Abstract: A system and method for optimization and validation of the machine learning tasks is proposed. The system allows for a graphical representation of the underlying parallel execution and allows the user the ability to determine the critical path of execution that will allow the system take advantage of processing capability of the available resources. The engine is capable of being aware of the machine learning task, its parallel execution constraints and the underlying heterogeneous infrastructure. This allows for optimal execution based on speed or reduced execution to comply with other constraints such as allowable time, costs, or other miscellaneous parameters. The disclosure also features a graphical user interface that displays the critical path on other instances besides computational workloads.

Abstract: A system and method for clustering machine learning workflows according to inclusion/exclusion criteria. The clustering is based on a plurality of information obtained from operators on the workflow, the position on the workflow of each operator and the data each operator is working on. The position of each operator on the workflow is obtained from its graph-based representation embedded on a coordinate system.

Abstract: A system and method of for providing assistance to complete machine learning on workflow engines that deal with machine learning flows comprising operators configured in a coordinate grid. The process analyzes the positions and composition of operators, branches, inconsistencies, collisions and redundancy in the workflow in order to suggest to the user which changes should be made to the workflow.

Abstract: A system and method for determining a candidate workflow from a cluster of similar workflows is presented. The process uses the differences classified as insertions of operators, deletions of operators, transpositions of operators and operator shifting in a parallel workflow to determine similarities in the workflow cluster and extract a candidate similar to the workflow in the comparison query. The extracted candidate workflow can then be used to suggest modifications to the workflow in the comparison query.

Abstract: A system and method of constructing a machine learning workflow by using machine learning suggestions derived from determining path lengths in a plurality of existing workflows, assigning a frequency threshold for each path and determining a probability for each path. This information is utilized to determine transpositions and deletions between paths that can be used as training for a machine learning algorithm that will suggest to the user which operators to put in a new machine learning workflow.

Abstract: A system and method of processing a machine learning flows by decomposing the flows on an x-y grid and extracting relevant information about their utilization on a particular category of machine learning workflow. This information is utilized to extract N-gram sequences that can be used as training for a machine learning algorithm that will suggest to the user which operator to put in a new machine learning workflow.

Abstract: A distributed machine learning optimization flow processing engine is proposed. The processing engine takes into account the structure of the programming to assign proper allocation within a distributed computing infrastructure. The processing engine also takes into account availability and loads of the different computing elements within the distributed infrastructure to maximize their utilization according to the software being executed.

Abstract: A distributed machine learning engine is proposed that allows for optimization and parallel execution of the machine learning tasks. The system allows for a graphical representation of the underlying parallel execution and allows the user the ability to select additional execution configurations that will allow the system to either take advantage of processing capability or to limit the available computing power. The engine is able to run from a single machine to a heterogeneous cloud of computing devices. The engine is capable of being aware of the machine learning task, its parallel execution constraints and the underlying heterogeneous infrastructure to allow for optimal execution based on speed or reduced execution to comply with other constraints such as allowable time, costs, or other miscellaneous parameters.

Abstract: A distributed machine learning optimization flow processing engine is proposed. The processing engine takes into account the structure of the programming to assign proper allocation within a distributed computing infrastructure. The processing engine also takes into account availability and loads of the different computing elements within the distributed infrastructure to maximize their utilization according to the software being executed.

Abstract: A system and method of generating a database schema from a graphical user interface used to create a form. The embodiments discloses the system that utilizes a drag and drop application that allows for configuration of a plurality of forms. These forms can then be placed in a graphical flow that will dictate the order of the forms. Through its graphical user interface, the system is able to gather information on field structure, flow among form elements, element identification, among other embodiments. This information allows the system to automate the creation of the database schema without user intervention.

Abstract: A distributed machine learning engine is proposed that allows for optimization and parallel execution of the machine learning tasks. The system allows for a graphical representation of the underlying parallel execution and allows the user the ability to select additional execution configurations that will allow the system to either take advantage of processing capability or to limit the available computing power. The engine is able to run from a single machine to a heterogeneous cloud of computing devices. The engine is capable of being aware of the machine learning task, its parallel execution constraints and the underlying heterogeneous infrastructure to allow for optimal execution based on speed or reduced execution to comply with other constraints such as allowable time, costs, or other miscellaneous parameters.

Abstract: A distributed machine learning engine is proposed that allows for optimization and parallel execution of the machine learning tasks. The system allows for a graphical representation of the underlying parallel execution and allows the user the ability to select additional execution configurations that will allow the system to either take advantage of processing capability or to limit the available computing power. The engine is able to run from a single machine to a heterogeneous cloud of computing devices. The engine is capable of being aware of the machine learning task, its parallel execution constraints and the underlying heterogeneous infrastructure to allow for optimal execution based on speed or reduced execution to comply with other constraints such as allowable time, costs, or other miscellaneous parameters.

Abstract: A distributed machine learning engine is proposed that allows for optimization and parallel execution of the machine learning tasks. The system allows for a graphical representation of the underlying parallel execution and allows the user the ability to select additional execution configurations that will allow the system to either take advantage of processing capability or to limit the available computing power. The engine is able to run from a single machine to a heterogeneous cloud of computing devices. The engine is capable of being aware of the machine learning task, its parallel execution constraints and the underlying heterogeneous infrastructure to allow for optimal execution based on speed or reduced execution to comply with other constraints such as allowable time, costs, or other miscellaneous parameters.

Abstract: System and method for creating enabling the user to select fields from a database, semi structured or unstructured documents that produces an automated process of joining the database tables, semi structured or unstructured documents into a feature vector that can be further processed by machine learning algorithms or preprocessing routines and filters. The full join performed starts by producing a graph representation of the links between data tables/documents and then restructuring the information into the most efficient join tree. The join tree then extracts the data in the form of a feature vector.