Abstract: A method comprises collecting usage data for a plurality of automated resources integrated in a platform, computing a utilization score for one or more automated resources of the plurality of automated resources based at least in part on the usage data, and predicting a future utilization for the one or more automated resources using one or more machine learning algorithms. Integration of the one or more automated resources in the platform is controlled based at least in part on one or more of the utilization score and the future utilization.

Abstract: A method comprises receiving a request for validation of software comprising one or more applications, analyzing the request and generating one or more validation steps based at least in part on the analysis. In the method, a time to complete the one or more validation steps is predicted. The predicting is performed using one or more machine learning models, and is based at least in part on a type and a number of the one or more applications.

Abstract: A method comprises collecting data corresponding to a plurality of components in a system, wherein the data comprises information about one or more issues with the plurality of components. The data is analyzed and categorized based at least in part on the analysis. In the method, one or more application programming interfaces (APIs) are selected to monitor respective statuses of the plurality of components, wherein the selection is based at least in part on the categorization of the data, and the data is pushed to the one or more APIs.

Abstract: A method comprises receiving a request for validation of software comprising one or more applications, analyzing the request and generating one or more validation steps based at least in part on the analysis. In the method, a time to complete the one or more validation steps is predicted. The predicting is performed using one or more machine learning models, and is based at least in part on a type and a number of the one or more applications.

Abstract: A method includes receiving a natural language query requesting data from a message oriented middleware infrastructure comprising a plurality of message oriented middleware providers, and analyzing the natural language query to determine one or more types of the data being requested. In the method, one or more stored queries corresponding to the determined one or more types of the data are identified. The one or more stored queries are in native command formats corresponding to respective ones of the plurality of message oriented middleware providers. The method also includes executing the identified one or more stored queries in the native command formats to retrieve the data from the plurality of message oriented middleware providers, and providing a response to the natural language query based on the retrieved data to a user via a user interface.

Abstract: A method includes monitoring and logging a plurality of transactions between one or more clients and an application programming interface gateway, and analyzing data corresponding to the plurality of transactions using one or more machine learning techniques. The method further includes determining, based on the analyzing, one or more issues corresponding to one or more application programming interfaces associated with the application programming interface gateway and resulting from one or more of the plurality of transactions. In the method, one or more corrective actions are performed to address the one or more issues.

Abstract: A method includes receiving a natural language query requesting data from a message oriented middleware infrastructure comprising a plurality of message oriented middleware providers, and analyzing the natural language query to determine one or more types of the data being requested. In the method, one or more queries corresponding to the determined one or more types of the data are dynamically generated. The one or more queries are in native command formats corresponding to respective ones of the plurality of message oriented middleware providers. The method also includes executing the one or more queries in the native command formats to retrieve the data from the plurality of message oriented middleware providers, and providing a response to the natural language query based on the retrieved data to a user via a user interface.

Abstract: A method comprises managing multiple tasks of multiple entities associated with a deployment of a software program with a deployment framework comprising a machine learning module configured to assist with managing the multiple tasks of the multiple entities. The managing step comprises tracking a status of one or more of the multiple tasks, and predicting a time taken for a given one of the multiple entities to complete a given one of the multiple tasks.

Abstract: An information handling system includes a repository that stores a pre-production validation suite and a production validation suite. The pre-production validation suite includes first validation factors, and the production validation suite includes second validation factors. A processor may deploy an application in a pre-production environment, and validate the application in the pre-production environment using the pre-production validation suite. If the application passes the pre-production validation suite, then the processor may deploy the application in a production environment. The processor also may validate the application in the production environment using the production validation suite, assign a score associated with each one of the first validation factors and each one of the second validation factors, and generate a report based on the score associated with each one of the first validation factors and each one of the second validation factors.

Abstract: A method includes retrieving vendor specific data from one or more message oriented middleware servers of a message oriented middleware infrastructure, and inputting the vendor specific data from the one or more message oriented middleware servers into a back-end database. The vendor specific data is converted into commonly formatted data, and the commonly formatted data is inputted into a front-end database. The method also includes retrieving the commonly formatted data from the front-end database, and displaying the commonly formatted data on a user interface providing a visualization of a topology of the message oriented middleware infrastructure.

Abstract: An information handling system includes a repository that stores a pre-production validation suite and a production validation suite. The pre-production validation suite includes first validation factors, and the production validation suite includes second validation factors. A processor may deploy an application in a pre-production environment, and validate the application in the pre-production environment using the pre-production validation suite. If the application passes the pre-production validation suite, then the processor may deploy the application in a production environment. The processor also may validate the application in the production environment using the production validation suite, assign a score associated with each one of the first validation factors and each one of the second validation factors, and generate a report based on the score associated with each one of the first validation factors and each one of the second validation factors.

