and computer-readable storage media for automatic creation of distributed ledger networks. A request to deploy a distributed ledger network is received by an automation framework. The request comprising a configuration file and is associated with a platform type for one or more platforms for deployment of the distributed ledger network. Configuration prerequisites are determined for performing installations of software components on the one or more platforms. In response to installing the software components, the software components are configured and executed to set up an environment for deploying the distributed ledger network by creating a build folder, copying the configuration file and key files for accessing a source code repository into the build folder, and updating the configuration file according to criteria associated with the platform type.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for system modernization using machine learning are disclosed. In one aspect, a method includes the actions of generating training data. The actions include generating a first model, a second model, and a third model. The actions include receiving data that is related to technological capabilities of an application and data that is related to business priorities of the application. The actions include applying the first model to the data that is related to the technological capabilities of the application and the second model to the data that is related to business priorities of the application. The actions include generating a modification recommendation for the application. The actions further include providing, for output, the modification recommendation for the application. The actions include receiving feedback data that indicates a level of acceptance of the modification recommendation for the application.

Abstract: A device receives application information associated with a monolithic application, and generates a recommendation based on utilizing an artificial intelligence technique. The recommendation relates to a service to be generated, a service category for the service, and a deployment model for the service. The artificial intelligence technique generates the recommendation based on the application information. The device automatically generates code for the service based on the service category and the application information, receives a request to deploy the generated code for the service via the deployment model, and deploys the generated code, based on the request, to provide the service via the deployment model.

Abstract: A device receives application information associated with a monolithic application, and generates a recommendation based on utilizing an artificial intelligence technique. The recommendation relates to a service to be generated, a service category for the service, and a deployment model for the service. The artificial intelligence technique generates the recommendation based on the application information. The device automatically generates code for the service based on the service category and the application information, receives a request to deploy the generated code for the service via the deployment model, and deploys the generated code, based on the request, to provide the service via the deployment model.

Abstract: An example device may include one or more processors to receive an input associated with developing an application; determine a feature that may be included in the application based on the input; select an application programming interface (API) from an API repository, where the API may be associated with the feature of the application; select a user interface (UI) to facilitate user interaction with the application based on the API; and/or perform an action associated with developing the application.

Abstract: A device receives application information associated with a monolithic application, and generates a recommendation based on utilizing an artificial intelligence technique. The recommendation relates to a service to be generated, a service category for the service, and a deployment model for the service. The artificial intelligence technique generates the recommendation based on the application information. The device automatically generates code for the service based on the service category and the application information, receives a request to deploy the generated code for the service via the deployment model, and deploys the generated code, based on the request, to provide the service via the deployment model.

Abstract: An example device may include one or more processors to receive an input associated with developing an application; determine a feature that may be included in the application based on the input; select an application programming interface (API) from an API repository, where the API may be associated with the feature of the application; select a user interface (UI) to facilitate user interaction with the application based on the API; and/or perform an action associated with developing the application.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for system modernization using machine learning are disclosed. In one aspect, a method includes the actions of generating training data. The actions include generating a first model, a second model, and a third model. The actions include receiving data that is related to technological capabilities of an application and data that is related to business priorities of the application. The actions include applying the first model to the data that is related to the technological capabilities of the application and the second model to the data that is related to business priorities of the application. The actions include generating a modification recommendation for the application. The actions further include providing, for output, the modification recommendation for the application. The actions include receiving feedback data that indicates a level of acceptance of the modification recommendation for the application.