Abstract: Disclosed herein are system, method, and computer program product embodiments for a method of cloud infrastructure optimization. The method identifies an existing infrastructure configuration deployed in a cloud environment and generates a plurality of proposal configurations, each of the plurality of proposal configurations having executable code configured to adjust the existing infrastructure configuration for at least one variable. The method selects a proposal configuration from the plurality of proposal configurations based on the at least one variable adjusted for in the existing infrastructure configuration, and the selected proposal configuration is deployed in the cloud environment. The method then analyzes the selected proposal configuration for a level of adjustment for the at least one variable. The method trains a model engine with existing and new training data.

Abstract: Disclosed herein are system, method, and computer program product embodiments for a method of cloud infrastructure optimization. The method identifies an existing infrastructure configuration deployed in a cloud environment and generates a plurality of proposal configurations, each of the plurality of proposal configurations having executable code configured to adjust the existing infrastructure configuration for at least one variable. The method selects a proposal configuration from the plurality of proposal configurations based on the at least one variable adjusted for in the existing infrastructure configuration, and the selected proposal configuration is deployed in the cloud environment. The method then analyzes the selected proposal configuration for a level of adjustment for the at least one variable. The method trains a model engine with existing and new training data.

Abstract: A system may be configured to perform a method for generating customized training. The system may receive first user interaction data associated with a user. The system may determine, using a machine learning model (MLM), whether the first user interaction data exceeds a predetermined threshold. Based on such determination, the system may assign a training module to the user. The system may access a user profile associated with the user, the user profile comprising a plurality of training modules. The system may generate a training plan based on the training module and the plurality of training modules. The system may receive second user interaction data associated with the user, and may determine an efficacy level of the training plan based on the second user interaction data. The system may dynamically update the training plan based on the efficacy level, and may dynamically display the training plan in the user profile.

Abstract: In order to facilitate automatic roll-forward updates, embodiments include detecting a failure in a software package. In response to the failure, data indicative of a current version of a software package is extracted from a release directory. In response to the application data, a base repository with a version history of the application is cloned. In response to the cloning, a prior set of changes from the base repository in the temporary directory, where the prior set of changes form an old application version of the application, is imported. In response to the import of the prior set of changes, the current version is overwritten with the prior set of changes to generate a new version. The new version is pushed directly to the base repository and a deployment API causes the new version to replace the current version.

Abstract: In order to facilitate automatic roll-forward updates, embodiments include detecting a failure in a software package. In response to the failure, data indicative of a current version of a software package is extracted from a release directory. In response to the application data, a base repository with a version history of the application is cloned. In response to the cloning, a prior set of changes from the base repository in the temporary directory, where the prior set of changes form an old application version of the application, is imported. In response to the import of the prior set of changes, the current version is overwritten with the prior set of changes to generate a new version. The new version is pushed directly to the base repository and a deployment API causes the new version to replace the current version.

Abstract: In order to facilitate automatic roll-forward updates, embodiments include detecting a failure in a software package. In response to the failure, data indicative of a current version of a software package is extracted from a release directory. In response to the application data, a base repository with a version history of the application is cloned. In response to the cloning, a prior set of changes from the base repository in the temporary directory, where the prior set of changes form an old application version of the application, is imported. In response to the import of the prior set of changes, the current version is overwritten with the prior set of changes to generate a new version. The new version is pushed directly to the base repository and a deployment API causes the new version to replace the current version.

Abstract: In order to facilitate automatic roll-forward updates, embodiments include detecting a failure in a software package. In response to the failure, data indicative of a current version of a software package is extracted from a release directory. In response to the application data, a base repository with a version history of the application is cloned. In response to the cloning, a prior set of changes from the base repository in the temporary directory, where the prior set of changes form an old application version of the application, is imported. In response to the import of the prior set of changes, the current version is overwritten with the prior set of changes to generate a new version. The new version is pushed directly to the base repository and a deployment API causes the new version to replace the current version.