Pavan, I guess part of the documentation difficulty is in that Mahout Samsara environment is only used for "training" but external components are used for "scoring". So it is not 100% end-to-end Mahout solution to document.
Pat, it would be nice though to put some of your docs on to Mahout site though, what you think? They will be bound by Apache ICLA though after that (meaning anyone can cut-and-paste it and put it in their books, with or -- in practice -- without any attribution). On Tue, Oct 20, 2015 at 12:05 PM, Pavan K Narayanan < pavan.naraya...@gmail.com> wrote: > Perhaps this page <http://mahout.apache.org/users/basics/algorithms.html> > needs > to be updated with algorithms and features of 0.11.0? > > On 19 October 2015 at 18:29, Pat Ferrel <p...@occamsmachete.com> wrote: > > > BTW this use of Mahout-Samsara on Spark for recs has really expanded. The > > Samsara part I’m calling a Correlation Engine, it can be used to mix > usage, > > content, and context to make recs. I look back on 2 years ago as pretty > > much groping around for solutions. Things are much clearer now (for me at > > least) > > > > Check out some slides about the math, leading to the “whole enchilada” > > equation. Ted Dunning, Sean Owen, and Sebastian Schelter get no small > > credit. > > http://www.slideshare.net/pferrel/unified-recommender-39986309 > > > > Even have code running using the PredicitonIO framework. This includesa > > SDK to event store to realtime query. Loosely speaking a lambda > > architecture. Most of the whole enchilada running except the content part > > of the equation, which only works on metadata for how. > > https://github.com/pferrel/scala-parallel-universal-recommendation > > > > We even do custom versions at actionML.com > > > > > > On Oct 19, 2015, at 6:42 AM, Sean Owen <sro...@gmail.com> wrote: > > > > No, this is pretty wrong. Spark is not, in general, a real-time > > anything. Spark Streaming is a near-real-time streaming framework, but > > it is not something you can build models with. Spark MLlib / ML are > > offline / batch. Not sure what you mean by Hadoop engine, but Spark > > does not build on MapReduce, if that's what you mean. > > > > The "classic" Mahout code (<= 0.9) is definitely deprecated. The "new" > > Mahout is not. It has a fairly different new recommender system called > > Samsara. It has Scala APIs. In fact, it uses Spark. I think you're > > somehow talking about the "classic" Mahout code here only. > > > > On Mon, Oct 19, 2015 at 2:38 PM, Fei Shan <shanfeishan...@gmail.com> > > wrote: > > > Spark is a in memory , near realtime Machine Learning frameowork , has > > > scala and java interface > > > Mahout is an offline Machine Learning framework, no scala apis > > > > > > they both built on the HDFS and Hadoop engine > > > > > > Spark has an ecosystem like Hadoop > > > Mahout is part of of Hadoop ecosystem > > > > > > Spark could beat Mahout on processing speed > > > and concise programming APIs > > > > > > for online data anaysis , Spark is a better choice. > > > for offline data analysis, both fits well. > > > > > > > > > > > > On Mon, Oct 19, 2015 at 9:14 PM, Prasad Priyadarshana Fernando < > > > bpp...@gmail.com> wrote: > > > > > >> Hi, > > >> > > >> If I have used Mahout for my recommendation application, should I > > migrate > > >> into Spark MLib technology? Is the mahout still supported and > migrated? > > >> > > >> Thanks > > >> > > >> *Prasad Priyadarshana Fernando < > > http://www.linkedin.com/in/prasadfernando > > >>> * > > >> Mobile: +1 330 283 5827 > > >> > > > > >
