Abstract: Systems and methods for generating generative AI based solution are disclosed. A system receives a request for generating the generative AI (GenAI) based solution. The system classifies the received request into solution patterns and performs actions corresponding to at least one of the solution patterns. The system extracts a metadata from the received request and the actions based on the type of GenAI based solution to be generated. Further, the system segments the extracted metadata into data segments and generates a vector representation of the data segments. The system generates the GenAI based solution corresponding to the received request based on the generated vector representation. The system validates the GenAI based solution large language model (LLM). The system continuously updates the LLM and the vector-based machine learning model with the validated GenAI based solution and user feedback. The system outputs the GenAI based solution on a user interface.

Abstract: In some implementations, a scheduling platform may receive task information regarding a set of tasks for execution using a set of computing resources, wherein the task information includes, for the set of tasks, at least one of: a run time parameter, a priority parameter, or a success rate parameter. The scheduling platform may communicate with a computing resource management device to obtain first computing resource information regarding the set of computing resources. The scheduling platform may generate a first assignment of the set of tasks to the set of computing resources. The scheduling platform may transmit assignment information identifying the first assignment. The scheduling platform may receive second computing resource information. The scheduling platform may generate a second assignment of the set of tasks to the set of computing resources. The scheduling platform may transmit second assignment information identifying the second assignment.

Abstract: Computer-implemented methods and systems are disclosed for receiving and indexing a plurality of files for later querying, for dynamically generating scripts to be executed during a query of a data store, and for horizontally distributing a query and aggregating results of the distributed query.

Abstract: In some implementations, a scheduling platform may receive task information regarding a set of tasks for execution using a set of computing resources, wherein the task information includes, for the set of tasks, at least one of: a run time parameter, a priority parameter, or a success rate parameter. The scheduling platform may communicate with a computing resource management device to obtain first computing resource information regarding the set of computing resources. The scheduling platform may generate a first assignment of the set of tasks to the set of computing resources. The scheduling platform may transmit assignment information identifying the first assignment. The scheduling platform may receive second computing resource information. The scheduling platform may generate a second assignment of the set of tasks to the set of computing resources. The scheduling platform may transmit second assignment information identifying the second assignment.

Abstract: A device may obtain first data relating to a machine learning model. The device may pre-process the first data to alter the first data to generate second data. The device may process the second data to select a set of features from the second data. The device may analyze the set of features to evaluate a plurality of types of machine learning models with respect to the set of features. The device may select a particular type of machine learning model for the set of features based on analyzing the set of features to evaluate the plurality of types of machine learning models. The device may tune a set of parameters of the particular type of machine learning model to train the machine learning model. The device may receive third data for prediction. The device may provide a prediction using the particular type of machine learning model.

Abstract: A device may receive workflow data identifying an automation request, and may request jobs for the workflow data. The device may receive encrypted jobs based on the request for the jobs, and may determine whether encryption keys for the encrypted jobs are valid. The device may determine whether workflow portions for the encrypted jobs are valid, and may determine whether to allow or deny each of the encrypted jobs based on whether the encryption keys and the workflow portions are valid. The device may execute the encrypted jobs determined to be allowed, to generate execution results, and may forgo execution of the encrypted jobs determined to be denied. The device may process the execution results and the encrypted jobs determined to be denied, with a machine learning model, to predict a final result for the automation request, and may perform actions based on the final result.

Abstract: A device may receive image data that includes an image of a document and lexicon data identifying a lexicon, and may perform an extraction technique on the image data to identify at least one field in the document. The device may utilize form segmentation to automatically generate label data identifying labels for the image data, and may process the image data, the label data, and data identifying the at least one field, with a first model, to identify visual features. The device may process the image data and the visual features, with a second model, to identify sequences of characters, and may process the image data and the sequences of characters, with a third model, to identify strings of characters. The device may compare the lexicon data and the strings of characters to generate verified strings of characters that may be utilized to generate a digitized document.

Abstract: A device may receive information identifying a communication framework for a mass communication task. The device may determine a success score for the communication framework using a mass communication model, wherein the success score represents a likelihood of a successful response in connection with using the communication framework for the mass communication task. The device may generate a recommendation for the communication framework based on the success score and using the mass communication model. The device may alter the communication framework to implement the recommendation and generate a modified communication framework. The device may perform the mass communication task using the modified communication framework.

Abstract: A device may receive image data that includes an image of a document and lexicon data identifying a lexicon, and may perform an extraction technique on the image data to identify at least one field in the document. The device may utilize form segmentation to automatically generate label data identifying labels for the image data, and may process the image data, the label data, and data identifying the at least one field, with a first model, to identify visual features. The device may process the image data and the visual features, with a second model, to identify sequences of characters, and may process the image data and the sequences of characters, with a third model, to identify strings of characters. The device may compare the lexicon data and the strings of characters to generate verified strings of characters that may be utilized to generate a digitized document.

