Hi,
I am doing my PHD thesis on large scale machine learning e.g Online
learning, batch and mini batch learning.
Could somebody help me with ideas especially in the context of Spark and to
the above learning methods.
Some ideas like improvement to existing algorithms, implementing new
features
Hi,
Apologies for the generic question.
As I am developing predictive models for the first time and soon model will
be deployed in production very soon.
Could somebody help me with the model deployment in production , I have
read quite a few on model deployment and have read some books on Datab
o the kind of ground the oryx project is intended to cover,
> something I've worked on personally:
> https://github.com/OryxProject/oryx -- a layer on and around the
> core model building in Spark + Spark Streaming to provide a whole
> recommender (for example), down to the REST API
t, just stick the result in mem cache / redis / whatever and evict it
> when you recompute your offline model, or every hour or whatever.
>
>
> —
> Sent from Mailbox <https://www.dropbox.com/mailbox>
>
>
> On Sun, Mar 15, 2015 at 3:03 PM, Shashidhar Rao <
> raosha
s also the kind of ground the oryx project is intended to cover,
> something I've worked on personally:
> https://github.com/OryxProject/oryx -- a layer on and around the
> core model building in Spark + Spark Streaming to provide a whole
> recommender (for example), down to the
Hi,
Can anyone who has developed recommendation engine suggest what could be
the possible software stack for such an application.
I am basically new to recommendation engine , I just found out Mahout and
Spark Mlib which are available .
I am thinking the below software stack.
1. The user is goin
