Abstract: A method includes identifying a sequence of transformations to be performed on an input dataset via a user interface. The method also includes identifying a first context associated with the input dataset. The method further includes selecting a first one of multiple execution engines to be used to perform the sequence of transformations on the input dataset based on the first context. In addition, the method includes providing first code implementing the sequence of transformations to the first execution engine and executing the first code using the first execution engine to perform the sequence of transformations on the input dataset.

Abstract: A method includes identifying a sequence of transformations to be performed on an input dataset via a user interface. The method also includes identifying a first context associated with the input dataset. The method further includes selecting a first one of multiple execution engines to be used to perform the sequence of transformations on the input dataset based on the first context. In addition, the method includes providing first code implementing the sequence of transformations to the first execution engine and executing the first code using the first execution engine to perform the sequence of transformations on the input dataset.

Abstract: A method includes identifying a sequence of transformations to be performed on an input dataset via a user interface. The method also includes identifying a first context associated with the input dataset. The method further includes selecting a first one of multiple execution engines to be used to perform the sequence of transformations on the input dataset based on the first context. In addition, the method includes providing first code implementing the sequence of transformations to the first execution engine and executing the first code using the first execution engine to perform the sequence of transformations on the input dataset.

Abstract: A method includes identifying one or more transformations to be applied in order to generate one or more features or feature sets. The method also includes generating metadata identifying the one or more features or feature sets and the one or more transformations. The method further includes using the metadata to determine the one or more features or feature sets for specified data and storing the one or more determined features or feature sets in a feature store. In addition, the method includes outputting at least some of the one or more determined features or feature sets or data associated with the at least some of the one or more determined features or feature sets from the feature store to at least one machine learning model.

Abstract: A method, system, and computer program product for of configuring cloud computing platforms. One such method serves for recommending alternative computing architectures for a selected application using automated instrumentation of the application under an abstracted workload. The method commences by measuring workload characteristics of the selected application using pre-determined performance parameters. Additional performance parameters to be measured are selected based on previous measurements, and further analysis includes instrumenting the application to provide measurement instruments corresponding to the respective selected additional performance parameters. Such hardware- or software-based instruments are used for determining a baseline set of performance metrics by measuring the performance of the selected application on a first (e.g., currently-in-use) computing architecture, then, measuring the performance of the application on other proposed computing architectures.

Abstract: A method, system, and computer program product for of configuring cloud computing platforms. One such method serves for recommending alternative computing architectures for a selected application using automated instrumentation of the application under an abstracted workload. The method commences by measuring workload characteristics of the selected application using pre-determined performance parameters. Additional performance parameters to be measured are selected based on previous measurements, and further analysis includes instrumenting the application to provide measurement instruments corresponding to the respective selected additional performance parameters. Such hardware- or software-based instruments are used for determining a baseline set of performance metrics by measuring the performance of the selected application on a first (e.g., currently-in-use) computing architecture, then, measuring the performance of the application on other proposed computing architectures.