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 model uses, your model data might be produced by an RDD operation, and saved that way. There it's no different than anything else you do with RDDs. What part are you looking to automate beyond those things? that's most of it. On Fri, Jul 22, 2016 at 2:04 PM, Sergio Fernández <wik...@apache.org> wrote: > Hi Sean, > > On Fri, Jul 22, 2016 at 12:52 PM, Sean Owen <so...@cloudera.com> 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 other manual way of deploying model info, just >> do that again. There's no special thing to know. > > > Well, because some huge model, we typically bundle both logic > (pipeline/application) and models separately. Normally we use a shared > stores (e.g., HDFS) or coordinated distribution of the models. But I wanted > to know if there is any infrastructure in Spark that specifically addresses > such need. > > Thanks. > > Cheers, > > P.S.: sorry Jacek, with "ml" I meant "Machine Learning". I thought is a > quite spread acronym. Sorry for the possible confusion. > > > -- > Sergio Fernández > Partner Technology Manager > Redlink GmbH > m: +43 6602747925 > e: sergio.fernan...@redlink.co > w: http://redlink.co --------------------------------------------------------------------- To unsubscribe e-mail: user-unsubscr...@spark.apache.org
