Abstract: A device may detect a trigger to perform a benchmarking task. The benchmarking task may include a first benchmarking of a first resource utilization associated with one or more tasks completed via an automated procedure. The benchmarking task may include a second benchmarking of a second resource utilization associated with the one or more tasks completed via a manual procedure. The device may determine project data relating to a project platform based on detecting the trigger to perform the benchmarking task. The device may process the project data relating to the project platform to benchmark the project. The device may generate a recommendation relating to completion of the one or more tasks using the automated procedure or the manual procedure. The device may communicate with one or more other devices to perform a response action based on the recommendation.

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: In some examples, artificial intelligence and machine learning based product development may include ascertaining an inquiry, by a user, related to a product that is to be developed or that is under development, and ascertaining an attribute associated with the user. The inquiry may be analyzed to determine at least one virtual assistant from a set of virtual assistants to respond to the inquiry. The determined at least one virtual assistant may be invoked based on an authorization by the user. Further, development of the product may be controlled based on the invocation of the determined at least one virtual assistant.

Abstract: A device may receive group information, associated with performing application maintenance, that includes information corresponding to one or more resources, associated with performing the application maintenance, and information associated with one or more shifts of the one or more resources. The device may receive ticket information, associated with performing the application maintenance, that includes priority information associated with one or more ticket types associated with performing the application maintenance. The device may simulate, based on the group information and the ticket information, performing the application maintenance to determine a simulation result. The simulation result may include information associated with one or more predicted performance metrics associated with performing the application maintenance. The device may provide the information associated with the one or more predicted performance metrics.

Abstract: A device may receive issue resolution information, associated with a cognitive model, including an item of issue resolution information that describes an issue and a resolution to the issue. The device may assign the item of issue resolution information to a domain hierarchy, where the assigning is associated with a first user. The device may generate a question and an answer corresponding to the item of issue resolution information, where the generating of the question and the answer is associated with a second user. The device may approve the question and the answer, where the approving is associated with a third user. The device may generate a question/answer (QA) pair for the question and the answer. The device may create a data corpus including the QA pair, and provide the data corpus to cause the cognitive model to be trained based on the data corpus.

Abstract: A device receives virtual network function (VNF) data identifying a VNF to be implemented in a network and configures the VNF data with hardware and software requirements for deploying the VNF in a non-production environment, wherein configuring the VNF data with the hardware and software requirements generates a configured VNF. The device deploys the configured VNF in the non-production environment and performs one or more tests on the configured VNF deployed in the non-production environment. The device receives one or more test results based on performing the one or more tests on the configured VNF, and updates the configured VNF, based on the one or more test results, to generate an updated VNF. The device causes the updated VNF to be deployed in the network.

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 receive data associated with a software development platform, and may correlate the data to generate correlated data. The device may train a first model, with the correlated data, to generate a software impact analyzer model, and may train a second model, with the correlated data, to generate a software development behavior model. The device may receive data identifying a new software requirement associated with the software development platform, and may process the data identifying the new software requirement, with the software impact analyzer model, to identify a file or a module impacted by the new software requirement. The device may process data identifying the file or the module, with the software development behavior model, to identify a developer to handle the new software requirement, and may perform one or more actions based on the data identifying the file or the module and data identifying the developer.

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 data associated with a software development platform, and may correlate the data to generate correlated data. The device may train a first model, with the correlated data, to generate a software impact analyzer model, and may train a second model, with the correlated data, to generate a software development behavior model. The device may receive data identifying a new software requirement associated with the software development platform, and may process the data identifying the new software requirement, with the software impact analyzer model, to identify a file or a module impacted by the new software requirement. The device may process data identifying the file or the module, with the software development behavior model, to identify a developer to handle the new software requirement, and may perform one or more actions based on the data identifying the file or the module and data identifying the developer.

Abstract: A device may determine a plurality of components for a development project. The device may determine a blueprint template based on the plurality of components of the development project. The device may generate a blueprint for the development project based on the blueprint template. The device may generate a package for the development project based on the blueprint for the development project. The device may provide output associated with the blueprint and the package based on generating the blueprint and generating the package.

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 receives a command to identify an automation evaluation for a role, determines tasks of the role based on data relating to the role, and determines activities for the tasks based on the data relating to the role. The device determines one or more automation scores, which correspond to a suitability for automation of the activities, based on a set of characteristics of the activities and based on the data relating to the role. The automation scores are determined using a machine learning model to parse natural language descriptions of the activities and score parsed portions of the natural language descriptions. The device generates, for the role, an aggregate automation score based on the automation scores, determines the automation evaluation for the role based on the aggregate automation score and characteristics of an entity associated with the role, and performs an action relating to the automation evaluation.

Abstract: A device may obtain process data relating to a set of processes. The device may process the process data to generate a process analysis model. The device may classify, using the process analysis model, a particular process into a particular class of a set of classes of the process analysis model. The device may automatically assess the particular process based on the particular class, wherein a set of assessment parameters for assessing the particular process is selected based on the particular class, and wherein an assessment score is assigned to the particular process based on values for the set of assessment parameters. The device may determine, based on the assessment score and the particular class, an automation recommendation for the particular process. The device may automatically complete the particular process using a particular tool based on determining the automation recommendation.

Abstract: A component analysis platform may communicate with one or more devices to obtain prediction data relating to a type of component. The component analysis platform may process the prediction data to determine a set of predictors for failure of an instance of the component, and may generate a model for failure of the instance of the component based on the set of predictors. The component analysis platform may monitor the instance of the component to obtain component data relating to the instance of the component. The component analysis platform may determine, using the model and based on the component data relating to the instance of the component, a predicted failure for the instance of the component. The component analysis platform may perform a response action related to the predicted failure.

Abstract: A device may determine a plurality of components for a development project. The device may determine a blueprint template based on the plurality of components of the development project. The device may generate a blueprint for the development project based on the blueprint template. The device may generate a package for the development project based on the blueprint for the development project. The device may provide output associated with the blueprint and the package based on generating the blueprint and generating the package.

Abstract: A device receives virtual network function (VNF) data identifying a VNF to be implemented in a network and configures the VNF data with hardware and software requirements for deploying the VNF in a non-production environment, wherein configuring the VNF data with the hardware and software requirements generates a configured VNF. The device deploys the configured VNF in the non-production environment and performs one or more tests on the configured VNF deployed in the non-production environment. The device receives one or more test results based on performing the one or more tests on the configured VNF, and updates the configured VNF, based on the one or more test results, to generate an updated VNF. The device causes the updated VNF to be deployed in the network.

Abstract: A device may identify a tool operating on a first device for integration into a lifecycle management platform operating on a second device. The tool may be associated with providing a functionality not included in the lifecycle management platform. The first device may be external to the second device. The device may determine a set of tool attributes for data events associated with the tool. The data events may include a data input, a data output, a new message, an updated message, a deleted message, or the like. The device may select a message format based on the set of tool attributes. The device may configure adaptation for a tool application programming interface (API) of the tool and a platform API of the lifecycle management platform based on the message format. The device may provide information associated with configuring adaptation for the tool API and the platform API.

Abstract: A device may predict, based on historical data relating to a plurality of past projects, a trigger to perform a project health check for a project. The device may process project data relating to the project to determine a health check status of the project based on predicting the trigger to perform the project health check for the project. The device may generate a recommendation relating to altering completion of the project based on the health check status of the project. The device may communicate with one or more devices to provide information identifying the recommendation. The device may receive, from the one or more devices, response information relating to the recommendation. The device may perform a response action relating to the recommendation based on receiving the response information.