Abstract: Systems and methods for managing software resources such as machine learning/artificial intelligence models. The system includes a number of components that provide versioning services to both software resources and to data sets. A data storage component also addresses storage/retrieval functions for the system. A user interface element is also present to provide users with both a GUI and a command line interface. An assessment element assesses user selected software assets for project inclusion, with the assessment being based on good software development principles and practices. Once a project of selected software assets has passed the assessment stage, the software assets are pipelined for execution by an execution unit. The outputs of the execution unit are sent to a data output module automatically populates predetermined and preconfigured templates with the outputs to produce reports. The outputs may be final outputs or they may be intermediate results produced by the software assets.

Abstract: Systems and methods for adjusting operating parameters of at least one pipelined software asset. Within a pipelined environment, an agent software asset is inserted immediately preceding a software asset whose operating parameters are to be adjusted. The agent software asset receives data and/or data sets from a user and such data and data sets are inserted/used by the software asset. The agent software asset also allows for a reporting of the output of other software assets to thereby provide users with intermediate outputs from the pipelined environment.

Abstract: A method includes receiving a plurality of user use cases; analyzing the use cases using an AI engine to order the use cases; generating an optimized machine learning model; and causing an optimized deployment option to be displayed. A computing system includes a processor; and a memory comprising instructions, that when executed, cause the computing system to: receive a plurality of user use cases; analyze the use cases using an AI engine to order the use cases; generate an optimized machine learning model; and cause an optimized deployment option to be displayed. A non-transitory computer-readable storage medium stores executable instructions that, when executed by a processor, cause a computer to: receive a plurality of user use cases; analyze the use cases using an AI engine to order the use cases; generate an optimized machine learning model; and cause an optimized deployment option to be displayed.

Abstract: A method includes receiving first input, analyzing the first input using a first model, receiving second input, analyzing the second input using a second model; and generating infrastructure-as-code. A computing system includes a processor; and a memory comprising instructions, that when executed, cause the computing system to: receive first input, analyze the first input using a first model, receive second input, analyze the second input using a second model; and generate infrastructure-as-code. A non-transitory computer-readable storage medium storing executable instructions that, when executed by a processor, cause a computer to: receive first input, analyze the first input using a first model, receive second input, analyze the second input using a second model; and generate infrastructure-as-code.

Abstract: As organizations scale analytics, a challenge is the huge increase in number of analytics pipelines to be maintained by developers. In here, lies an example method and modular software architecture to assemble analytics pipeline from reusable components. (1) Reusable components can be utilities where each utility is versioned and has a collection of programming functions, also known as nodes, that follow “data-in-data-out” convention. As nodes are building blocks of a pipeline, an assembly instruction and human readable configurations can now be created to define a project pipeline from a repository of pipelines and well-versioned utilities. (2) By separating out re-usable functionalities from pipelines and data layers, this layered architecture can now support upgrades of functionalities overtime without re-writing of pipelines.

Abstract: A method includes receiving user inputs; receiving codified knowledge management information; receiving engine data; and processing the user inputs, the codified knowledge management information and engine data using a trained machine learning model to generate a living document. A computing system includes one or more processors; and a memory comprising instructions that, when executed, cause the computing system to: receive user inputs; receive codified knowledge management information; receive engine data; and process the user inputs, the codified knowledge management information and engine data using a trained machine learning model to generate a living document.

Abstract: A method includes receiving first input, analyzing the first input using a first model, receiving second input, analyzing the second input using a second model; and generating infrastructure-as-code. A computing system includes a processor; and a memory comprising instructions, that when executed, cause the computing system to: receive first input, analyze the first input using a first model, receive second input, analyze the second input using a second model; and generate infrastructure-as-code. A non-transitory computer-readable storage medium storing executable instructions that, when executed by a processor, cause a computer to: receive first input, analyze the first input using a first model, receive second input, analyze the second input using a second model; and generate infrastructure-as-code.

Abstract: A computer-implemented method for automatically updating work schedules includes distributing a first plurality of work schedules to a work crew. The method further includes receiving productivity updates from the work crew, the productivity updates indicative of an actual amount of work accomplished by the work crew in the first time period, and, based on one or more differences between the actual amount of work and the estimated amount of work, updating the one or more initial estimates of productivity. Still further, the method includes causing a schedule generator to generate a second plurality of work schedules based on the one or more updated estimates of productivity, and distributing the second plurality of work schedules to the work crew.

Abstract: A parallel manufacturing system for use in a manufacturing plant that utilizes a massively parallel computer determines the scheduling of all manufacturing operations used in connection with the manufacture of the products made in the plant. The system utilizes an explosion procedure to determine the net requirements for all components used in the manufacturing operations, and then schedules any manufacturing operations necessary to make the required components.