Re killer feature: I believe it's being end-to-end, provide experience for "citizen x", domain experts, rather than only ML engineers: so it's no-code data clea

Re killer feature: I believe it's being end-to-end, provide experience for "citizen x", domain experts, rather than only ML engineers: so it's no-code data cleansing, transformation + ML layer on top of this + productionalization, and being cloud-agnostic. We found that the problem that we're trying to solve is bigger than just giving the possibility to use ML instruments for non-developers. There are a lot of citizen data scientists who want to automate their spreadsheet work, make inference easy once the model is created without too much efforts to change processing pipeline, etc. That's why we don't compete with Azure ML Studio as they more developers-focused and with AutoML tools as ML is only a feature of platform. In addition, we've designed and implemented own pipeline scheduling algorithm and APIs out-of-the-box, so ready pipelines or models could be easily embedded into business process or used outside of the platform.