Abstract: A method includes receiving a natural language query requesting data from a message oriented middleware infrastructure comprising a plurality of message oriented middleware providers, and analyzing the natural language query to determine one or more types of the data being requested. In the method, one or more queries corresponding to the determined one or more types of the data are dynamically generated. The one or more queries are in native command formats corresponding to respective ones of the plurality of message oriented middleware providers. The method also includes executing the one or more queries in the native command formats to retrieve the data from the plurality of message oriented middleware providers, and providing a response to the natural language query based on the retrieved data to a user via a user interface.

Abstract: A method includes receiving a natural language query requesting data from a message oriented middleware infrastructure comprising a plurality of message oriented middleware providers, and analyzing the natural language query to determine one or more types of the data being requested. In the method, one or more stored queries corresponding to the determined one or more types of the data are identified. The one or more stored queries are in native command formats corresponding to respective ones of the plurality of message oriented middleware providers. The method also includes executing the identified one or more stored queries in the native command formats to retrieve the data from the plurality of message oriented middleware providers, and providing a response to the natural language query based on the retrieved data to a user via a user interface.

Abstract: A method includes monitoring and logging a plurality of transactions between one or more clients and an application programming interface gateway, and analyzing data corresponding to the plurality of transactions using one or more machine learning techniques. The method further includes determining, based on the analyzing, one or more issues corresponding to one or more application programming interfaces associated with the application programming interface gateway and resulting from one or more of the plurality of transactions. In the method, one or more corrective actions are performed to address the one or more issues.

Abstract: A method comprises managing multiple tasks of multiple entities associated with a deployment of a software program with a deployment framework comprising a machine learning module configured to assist with managing the multiple tasks of the multiple entities. The managing step comprises tracking a status of one or more of the multiple tasks, and predicting a time taken for a given one of the multiple entities to complete a given one of the multiple tasks.

Abstract: An apparatus in one embodiment comprises at least one processing platform comprising a plurality of processing devices. The processing platform is configured to abstract a plurality of partner platforms and a plurality of enterprise applications to extract a plurality of connectivity parameters associated with respective ones of the partner platforms and the enterprise applications, to manage connections between the partner platforms and the enterprise applications by implementing connectivity parameters, wherein the implementing provides the partner platforms with centralized access to the enterprise applications via a broker layer, to select one or more of the connectivity parameters to be used in connection with routing of data between a given partner platform and a given enterprise application, and to route the data between the given partner platform and the given enterprise application via the broker layer using the selected one or more connectivity parameters.

Abstract: A method includes retrieving vendor specific data from one or more message oriented middleware servers of a message oriented middleware infrastructure, and inputting the vendor specific data from the one or more message oriented middleware servers into a back-end database. The vendor specific data is converted into commonly formatted data, and the commonly formatted data is inputted into a front-end database. The method also includes retrieving the commonly formatted data from the front-end database, and displaying the commonly formatted data on a user interface providing a visualization of a topology of the message oriented middleware infrastructure.

Abstract: An apparatus in one embodiment comprises at least one processing platform comprising a plurality of processing devices. The at least one processing platform is configured to provide a plurality of applications with centralized access to a plurality of message oriented middleware (MOM) servers via a connectivity layer, to establish a connection between a given one of the plurality of applications and a given one of the plurality of MOM servers via the connectivity layer, and to exchange data between the given one of the plurality of applications and the given one of the plurality of MOM servers via the connectivity layer.

Abstract: An apparatus in one embodiment comprises at least one processing platform comprising a plurality of processing devices. The at least one processing platform is configured to provide a plurality of applications with centralized access to a plurality of message oriented middleware (MOM) servers via a connectivity layer, to establish a connection between a given one of the plurality of applications and a given one of the plurality of MOM servers via the connectivity layer, and to exchange data between the given one of the plurality of applications and the given one of the plurality of MOM servers via the connectivity layer.

Abstract: An apparatus in one embodiment comprises at least one processing platform comprising a plurality of processing devices. The processing platform is configured to abstract a plurality of partner platforms and a plurality of enterprise applications to extract a plurality of connectivity parameters associated with respective ones of the partner platforms and the enterprise applications, to manage connections between the partner platforms and the enterprise applications by implementing connectivity parameters, wherein the implementing provides the partner platforms with centralized access to the enterprise applications via a broker layer, to select one or more of the connectivity parameters to be used in connection with routing of data between a given partner platform and a given enterprise application, and to route the data between the given partner platform and the given enterprise application via the broker layer using the selected one or more connectivity parameters.