[dolphinscheduler-website] branch asf-site updated: Automated deployment: d0d5befab4a6485e9e62794ebd29915f0e7796aa

[github-bot]

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

github-bot pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/dolphinscheduler-website.git


The following commit(s) were added to refs/heads/asf-site by this push:
     new 2d0f226  Automated deployment: d0d5befab4a6485e9e62794ebd29915f0e7796aa
2d0f226 is described below

commit 2d0f2269fa0b9e1c22b00ab7d73b5f91915aa810
Author: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
AuthorDate: Wed Dec 1 11:19:10 2021 +0000

    Automated deployment: d0d5befab4a6485e9e62794ebd29915f0e7796aa
---
 .../blog/Lizhi case study(en) blog correction.html |  36 ++++++++++++++-------
 .../blog/Lizhi case study(en) blog correction.json |   2 +-
 img/present1.jpg                                   | Bin 0 -> 24709 bytes
 img/present2.jpg                                   | Bin 0 -> 24026 bytes
 img/present3.jpg                                   | Bin 0 -> 24242 bytes
 img/streamline.png                                 | Bin 0 -> 24101 bytes
 6 files changed, 25 insertions(+), 13 deletions(-)

diff --git a/en-us/blog/Lizhi case study(en) blog correction.html 
b/en-us/blog/Lizhi case study(en) blog correction.html
index f495200..6cfbeef 100644
--- a/en-us/blog/Lizhi case study(en) blog correction.html      
+++ b/en-us/blog/Lizhi case study(en) blog correction.html      
@@ -63,27 +63,39 @@
 <p>After selecting the DolphinScheduler, the Lizhi machine learning platform 
carries out re-development based on it and applies the achievements to actual 
business scenarios, which are mainly about recommendation and risk control. 
Recommendation scenarios cover recommendation of voice, anchor, live broadcast, 
podcast, friend, etc., and risk control scenarios cover risk control in 
payment, advertising, and comment, etc.
 At the technical level of the platform, Lizhi optimizes the extended modules 
for the five paradigms of machine learning, i.e. obtaining training samples, 
data preprocessing, model training, model evaluation, and model release.</p>
 <p>A simple xgboost case:</p>
-<div align=center>
-<img 
src="https://imgpp.com/images/2021/11/30/32db43420c7c44e28ff2fb7be27ec79c.md.png"/>
-</div>
+<p align="center">
+  <img src="/img/streamline.png" alt="streamline"  width="60%" />
+  <p align="center">
+        <em>streamline</em>
+  </p>
+</p>
 <h3>1. Obtaining training samples</h3>
 <p>At present, Lizhi does not directly select data from Hive, and joins the 
union, splitting the sample afterward, but directly processes the sample by 
shell nodes.</p>
 <h3>2. Data preprocessing</h3>
 <p>Transformer&amp; custom preprocessing configuration file, use the same 
configuration for online training, and feature preprocessing is performed after 
the feature is obtained. It contains the itemType and its feature set to be 
predicted, the user’s userType and its feature set, as well as the associated 
and crossed itemType and its feature set. Define the transformer function for 
each feature preprocessing, supports custom transformer and hot update, 
xgboost, and tf model feature prep [...]
-<div align=center>
-<img src="https://imgpp.com/images/2021/11/30/1afaee9a4142648f0.md.jpg"/>
-</div>
+<p align="center">
+  <img src="/img/present1.jpg" alt="training data preprocess"  width="60%" />
+  <p align="center">
+        <em>Training data preprocess</em>
+  </p>
+</p>
 <h3>3. Xgboost training</h3>
 <p>It supports w2v, xgboost, tf model training modules. The training modules 
are first packaged with TensorFlow or PyTorch and then packaged into 
DolphinScheduler modules.
 For example, in the xgboost training process, use Python to package the 
xgboost training script into the xgboost training node of DolphinScheduler, and 
show the parameters required for training on the interface. The file exported 
by “training set data preprocessing” is input to the training node through 
HDFS.</p>
-<div align=center>
-<img src="https://imgpp.com/images/2021/11/23/3.md.png"/>
-</div>
+<p align="center">
+  <img src="/img/present3.jpg" alt="Xgboost training"  width="60%" />
+  <p align="center">
+        <em>Xgboost training</em>
+  </p>
+</p>
 <h3>4. Model release</h3>
 <p>The release model will send the model and preprocessing configuration files 
to HDFS and insert records into the model release table. The model service will 
automatically identify the new model, update the model, and provide online 
prediction services to the external.</p>
-<div align=center>
-<img src="https://imgpp.com/images/2021/11/30/2c4b9ff8072e348ee.md.jpg"/>
-</div>
+<p align="center">
+  <img src="/img/present2.jpg" alt="Model release"  width="60%" />
+  <p align="center">
+        <em>model release</em>
+  </p>
+</p>
 <p>Haibin Yu said that due to historical and technical limitations, Lizhi has 
not yet built a machine learning platform like Ali PAI, but the practice has 
proved that similar platform functions can be achieved based on 
DolphinScheduler.</p>
 <p>In addition, Lizhi has also carried out many re-developments based on 
DolphinScheduler to make the scheduling system more in line with actual 
business needs, such as:</p>
 <ol>
diff --git a/en-us/blog/Lizhi case study(en) blog correction.json 
b/en-us/blog/Lizhi case study(en) blog correction.json
index f37b05e..e785ee0 100644
--- a/en-us/blog/Lizhi case study(en) blog correction.json      
+++ b/en-us/blog/Lizhi case study(en) blog correction.json      
@@ -1,6 +1,6 @@
 {
   "filename": "Lizhi case study(en) blog correction.md",
-  "__html": "<h1>A Formidable Combination of Lizhi Machine Learning 
Platform&amp; DolphinScheduler Creates New Paradigm for Data Process in the 
Future</h1>\n<blockquote>\n<p>Editor's word: The online audio industry is a 
blue ocean market in China nowadays. According to CIC data, the market size of 
China’s online audio industry has grown from 1.6 billion yuan in 2016 to 13.1 
billion yuan in 2020, with a compound annual growth rate of 69.4%. With the 
popularity of the Internet of Things, a [...]
+  "__html": "<h1>A Formidable Combination of Lizhi Machine Learning 
Platform&amp; DolphinScheduler Creates New Paradigm for Data Process in the 
Future</h1>\n<blockquote>\n<p>Editor's word: The online audio industry is a 
blue ocean market in China nowadays. According to CIC data, the market size of 
China’s online audio industry has grown from 1.6 billion yuan in 2016 to 13.1 
billion yuan in 2020, with a compound annual growth rate of 69.4%. With the 
popularity of the Internet of Things, a [...]
   "link": "/dist/en-us/blog/Lizhi case study(en) blog correction.html",
   "meta": {}
 }
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