Hi, I am in the same spot as Isabel. Used to use/understand most of the «old» standalone mahout, now doing some data transformation with spark, but I am not sure where Samsara fits in the ecosystem. We also do quite a bit of computation in R. Basically we are willing to learn and support the project by for instance buying the books Rob mentioned, but a short doc with the outline Isabel describes would be great!
Many thanks, Florent Le 31 janv. 2017 12:01, "Isabel Drost-Fromm" <isa...@apache.org> a écrit : Hi, On Fri, Sep 16, 2016 at 11:36:03PM -0700, Andrew Musselman wrote: > and we're thinking about just how many pre-built algorithms we > should include in the library versus working on performance behind the > scenes. To pick this question up: I've been watching Mahout from a distance for quite some time. So from what limited background I have of Samsara I really like it's approach to be able to run on more than one execution engine. To give some advise to downstream users in the field - what would be your advise for people tasked with concrete use cases (stuff like fraud detection, anomaly detection, learning search ranking functions, building a recommender system)? Is that something that can still be done with Mahout? What would it take to get from raw data to finished system? Is there something we can do to help users get that accomplished? Is there even interest from users in such a use case based perspective? If so, would there be interest among the Mahout committers to help users publicly create docs/examples/modules to support these use cases? Isabel
