We allow our users to interact with spark cluster using SQL queries only. That's easy for them. MLLib does not have SQL extensions and we cannot expose it to our users.
SQL extensions can further accelerate MLLib's adoption. See https://cloud.google.com/bigquery/docs/bigqueryml-intro. Hemant On Thu, Aug 30, 2018 at 9:41 PM William Benton <wi...@redhat.com> wrote: > What are you interested in accomplishing? > > The spark.ml package has provided a machine learning API based on > DataFrames for quite some time. If you are interested in mixing query > processing and machine learning, this is certainly the best place to start. > > See here: https://spark.apache.org/docs/latest/ml-guide.html > > > best, > wb > > > > On Thu, Aug 30, 2018 at 1:45 AM Hemant Bhanawat <hemant9...@gmail.com> > wrote: > >> Is there a plan to support SQL extensions for mllib? Or is there an >> effort already underway? >> >> Any information is appreciated. >> >> Thanks in advance. >> Hemant >> >
