Abstract: In some implementations, a device may receive training data identifying a set of workflows. The device may generate a set of workflow templates based on the training data identifying the set of workflows, wherein a workflow template, of the set of workflow templates, is associated with a set of steps for completing a task associated with the workflow template. The device may receive, from a client device associated with an entity, a new task for automation using a new workflow. The device may parse the new task to identify one or more steps associated with the new task. The device may identify one or more workflow templates. The device may a workflow recommendation relating to the one or more workflow templates. The device may output workflow data associated with the workflow recommendation.

Abstract: A device may receive, in near-real time, unstructured data associated with an application or a system, and may extract textual data from the unstructured data. The device may parse the textual data to generate parsed textual data, and may perform natural language processing on the parsed textual data to generate processed textual data. The device may process the processed textual data, with a clustering model, to identify topical data associated with the processed textual data, and may process the topical data, with a classification model, to group the topical data into categories. The device may generate a knowledge graph based on the categories, and may store the knowledge graph in a data structure. The knowledge graph may enable the device to provide answers to questions associated with the application or the system.

Abstract: A device may receive project management data associated with development of a software product and may process a first portion of the project management data, with first models, to generate timeliness scores and an overall timeliness score for the software product. The device may process a second portion of the project management data, with second models, to generate quality scores and an overall quality score for the software product and may process a third portion of the project management data, with third models, to generate product readiness scores and an overall product readiness score for the software product. The device may utilize a fourth machine learning model, with the overall timeliness score, the overall quality score, and the overall product readiness score, to generate a success probability for the software product and may perform one or more actions based on the success probability for the software product.

Abstract: A device may receive and process a change request, work items, and IT data, to generate processed data. The device may transform the processed data into vectorized data, and may select similarity analytics models, regression models, and a classification model. The device may process the vectorized data, with the similarity analytics models, to determine an estimated effort, a user story, and IT requirements, and may process the vectorized data, with the regression models, to determine a schedule overrun, a defect rate, and a sprint velocity. The device may process the vectorized data, with the classification model, to determine a story point, and may calculate a resource capacity. The device may generate an impact analysis based on the estimated effort, the user story, the IT requirements, the schedule overrun, the defect rate, the sprint velocity, the story point, or the resource capacity, and may perform actions based on the impact analysis.

Abstract: One or more techniques and/or systems are provided for dynamically provisioning logical storage pools of storage devices for applications. For example, a logical storage pool, of one or more storage devices, may be constructed based upon a service level agreement for an application (e.g., an acceptable latency, an expected throughput, etc.). Real-time performance statistics of the logical storage pool may be collected and evaluated against the service level agreement to determine whether a storage device does not satisfy the service level agreement. For example, a latency of a storage device within the logical storage pool may increase overtime as log files and/or other data of the application increase. Accordingly, a new logical storage pool may be automatically and dynamically defined and provisioned for the application to replace the logical storage pool. The new logical storage pool may comprise storage devices expected to satisfy the storage level agreement.

Abstract: In some examples, artificial intelligence based project implementation may include implementing, for a project team, self-evaluation of viability for utilizing a project implementation framework. The project team may be guided on utilization of the project implementation framework. A discussion by the project team may be documented in a digital format during envisioning associated with the project implementation framework, and documented information may be maintained for reference during execution, by the project team, of a journey associated with the project implementation framework. During execution of the journey, implementation of a social contract by the project team may be evaluated. A determination may be made as to whether the social contract is not implemented for at least one specified occurrence associated with the project implementation framework, and at least one impediment associated with the project implementation framework may be identified.

Abstract: One or more techniques and/or systems are provided for dynamically provisioning logical storage pools of storage devices for applications. For example, a logical storage pool, of one or more storage devices, may be constructed based upon a service level agreement for an application (e.g., an acceptable latency, an expected throughput, etc.). Real-time performance statistics of the logical storage pool may be collected and evaluated against the service level agreement to determine whether a storage device does not satisfy the service level agreement. For example, a latency of a storage device within the logical storage pool may increase overtime as log files and/or other data of the application increase. Accordingly, a new logical storage pool may be automatically and dynamically defined and provisioned for the application to replace the logical storage pool. The new logical storage pool may comprise storage devices expected to satisfy the storage level agreement.

Abstract: In some implementations, a device may receive project management data including text descriptions related to one or more requirements associated with a project. The device may identify a set of work items associated with the project and a set of existing dependencies among the set of work items associated with the project. The device may generate, based on the text descriptions related to the one or more requirements associated with the project, a set of recommended dependencies among the set of work items associated with the project using an artificial intelligence model. The device may generate a user interface to visually indicate information related to the set of existing dependencies and the set of recommended dependencies among the set of work items associated with the project and to provide one or more elements to manage the set of existing dependencies and/or the set of recommended dependencies.

