[flink-web] branch asf-site updated: Make minor change to the 'Apache Flink ML 2.0.0 Release Announcement' (#498)

[jq...@apache.org]

This is an automated email from the ASF dual-hosted git repository.

jqin pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/flink-web.git


The following commit(s) were added to refs/heads/asf-site by this push:
     new f9d8660  Make minor change to the 'Apache Flink ML 2.0.0 Release Announcement' (#498)
f9d8660 is described below

commit f9d8660204aaebea2b34874360d2aa803fe7f88b
Author: Dong Lin <li...@gmail.com>
AuthorDate: Thu Jan 13 10:57:38 2022 +0800

    Make minor change to the 'Apache Flink ML 2.0.0 Release Announcement' (#498)
---
 _posts/2022-01-07-release-ml-2.0.0.md | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/_posts/2022-01-07-release-ml-2.0.0.md b/_posts/2022-01-07-release-ml-2.0.0.md
index 4cbea98..1468d19 100644
--- a/_posts/2022-01-07-release-ml-2.0.0.md
+++ b/_posts/2022-01-07-release-ml-2.0.0.md
@@ -150,12 +150,14 @@ TensorFlow program).
 ## Algorithm Library
 
 Now that the Flink ML API re-design is done, we started the initiative to add
-off-the-shelf algorithms in Flink ML. As part of this initiative, we borrowed
-ideas from the [Alink](https://github.com/alibaba/alink) project, and worked
-closely with developers of the Alink project to design the new Flink ML APIs,
-refactor, optimize and migrate algorithms from Alink to Flink. Our long-term
-goal is to provide a library of performant algorithms that are easy to use,
-debug and customize for your needs.
+off-the-shelf algorithms in Flink ML. The release of Flink-ML 2.0.0 is closely
+related to project Alink - an Apache Flink ecosystem project open sourced by
+Alibaba. The connection between the Flink community and developers of the Alink
+project dates back to 2017. The project Alink developers have a significant
+contribution in designing the new Flink ML APIs, refactoring, optimizing and
+migrating algorithms from Alink to Flink. Our long-term goal is to provide a
+library of performant algorithms that are easy to use, debug and customize for
+your needs.
 
 We have implemented five algorithms in this release, i.e. logistic regression,
 k-means, k-nearest neighbors, naive bayes and one-hot encoder. For now these