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 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 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 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 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 identify a ticket associated with an issue, a user associated with resolving the issue, and a particular business process including a particular business process step associated with the issue. The device may present information regarding a particular business process workflow, corresponding to the particular business process, associated with a business process steps. The device may identify the particular business process step from the business process steps, and may identify a particular technical solution corresponding to the particular business process step. The device may present information regarding a particular technical solution workflow, corresponding to the particular technical solution, associated with technical solution steps. The device may identify a particular technical solution step, from the technical solution steps, associated with resolving the issue.

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: An on-demand learning system provides an enhanced leaning environment capable of delivering relevant content on virtually any topic to specific learners. The learning system implements technical features that facilitate curation and subject matter validation of many different types of content. The technical architecture of the learning system also supports intelligent matching of learners to subject matter areas, creation of specific subject matter boards, and resilient maintenance of the boards.

Abstract: A device may identify a ticket associated with an issue, a user associated with resolving the issue, and a particular business process including a particular business process step associated with the issue. The device may present information regarding a particular business process workflow, corresponding to the particular business process, associated with a business process steps. The device may identify the particular business process step from the business process steps, and may identify a particular technical solution corresponding to the particular business process step. The device may present information regarding a particular technical solution workflow, corresponding to the particular technical solution, associated with technical solution steps. The device may identify a particular technical solution step, from the technical solution steps, associated with resolving the issue.

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: An on-demand learning system provides an enhanced leaning environment capable of delivering relevant content on virtually any topic to specific learners. The learning system implements technical features that facilitate curation and subject matter validation of many different types of content. The technical architecture of the learning system also supports intelligent matching of learners to subject matter areas, creation of specific subject matter boards, and resilient maintenance of the boards.

Abstract: A device identifies training data and scoring data for a model, and removes bias from the training data to generate unbiased training data. The device trains the model with the unbiased training data to generate trained models, and processes the trained models, with the scoring data, to generate scores for the trained models. The device selects a trained model, from the trained models, based on model metrics and the scores, and processes a training sample, with the trained model, to generate first results, wherein the training sample is created based on the unbiased training data and production data. The device processes a production sample, with the trained model, to generate second results, wherein the production sample is created based on the production data and the training sample. The device provides the trained model for use in a production environment based on the first results and the second results.

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 provide a user interface for receiving project information for a software implementation project. The project information may be associated with a set of requirements defining the software implementation project. The project information may be associated with a set of deliverables describing results of the software implementation project. The device may generate an initial project plan based on the project information. The device may receive information regarding the initial project plan during fulfillment of the project plan. The device may selectively provide an alert associated with the initial project plan based on receiving the information regarding the initial project plan. The device may selectively generate a modified project plan based on receiving the information regarding the project plan.

Abstract: In some examples, natural language eminence based robotic agent control may include ascertaining, by a robotic agent, an image of an object or an environment, and ascertaining a plurality of natural language insights for the image. For each insight of the plurality of insights, an eminence score may be generated, and each insight of the plurality of insights may be ranked according to the eminence scores. An operation associated with the robotic agent, the object, or the environment may be controlled by the robotic agent and based on a highest ranked insight.

Abstract: In some examples, natural language unification based robotic agent control may include ascertaining, by a robotic agent, an image of an object or an environment, and ascertaining a plurality of natural language insights for the image. A semantic relatedness may be determined between each insight of the plurality of insights, and a semantic relatedness graph may be generated for the plurality of insights. For each insight of the plurality of insights, at least one central concept may be identified. Based on the semantic relatedness graph and the identified at least one central concept, the plurality of insights may be clustered to generate at least one insights cluster. For insights included in the least one insights cluster, a unified insight may be generated. Further, an operation associated with the robotic agent, the object, or the environment may be controlled by the robotic agent and based on the unified insight.

Abstract: A device may receive information associated with a work request. The device may identify a work profile, associated with a group of tasks to perform, and a group of task profiles, associated with estimating a resource demand for performing the group of tasks by a group of resources. The device may generate, based on the work profile and the group of task profiles, a plan identifying the group of tasks, an order for performing the group of tasks, and an estimated resource demand associated with performing the tasks. The device may provide, to a resource device associated with the group of resources, an assignment identifying a task from the group of tasks; may receive, from the resource device, a completion update identifying a completion status associated with the task; may update status information based on the completion update; and may output a status update based on the status information.

Abstract: A device receive data regarding a project. The device may automatically evaluate the data using a set of analytics tools. The device may communicate with a client device to provide a virtual agent user interface component based on evaluating the data using the set of analytics tools. The virtual agent user interface component may include information identifying a set of alerts relating to the project and a set of recommendations relating to the set of alerts. The device may receive information indicating that a particular recommendation, of the set of recommendations is to be implemented. The device may communicate with one or more other devices to implement the particular recommendation.

Abstract: One or more computing resources, of a project management platform, may implement a delivery tool layer to provide one or more tools associated with managing portions of a project. The one or more computing resources may implement an integration layer to integrate the one or more tools into the project management platform for the project. The one or more computing resources may implement a data acquisition layer to obtain data regarding the project. The one or more computing resources may implement an automation layer to automate tasks associated with the project. The automation layer may be associated with automating a set of development operations, a set of maintenance operations, a set of management operations, a set of testing operations, or the like. The one or more computing resources may implement an artificial intelligence (AI) layer to process data relating to the project using one or more AI integrations.

Abstract: A method of maintaining a data store is disclosed. The data store is distributed across a plurality of storage provider instances, including a master storage provider instance and a plurality of additional storage provider instances. A master data log is updated to reflect a change to the data store. The master data log is synchronized with a plurality of replicas of the master data log. The change is propagated across the plurality of additional storage provider instances based on an analysis of the replicas.