This is an automated email from the ASF dual-hosted git repository.
liuxun pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/submarine.git
The following commit(s) were added to refs/heads/master by this push:
new 3ac2603 SUBMARINE-789. Update the link of HitCount
3ac2603 is described below
commit 3ac26033844e0d98250383c6dfe71dac26be8048
Author: aeioulisa <[email protected]>
AuthorDate: Wed Apr 14 07:57:19 2021 +0000
SUBMARINE-789. Update the link of HitCount
### What is this PR for?
The link of HitCount doesn't work on README now and we would move the
domain from .io to .com to fix it.
### What type of PR is it?
[Bug Fix]
### Todos
* [ ] - Task
### What is the Jira issue?
https://issues.apache.org/jira/browse/SUBMARINE-789
### How should this be tested?
### Screenshots (if appropriate)

### Questions:
* Do the license files need updating? No
* Are there breaking changes for older versions? No
* Does this need new documentation? No
Author: aeioulisa <[email protected]>
Author: Lisa <[email protected]>
Signed-off-by: Liu Xun <[email protected]>
Closes #559 from aeioulisa/SUBMARINE-789 and squashes the following commits:
a9cb8cb [aeioulisa] Fix head type in README
3aefdff [Lisa] Update the link of HitCount
---
README.md | 3 ++-
1 file changed, 2 insertions(+), 1 deletion(-)
diff --git a/README.md b/README.md
index d460821..0bad1d0 100644
--- a/README.md
+++ b/README.md
@@ -16,9 +16,10 @@

-[](https://travis-ci.org/apache/submarine)
[](https://www.apache.org/licenses/LICENSE-2.0.html)
[](http://hits.dwyl.io/apache/submarine)
[](https://badge.fury.io/py/apache-submarine)
+[](https://travis-ci.org/apache/submarine)
[](https://www.apache.org/licenses/LICENSE-2.0.html)
[](http://hits.dwyl.io/apache/submarine)
[](https://badge.fury.io/py/apache-submarine)
</div>
+
# What is Apache Submarine?
**Apache Submarine** (Submarine for short) is an **End-to-End Machine Learning
Platform** to allow data scientists to create end-to-end machine learning
workflows. On **Submarine**, data scientists can finish each stage in the ML
model lifecycle, including data exploration, data pipeline creation, model
training, serving, and monitoring.
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