Abstract: A device may receive logs and files associated with a system to be migrated to a cloud computing environment, and may determine workload data associated of the system. The device may derive a data lineage for source data and target data, and may assess a utilization pattern of the system. The device may process the workload data, the data lineage, and data identifying utilization of a distributed computing feature of the system, with a model, to label utilization features and to recommend a cloud architecture. The device may process the workload data, the data lineage, and the data identifying utilization, with a natural language processing model, to determine a cost of migrating the system. The device may process the labelled utilization features, the cloud architecture, and the cost, with a Q-matrix model, to determine migration actions for migrating the system, and may perform actions based on the migration actions.

Abstract: A chemical product formulation system automatically generates seed formulae from historic experiments data for the synthesis of a chemical product. Independent and dependent features are identified from the historic experiments data and feature importance scores are calculated using a supervised machine learning (ML) model. The feature importance scores are used to build data structures from which analytical rules are extracted. The analytical rules are further processed to derive the seed formulae which are user-editable. The intermediate formulae generated via user edits of the seed formulae are further validated and approved in order to be used as the final formulae which are employed for the synthesis of the chemical product.

Abstract: A system may receive, from multiple data providers or multiple platforms, data associated with content, a content provider that provides the content, and multiple channels via which the content is provided or consumed. The data may include first data, received from a first data provider or a first platform, that is associated with a different file type than second data received from a second data provider or a second platform. The system may include a data model associated with the first data and the second data. The system may aggregate the data from the multiple data providers or the multiple platforms. The system may identify a relationship between the first data and the second data. The first data and the second data may be different types of data. The system may perform an action based on the relationship between the first data and the second data.

Abstract: A system may receive, from multiple data providers or multiple platforms, data associated with content, a content provider that provides the content, and multiple channels via which the content is provided or consumed. The data may include first data, received from a first data provider or a first platform, that is associated with a different file type than second data received from a second data provider or a second platform. The system may include a data model associated with the first data and the second data. The system may aggregate the data from the multiple data providers or the multiple platforms. The system may identify a relationship between the first data and the second data. The first data and the second data may be different types of data. The system may perform an action based on the relationship between the first data and the second data.