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The following commit(s) were added to refs/heads/master by this push:
     new fbaeb22  Add Viewflow to the Ecosystem (#398)
fbaeb22 is described below

commit fbaeb2283b944f66867aae82dba6bf7899456234
Author: gpodevijn <[email protected]>
AuthorDate: Mon Mar 29 11:47:52 2021 +0200

    Add Viewflow to the Ecosystem (#398)
---
 landing-pages/site/content/en/ecosystem/_index.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/landing-pages/site/content/en/ecosystem/_index.md 
b/landing-pages/site/content/en/ecosystem/_index.md
index 10c5609..6a5ff3c 100644
--- a/landing-pages/site/content/en/ecosystem/_index.md
+++ b/landing-pages/site/content/en/ecosystem/_index.md
@@ -63,6 +63,8 @@ If you would you like to be included on this page, please 
reach out to the [Apac
 
 [Airflow Plugins](https://github.com/airflow-plugins/) - Central collection of 
repositories of various plugins for Airflow, including mailchimp, trello, sftp, 
GitHub, etc.
 
+[Apache-Liminal-Incubating](https://github.com/apache/incubator-liminal) -  
Liminal provides a domain-specific-language (DSL) to build ML/AI workflows on 
top of Apache Airflow. Its goal is to operationalise the machine learning 
process, allowing data scientists to quickly transition from a successful 
experiment to an automated pipeline of model training, validation, deployment 
and inference in production.
+
 [Chartis](https://github.com/trejas/chartis) - Python package to convert 
Common Workflow Language (CWL) into Airflow DAG.
 
 [CWL-Airflow](https://github.com/Barski-lab/cwl-airflow) - Python package to 
extend Apache-Airflow 1.10.11 functionality with CWL v1.2 support.
@@ -89,4 +91,4 @@ If you would you like to be included on this page, please 
reach out to the [Apac
 
 [simple-dag-editor](https://github.com/ohadmata/simple-dag-editor) - Zero 
configuration Airflow plugin that let you manage your DAG files.
 
-[Apache-Liminal-Incubating](https://github.com/apache/incubator-liminal) -  
Liminal provides a domain-specific-language (DSL) to build ML/AI workflows on 
top of Apache Airflow. Its goal is to operationalise the machine learning 
process, allowing data scientists to quickly transition from a successful 
experiment to an automated pipeline of model training, validation, deployment 
and inference in production.
+[Viewflow](https://github.com/datacamp/viewflow) - An Airflow-based framework 
that allows data scientists to create data models without writing Airflow code.

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