Abstract: A device may communicate with a server to obtain historical ticket data. The device may generate a data model, based on the historical ticket data. The device may communicate with a client device to obtain ticket data relating to an issue associated with a project. The device may classify, using the data model, the ticket data into a ticket type. The device may generate, using the data model and based on the ticket type, a set of recommended resolutions for resolving the issue associated with the project. The device may select, from the set of recommended resolutions, a particular resolution based on a set of selection criteria. The device may automatically perform one or more actions to implement the particular resolution to resolve the issue associated with the project.

Abstract: A data analytics platform may determine whether a machine learning model is a regression model. The data analytics platform may perform, based on determining that the machine learning model is a regression model, a regression prescription method including acquiring a predicted value of a performance indicator determined by the machine learning model processing data associated with a plurality of features and the performance indicator, acquiring a target value of the performance indicator, determining a rate of change of the performance indicator with respect to each feature to generate first results, determining, based on the regression model and for each feature, a rate of change of each feature with respect to other features to generate second results, and determining, for each feature and based on the predicted value, the target value, the first results, and the second results, a change in each feature to achieve the target value.

Abstract: A device may obtain first data relating to a machine learning model. The device may pre-process the first data to alter the first data to generate second data. The device may process the second data to select a set of features from the second data. The device may analyze the set of features to evaluate a plurality of types of machine learning models with respect to the set of features. The device may select a particular type of machine learning model for the set of features based on analyzing the set of features to evaluate the plurality of types of machine learning models. The device may tune a set of parameters of the particular type of machine learning model to train the machine learning model. The device may receive third data for prediction. The device may provide a prediction using the particular type of machine learning model.

Abstract: A device may obtain ticket data relating to a set of tickets, and process the ticket data to generate a ticket analysis model that is a clustering based natural language analysis model of natural language text associated with tickets of the set of tickets. The device may classify the set of tickets using the ticket analysis model, may determine an automation plan for at least one class of ticket determined based on classifying the set of tickets, and may implement the automation plan to configure an automatic ticket resolution or ticket generation mitigation procedure for the at least one class of ticket. The device may receive a ticket after configuring the automatic ticket resolution or ticket generation mitigation procedure, may classify, using the ticket analysis model, the ticket into the at least one class of ticket, and may automatically implement a response action for the ticket based on classifying the ticket and using the automatic ticket resolution or ticket generation mitigation procedure.

Abstract: A device may receive information identifying a communication framework for a mass communication task. The device may determine a success score for the communication framework using a mass communication model, wherein the success score represents a likelihood of a successful response in connection with using the communication framework for the mass communication task. The device may generate a recommendation for the communication framework based on the success score and using the mass communication model. The device may alter the communication framework to implement the recommendation and generate a modified communication framework. The device may perform the mass communication task using the modified communication framework.

Abstract: A device may obtain ticket data relating to a set of tickets, and process the ticket data to generate a ticket analysis model that is a clustering based natural language analysis model of natural language text associated with tickets of the set of tickets. The device may classify the set of tickets using the ticket analysis model, may determine an automation plan for at least one class of ticket determined based on classifying the set of tickets, and may implement the automation plan to configure an automatic ticket resolution or ticket generation mitigation procedure for the at least one class of ticket. The device may receive a ticket after configuring the automatic ticket resolution or ticket generation mitigation procedure, may classify, using the ticket analysis model, the ticket into the at least one class of ticket, and may automatically implement a response action for the ticket based on classifying the ticket and using the automatic ticket resolution or ticket generation mitigation procedure.

Abstract: A device may communicate with a server to obtain historical ticket data. The device may generate a data model, based on the historical ticket data. The device may communicate with a client device to obtain ticket data relating to an issue associated with a project. The device may classify, using the data model, the ticket data into a ticket type. The device may generate, using the data model and based on the ticket type, a set of recommended resolutions for resolving the issue associated with the project. The device may select, from the set of recommended resolutions, a particular resolution based on a set of selection criteria. The device may automatically perform one or more actions to implement the particular resolution to resolve the issue associated with the project.

Abstract: A device may communicate with a server to obtain historical ticket data. The device may generate a data model, based on the historical ticket data. The device may communicate with a client device to obtain ticket data relating to an issue associated with a project. The device may classify, using the data model, the ticket data into a ticket type. The device may generate, using the data model and based on the ticket type, a set of recommended resolutions for resolving the issue associated with the project. The device may select, from the set of recommended resolutions, a particular resolution based on a set of selection criteria. The device may automatically perform one or more actions to implement the particular resolution to resolve the issue associated with the project.

Abstract: A device may communicate with a server to obtain historical ticket data. The device may generate a data model, based on the historical ticket data. The device may communicate with a client device to obtain ticket data relating to an issue associated with a project. The device may classify, using the data model, the ticket data into a ticket type. The device may generate, using the data model and based on the ticket type, a set of recommended resolutions for resolving the issue associated with the project. The device may select, from the set of recommended resolutions, a particular resolution based on a set of selection criteria. The device may automatically perform one or more actions to implement the particular resolution to resolve the issue associated with the project.