awesome thanks Joseph 2018-03-20 14:51 GMT-07:00 Joseph Bradley <jos...@databricks.com>:
> The promised roadmap JIRA: https://issues.apache. > org/jira/browse/SPARK-23758 > > Note it doesn't have much explicitly listed yet, but committers can add > items as they agree to shepherd them. (Committers, make sure to check what > you're currently listed as shepherding!) The links for searching can be > useful too. > > On Thu, Dec 7, 2017 at 3:55 PM, Stephen Boesch <java...@gmail.com> wrote: > >> Thanks Joseph. We can wait for post 2.3.0. >> >> 2017-12-07 15:36 GMT-08:00 Joseph Bradley <jos...@databricks.com>: >> >>> Hi Stephen, >>> >>> I used to post those roadmap JIRAs to share instructions for >>> contributing to MLlib and to try to coordinate amongst committers. My >>> feeling was that the coordination aspect was of mixed success, so I did not >>> post one for 2.3. I'm glad you pinged about this; if those were useful, >>> then I can plan on posting one for the release after 2.3. As far as >>> identifying committers' plans, the best option right now is to look for >>> Shepherds in JIRA as well as the few mailing list threads about directions. >>> >>> For myself, I'm mainly focusing on fixing some issues with persistence >>> for custom algorithms in PySpark (done), adding the image schema (done), >>> and using ML Pipelines in Structured Streaming (WIP). >>> >>> Joseph >>> >>> On Wed, Nov 29, 2017 at 6:52 AM, Stephen Boesch <java...@gmail.com> >>> wrote: >>> >>>> There are several JIRA's and/or PR's that contain logic the Data >>>> Science teams that I work with use in their local models. We are trying to >>>> determine if/when these features may gain traction again. In at least one >>>> case all of the work were done but the shepherd said that getting it >>>> committed were of lower priority than other tasks - one specifically >>>> mentioned was the mllib/ml parity that has been ongoing for nearly three >>>> years. >>>> >>>> In order to prioritize work that the ML platform would do it would be >>>> helpful to know at least which if any of those tasks were going to be moved >>>> ahead by the community: since we could then focus on other ones instead of >>>> duplicating the effort. >>>> >>>> In addition there are some engineering code jam sessions that happen >>>> periodically: knowing which features are actively on the roadmap would >>>> *certainly >>>> *influence our selection of work. The roadmaps from 2.2.0 and earlier >>>> were a very good starting point to understand not just the specific work in >>>> progress - but also the current mindset/thinking of the committers in terms >>>> of general priorities. >>>> >>>> So if the same format of document were not available - then what >>>> content *is *that gives a picture of where spark.ml were headed? >>>> >>>> 2017-11-29 6:39 GMT-08:00 Stephen Boesch <java...@gmail.com>: >>>> >>>>> Any further information/ thoughts? >>>>> >>>>> >>>>> >>>>> 2017-11-22 15:07 GMT-08:00 Stephen Boesch <java...@gmail.com>: >>>>> >>>>>> The roadmaps for prior releases e.g. 1.6 2.0 2.1 2.2 were available: >>>>>> >>>>>> 2.2.0 https://issues.apache.org/jira/browse/SPARK-18813 >>>>>> >>>>>> 2.1.0 https://issues.apache.org/jira/browse/SPARK-15581 >>>>>> .. >>>>>> >>>>>> It seems those roadmaps were not available per se' for 2.3.0 and >>>>>> later? Is there a different mechanism for that info? >>>>>> >>>>>> stephenb >>>>>> >>>>> >>>>> >>>> >>> >>> >>> -- >>> >>> Joseph Bradley >>> >>> Software Engineer - Machine Learning >>> >>> Databricks, Inc. >>> >>> [image: http://databricks.com] <http://databricks.com/> >>> >> >> > > > -- > > Joseph Bradley > > Software Engineer - Machine Learning > > Databricks, Inc. > > [image: http://databricks.com] <http://databricks.com/> >
