Abstract: In some implementations, a device may receive project management data including information related to project elements associated with one or more projects and associations among the project elements associated with the one or more projects. The device may generate one or more data structures representing the project elements associated with the one or more projects and the associations among the project elements associated with the one or more projects. The device may receive a request to visualize project data, wherein the request may indicate a view to use in order to visualize the project data and/or specify criteria related to the project data to be visualized in the view. The device may search the one or more data structures to obtain the project data to be visualized based on the criteria specified in the request. The device may render the project data using the view indicated in the request.

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: In some implementations, a device may receive project management data including information related to project elements associated with one or more projects and associations among the project elements associated with the one or more projects. The device may generate one or more data structures representing the project elements associated with the one or more projects and the associations among the project elements associated with the one or more projects. The device may receive a request to visualize project data, wherein the request may indicate a view to use in order to visualize the project data and/or specify criteria related to the project data to be visualized in the view. The device may search the one or more data structures to obtain the project data to be visualized based on the criteria specified in the request. The device may render the project data using the view indicated in the request.

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, 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 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 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: 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: Laminates of improved wear resistant are made using decor sheets prepared by treating printed décor paper with a mordant so that decorative ink is bonded to the paper. The decor sheets are then suitable for use in high pressure or low pressure laminates.

Abstract: Laminates of improved wear resistant are made using decor sheets prepared by treating printed décor paper with a mordant so that decorative ink is bonded to the paper. The decor sheets are then suitable for use in high pressure or low pressure laminates.

Abstract: Laminates of improved wear resistant are made using decor sheets prepared by treating printed decor paper with a mordant so that decorative ink is bonded to the paper. The decor sheets are then suitable for use in high pressure or low pressure laminates.