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Since the authors claim that it is "Built on top of scalable frameworks such as Hadoop and Cascading" I guess that it is aiming to be production-ready while scikit is mostly for prototyping.

In that case then what is the added value compared to Mahout, the Hadoop-based ML framework?

I'm always extremely skeptical of such initiatives, because ML is not a magical black box where you put your data in one end and you get results on the other end. Automating the trivial parts of ML, ie. providing an API to a ML library, is a week-end project, but that in itself is useless. If you don't automate the hard parts of ML, such as feature engineering, then you're not providing any value at all.

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