Abstract: A system and method for facilitating interoperability of data transformations developed in different programming platforms under a unified platform including receiving a first transformation utilizing a first programming platform; receiving information about the first transformation; wrapping the first transformation; including the wrapped, first transformation in a transformation pipeline, the transformation pipeline including a second transformation that is wrapped, the second transformation utilizing a second programming platform different from the first programming platform; and executing the transformation pipeline including the wrapped, first transformation and the wrapped, second transformation in batch mode or real-time streaming mode.

Abstract: A system and method for providing various user interfaces is disclosed. In one embodiment, the various user interfaces include a series of user interfaces that guide a user through the machine learning process. In one embodiment, the various user interfaces are associated with a unified, project-based data scientist workspace to visually prepare, build, deploy, visualize and manage models, their results and datasets.

Abstract: A system and method for selecting a machine learning method and optimizing the parameters that control its behavior including receiving data; determining, using one or more processors, a first candidate machine learning method; tuning, using one or more processors, one or more parameters of the first candidate machine learning method; determining, using one or more processors, that the first candidate machine learning method and a first parameter configuration for the first candidate machine learning method are the best based on a measure of fitness subsequent to satisfaction of a stop condition; and outputting, using one or more processors, the first candidate machine learning method and the first parameter configuration for the first candidate machine learning method.