Thank you all for the vote and the comments! Based on the response received in this thread, we will go ahead and release Flink ML 2.1.0 RC1 today.
Cheers, Zhipeng Jing Ge <j...@ververica.com> 于2022年6月25日周六 06:30写道： > +1 > > Looking forward to the new release! Thanks! > > Best regards, > Jing > > > On Fri, Jun 24, 2022 at 4:56 AM Becket Qin <becket....@gmail.com> wrote: > > > +1. > > > > It looks like we have some decent progress on Flink ML :) > > > > Thanks, > > > > Jiangjie (Becket) Qin > > > > On Fri, Jun 24, 2022 at 8:29 AM Dong Lin <lindon...@gmail.com> wrote: > > > > > Hi Zhipeng and Yun, > > > > > > Thanks for starting the discussion. +1 for the Flink ML 2.1.0 release. > > > > > > Cheers, > > > Dong > > > > > > On Thu, Jun 23, 2022 at 11:15 AM Zhipeng Zhang < > zhangzhipe...@gmail.com> > > > wrote: > > > > > > > Hi devs, > > > > > > > > Yun and I would like to start a discussion for releasing Flink ML > > > > <https://github.com/apache/flink-ml> 2.1.0. > > > > > > > > In the past few months, we focused on improving the infra (e.g. > memory > > > > management, benchmark infra, online training, python support) of > Flink > > ML > > > > by implementing, benchmarking, and optimizing 9 new algorithms in > Flink > > > ML. > > > > Our results have shown that Flink ML is able to meet or exceed the > > > > performance of selected algorithms in alternative popular ML > libraries. > > > > > > > > Please see below for a detailed list of improvements: > > > > > > > > - A set of representative machine learning algorithms: > > > > - feature engineering > > > > - MinMaxScaler ( > > > https://issues.apache.org/jira/browse/FLINK-25552) > > > > - StringIndexer ( > > > https://issues.apache.org/jira/browse/FLINK-25527 > > > > ) > > > > - VectorAssembler ( > > > > https://issues.apache.org/jira/browse/FLINK-25616 > > > > ) > > > > - StandardScaler ( > > > > https://issues.apache.org/jira/browse/FLINK-26626) > > > > - Bucketizer ( > > https://issues.apache.org/jira/browse/FLINK-27072) > > > > - online learning: > > > > - OnlineKmeans ( > > > https://issues.apache.org/jira/browse/FLINK-26313) > > > > - OnlineLogisiticRegression ( > > > > https://issues.apache.org/jira/browse/FLINK-27170) > > > > - regression: > > > > - LinearRegression ( > > > > https://issues.apache.org/jira/browse/FLINK-27093) > > > > - classification: > > > > - LinearSVC ( > https://issues.apache.org/jira/browse/FLINK-27091 > > ) > > > > - Evaluation: > > > > - BinaryClassificationEvaluator ( > > > > https://issues.apache.org/jira/browse/FLINK-27294) > > > > - A benchmark framework for Flink ML. ( > > > > https://issues.apache.org/jira/browse/FLINK-26443) > > > > - A website for Flink ML users ( > > > > https://nightlies.apache.org/flink/flink-ml-docs-stable/) > > > > - Python support for Flink ML algorithms ( > > > > https://issues.apache.org/jira/browse/FLINK-26268, > > > > https://issues.apache.org/jira/browse/FLINK-26269) > > > > - Several optimizations for FlinkML infrastructure ( > > > > https://issues.apache.org/jira/browse/FLINK-27096, > > > > https://issues.apache.org/jira/browse/FLINK-27877) > > > > > > > > With the improvements and throughput benchmarks we have made, we > think > > it > > > > is time to release Flink ML 2.1.0, so that interested developers in > the > > > > community can try out the new Flink ML infra to develop algorithms > with > > > > high throughput and low latency. > > > > > > > > If there is any concern, please let us know. > > > > > > > > > > > > Best, > > > > Yun and Zhipeng > > > > > > > > > > -- best, Zhipeng
