Re: ml models distribution

hey everyone- this concept of deploying your Spark ML Pipelines and Algos into Production (user-facing production) has been coming up a lot recently. so much so, that i've dedicated the last few months of my research and engineering efforts to build out the infrastructure to support this in a

Re: ml models distribution

Hello guys..i know its irrelevant to this topic but i've been looking desperately for the solution. I am facing en exception http://apache-spark-user-list.1001560.n3.nabble.com/how-to-resolve-you-must-build-spark-with-hive-exception-td27390.html plz help me.. I couldn't find any solution.. plz

Re: ml models distribution

No there isn't anything in particular, beyond the various bits of serialization support that write out something to put in your storage to begin with. What you do with it after reading and before writing is up to your app, on purpose. If you mean you're producing data outside the model that your

Re: ml models distribution

Hi Sean, On Fri, Jul 22, 2016 at 12:52 PM, Sean Owen wrote: > > If you mean, how do you distribute a new model in your application, > then there's no magic to it. Just reference the new model in the > functions you're executing in your driver. > > If you implemented some

Re: ml models distribution

twitter.com/jaceklaskowski >> >> >> On Fri, Jul 22, 2016 at 11:49 AM, Sergio Fernández <wik...@apache.org> wrote: >>> Hi, >>> >>> I have one question: >>> >>> How is the ML models distribution done across all nodes of a Spark clus

Re: ml models distribution

> https://medium.com/@jaceklaskowski/ > Mastering Apache Spark http://bit.ly/mastering-apache-spark > Follow me at https://twitter.com/jaceklaskowski > > > On Fri, Jul 22, 2016 at 11:49 AM, Sergio Fernández <wik...@apache.org> wrote: >> Hi, >> >> I have one ques

Re: ml models distribution

, Jul 22, 2016 at 11:49 AM, Sergio Fernández <wik...@apache.org> wrote: > Hi, > > I have one question: > > How is the ML models distribution done across all nodes of a Spark cluster? > > I'm thinking about scenarios where the pipeline implementation does not > necessary

ml models distribution

Hi, I have one question: How is the ML models distribution done across all nodes of a Spark cluster? I'm thinking about scenarios where the pipeline implementation does not necessary need to change, but the models have been upgraded. Thanks in advance. Best regards, -- Sergio Fernández