Abstract: A device may receive entity data identifying an entity and may identify harvested templates, contract profiles, and commitment accuracy scores, based on the entity data. The device may process the harvested templates, the contract profiles, and the commitment accuracy scores, with a first machine learning model, to generate first templates and may process the harvested templates, the contract profiles, and the commitment accuracy scores, with a second machine learning model, to generate second templates. The device may process the first templates and the second templates, with a third machine learning model, to generate final templates and final rankings for the final templates and may select a template from the final templates based on the final rankings. The device may process the template, with a fourth machine learning model, to identify deliverables associated with the template and may generate and implement a strategic plan based on the template and the deliverables.

Abstract: A device may receive project management data associated with development of a software product and may process a first portion of the project management data, with first models, to generate timeliness scores and an overall timeliness score for the software product. The device may process a second portion of the project management data, with second models, to generate quality scores and an overall quality score for the software product and may process a third portion of the project management data, with third models, to generate product readiness scores and an overall product readiness score for the software product. The device may utilize a fourth machine learning model, with the overall timeliness score, the overall quality score, and the overall product readiness score, to generate a success probability for the software product and may perform one or more actions based on the success probability for the software product.

Abstract: A device may receive work item data identifying work items associated with requirements from different tools of a project and may perform data cleansing to remove and/or modify particular words from the work item data and to generate cleansed work item data. The device may perform natural language processing on the cleansed work item data to identify synonyms for words in the cleansed work item data and may replace abbreviations in the cleansed work item data with full form text to generate final work item data. The device may identify keywords in the final work item data and may process the final work item data, the synonyms, and the keywords, with a machine learning model, to identify mappings between work items of the final work item data and to determine a confidence score for the mappings. The device may perform actions based on the mappings and the confidence score.

Abstract: A device may receive audio-video content regarding a system; segment the audio-video content to generate audio content and video content; process the audio content based on generating the audio content; process the video content based on generating the video content; identify a hierarchy for the set of sections based on processing the audio content and the video content; generate a system understanding document based on the hierarchy of the set of sections and based on the audio content and the video content; and store the system understanding document in a knowledge base.

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, in near-real time, unstructured data associated with an application or a system, and may extract textual data from the unstructured data. The device may parse the textual data to generate parsed textual data, and may perform natural language processing on the parsed textual data to generate processed textual data. The device may process the processed textual data, with a clustering model, to identify topical data associated with the processed textual data, and may process the topical data, with a classification model, to group the topical data into categories. The device may generate a knowledge graph based on the categories, and may store the knowledge graph in a data structure. The knowledge graph may enable the device to provide answers to questions associated with the application or the system.

Abstract: A device may receive input data identifying user stories, test case documents, event logs, and application logs associated with an application, and may perform natural language processing on the user stories and the test case documents, identified in the input data, to generate a first state diagram associated with the application. The device may process the event logs identified in the input data, with a heuristic miner model, to generate a second state diagram associated with the application, and may process the application logs identified in the input data, with a clustering model, to generate a volumetric analysis associated with the application. The device may perform post processing of the first state diagram, the second state diagram, and the volumetric analysis, to remove duplicate data and unmeaningful data and to generate modified outputs, and may perform actions based on the modified outputs.

Abstract: A device may receive input data identifying user stories, test case documents, event logs, and application logs associated with an application, and may perform natural language processing on the user stories and the test case documents, identified in the input data, to generate a first state diagram associated with the application. The device may process the event logs identified in the input data, with a heuristic miner model, to generate a second state diagram associated with the application, and may process the application logs identified in the input data, with a clustering model, to generate a volumetric analysis associated with the application. The device may perform post processing of the first state diagram, the second state diagram, and the volumetric analysis, to remove duplicate data and unmeaningful data and to generate modified outputs, and may perform actions based on the modified outputs.

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: In some examples, artificial intelligence based project implementation may include implementing, for a project team, self-evaluation of viability for utilizing a project implementation framework. The project team may be guided on utilization of the project implementation framework. A discussion by the project team may be documented in a digital format during envisioning associated with the project implementation framework, and documented information may be maintained for reference during execution, by the project team, of a journey associated with the project implementation framework. During execution of the journey, implementation of a social contract by the project team may be evaluated. A determination may be made as to whether the social contract is not implemented for at least one specified occurrence associated with the project implementation framework, and at least one impediment associated with the project implementation framework may be identified.

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: One or more techniques and/or systems are provided for dynamically provisioning logical storage pools of storage devices for applications. For example, a logical storage pool, of one or more storage devices, may be constructed based upon a service level agreement for an application (e.g., an acceptable latency, an expected throughput, etc.). Real-time performance statistics of the logical storage pool may be collected and evaluated against the service level agreement to determine whether a storage device does not satisfy the service level agreement. For example, a latency of a storage device within the logical storage pool may increase overtime as log files and/or other data of the application increase. Accordingly, a new logical storage pool may be automatically and dynamically defined and provisioned for the application to replace the logical storage pool. The new logical storage pool may comprise storage devices expected to satisfy the storage level agreement.