Hi Yun, Can't wait to see your design.
Thanks Weihua Yun Gao <yungao...@aliyun.com.invalid> 于2018年11月21日周三 上午12:43写道： > Hi Weihua, > > Thanks for the exciting proposal! > > I have quickly read through it, and I really appropriate the idea of > providing the ML Pipeline API similar to the commonly used library > scikit-learn, since it greatly reduce the learning cost for the AI > engineers to transfer to the Flink platform. > > Currently we are also working on a related issue, namely enhancing the > stream iteration of Flink to support both SGD and online learning, and it > also support batch training as a special case. we have had a rough design > and will start a new discussion in the next few days. I think the enhanced > stream iteration will help to implement Estimators directly in Flink, and > it may help to simplify the online learning pipeline by eliminating the > requirement to load the models from external file systems. > > I will read the design doc more carefully. Thanks again for sharing > the design doc! > > Yours sincerely > Yun Gao > > > ------------------------------------------------------------------ > 发件人：Weihua Jiang <weihua.ji...@gmail.com> > 发送时间：2018年11月20日(星期二) 20:53 > 收件人：dev <dev@flink.apache.org> > 主 题：[DISCUSS] Embracing Table API in Flink ML > > 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 > >
