Hi Jincheng, Thanks a lot for the warm feedback.
I've already read your Table API enhancement google doc. Those enhancements are essential to implement any ML/DL algorithm on Table API. Our two designs are perfectly complementary to each other. :) Will add a section in my google doc for the implementation phased plan. Thanks Weihua jincheng sun <sunjincheng...@gmail.com> 于2018年11月20日周二 下午9:17写道： > Hi Weihua， > Thanks for bring up this discuss! > > I quickly read the google doc，and I fully agree that ML can be well > supported on TableAPI (at some stage in the future). > In fact, Xiaowei and I have already brought up a discussion on enhancing > the Table API. In the first phase, we will add support for > map/flatmap/agg/flatagg in TableAPI. > So I am very happy to be involved in this discussion and will leave a > comment in the good doc later. > > I think It's grateful if you can add a phased implementation plan in google > doc. What to do you think? > > Thanks, > Jincheng > > > Weihua Jiang <weihua.ji...@gmail.com> 于2018年11月20日周二 下午8:53写道： > > > ML Pipeline is the idea brought by Scikit-learn > > <https://arxiv.org/abs/1309.0238>. Both Spark and Flink has borrowed > this > > idea and made their own implementations [Spark ML Pipeline > > <https://spark.apache.org/docs/latest/ml-pipeline.html>, Flink ML > Pipeline > > < > > > https://ci.apache.org/projects/flink/flink-docs-release-1.6/dev/libs/ml/pipelines.html > > >]. > > > > > > > > NOTE: though I am using the term "ML", ML Pipeline shall apply to both ML > > and DL pipelines. > > > > > > ML Pipeline is quite helpful for model composition (i.e. using model(s) > for > > feature engineering) . And it enables logic reuse in train and inference > > phases (via pipeline persistence and load), which is essential for AI > > engineering. ML Pipeline can also be a good base for Flink based AI > > engineering platform if we can make ML Pipeline have good tooling support > > (i.e. meta data human readable). > > > > > > As the Table API will be the unified high level API for both stream and > > batch processing, I want to initiate the design discussion of new Table > > based Flink ML Pipeline. > > > > > > I drafted a design document [1] for this discussion. This design tries to > > create a new ML Pipeline implementation so that concrete ML/DL algorithms > > can fit to this new API to achieve interoperability. > > > > > > Any feedback is highly appreciated. > > > > > > Thanks > > > > Weihua > > > > > > [1] > > > > > https://docs.google.com/document/d/1PLddLEMP_wn4xHwi6069f3vZL7LzkaP0MN9nAB63X90/edit?usp=sharing > > >
