Abstract: Arrangements for database schema creation and control are provided. In some aspects, a user may request creation or modification of a database schema. In response to receiving the request, one or more interactive user interfaces may be generated. The one or more interactive user interfaces may enable a user to select one or more selectable options for presenting data, establishing relationships between data, data sources, and the like. The schema, including a schema configuration including the presentational aspects and relationships, may be stored. A user may request to view data and the stored database schema may be retrieved. The schema may be transmitted to a configuration engine that may retrieve the requested data from one or more non-relational database sources. One or more user interfaces may be dynamically generated based on the schema configuration and may include the retrieved data.

Abstract: Arrangements for database schema creation and control are provided. In some aspects, a user may request creation or modification of a database schema. In response to receiving the request, one or more interactive user interfaces may be generated. The one or more interactive user interfaces may enable a user to select one or more selectable options for presenting data, establishing relationships between data, data sources, and the like. The schema, including a schema configuration including the presentational aspects and relationships, may be stored. A user may request to view data and the stored database schema may be retrieved. The schema may be transmitted to a configuration engine that may retrieve the requested data from one or more non-relational database sources. One or more user interfaces may be dynamically generated based on the schema configuration and may include the retrieved data.

Abstract: A device configured to receive a user input that identifies a machine learning model type and hyperparameters for a set of machine learning models and to generate the set of machine learning models based on the hyperparameters. The device is further configured to convert training data into a first set of hexadecimal values and to train the set of machine learning models using the first set of hexadecimal values. The device is further configured to convert test data into a second set of hexadecimal values and to obtain a classification value in response to inputting the second set of hexadecimal values into the set of machine learning models. The device is further configured to determine performance metrics for each machine learning model, to generate a model comparison report that comprises the performance metrics for each machine learning model, and to output the model comparison report.

Abstract: Arrangements for database schema creation and control are provided. In some aspects, a user may request creation or modification of a database schema. In response to receiving the request, one or more interactive user interfaces may be generated. The one or more interactive user interfaces may enable a user to select one or more selectable options for presenting data, establishing relationships between data, data sources, and the like. The schema, including a schema configuration including the presentational aspects and relationships, may be stored. A user may request to view data and the stored database schema may be retrieved. The schema may be transmitted to a configuration engine that may retrieve the requested data from one or more non-relational database sources. One or more user interfaces may be dynamically generated based on the schema configuration and may include the retrieved data.

Abstract: Aspects of the disclosure relate to supervised machine-learning (“ML”) training platforms for artificial intelligence (“AI”) computer systems. The ML training platform may include isolated update testing. The isolated update testing may feature a plurality of environments with various levels of isolation. The ML training platform may also include bi-directional channels for controlled update propagation.