+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 > > > > > >
