diff --git a/docs/readthedocs/source/doc/PPML/Overview/ppml.md b/docs/readthedocs/source/doc/PPML/Overview/ppml.md
index 003a0867..739d6279 100644
--- a/docs/readthedocs/source/doc/PPML/Overview/ppml.md
+++ b/docs/readthedocs/source/doc/PPML/Overview/ppml.md
@@ -579,103 +579,7 @@ The result should look something like this:
 >
 > 2021-06-18 01:46:20 INFO DistriOptimizer$:180 - [Epoch 2 60032/60000][Iteration 938][Wall Clock 845.747782s] Top1Accuracy is Accuracy(correct: 9696, count: 10000, accuracy: 0.9696)
 
-##### 2.3.2.3.7 Run Trusted Spark XGBoost Regressor
-
-This example shows how to run trusted Spark XGBoost Regressor.
-
-First, make sure that `Boston_Housing.csv` is under `work/data` directory or the same path in the `start-spark-local-xgboost-regressor-sgx.sh`. Replace the value of `RABIT_TRACKER_IP` with your own IP address in the script.
-
-Run the script to run trusted Spark XGBoost Regressor and it would take some time to show the final results:
-
-```bash
-bash work/start-scripts/start-spark-local-xgboost-regressor-sgx.sh
-```
-
-Open another terminal and check the log:
-
-```bash
-sudo docker exec -it spark-local cat /ppml/trusted-big-data-ml/test-bigdl-xgboost-regressor-sgx.log | egrep "prediction" -A19
-```
-
-The result should look something like this:
-
-> | features|label| prediction|
->
-> +--------------------+-----+------------------+
->
-> |[41.5292,0.0,18.1...| 8.5| 8.51994514465332|
->
-> |[67.9208,0.0,18.1...| 5.0| 5.720333099365234|
->
-> |[20.7162,0.0,18.1...| 11.9|10.601168632507324|
->
-> |[11.9511,0.0,18.1...| 27.9| 26.19390106201172|
->
-> |[7.40389,0.0,18.1...| 17.2|16.112293243408203|
->
-> |[14.4383,0.0,18.1...| 27.5|25.952226638793945|
->
-> |[51.1358,0.0,18.1...| 15.0| 14.67484188079834|
->
-> |[14.0507,0.0,18.1...| 17.2|16.112293243408203|
->
-> |[18.811,0.0,18.1,...| 17.9| 17.42863655090332|
->
-> |[28.6558,0.0,18.1...| 16.3| 16.0191593170166|
->
-> |[45.7461,0.0,18.1...| 7.0| 5.300708770751953|
->
-> |[18.0846,0.0,18.1...| 7.2| 6.346951007843018|
->
-> |[10.8342,0.0,18.1...| 7.5| 6.571983814239502|
->
-> |[25.9406,0.0,18.1...| 10.4|10.235769271850586|
->
-> |[73.5341,0.0,18.1...| 8.8| 8.460335731506348|
->
-> |[11.8123,0.0,18.1...| 8.4| 9.193297386169434|
->
-> |[11.0874,0.0,18.1...| 16.7|16.174896240234375|
->
-> |[7.02259,0.0,18.1...| 14.2| 13.38729190826416|
-
-##### 2.3.2.3.8 Run Trusted Spark XGBoost Classifier
-
-This example shows how to run trusted Spark XGBoost Classifier.
-
-Before running the example, download the sample dataset from [pima-indians-diabetes](https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv) dataset. After downloading the dataset, make sure that `pima-indians-diabetes.data.csv` is under `work/data` directory or the same path in the `start-spark-local-xgboost-classifier-sgx.sh`. Replace `path_of_pima_indians_diabetes_csv` with your path of `pima-indians-diabetes.data.csv`  and the value of `RABIT_TRACKER_IP` with your own IP address in the script.
-
-Run the script to run trusted Spark XGBoost Classifier and it would take some time to show the final results:
-
-```bash
-bash start-spark-local-xgboost-classifier-sgx.sh
-```
-
-Open another terminal and check the log:
-
-```bash
-sudo docker exec -it spark-local cat /ppml/trusted-big-data-ml/test-xgboost-classifier-sgx.log | egrep "prediction" -A7
-```
-
-The result should look something like this:
-
-> | f1|  f2| f3| f4|  f5| f6|  f7| f8|label|    rawPrediction|     probability|prediction|
->
-> +----+-----+----+----+-----+----+-----+----+-----+--------------------+--------------------+----------+
->
-> |11.0|138.0|74.0|26.0|144.0|36.1|0.557|50.0| 1.0|[-0.8209581375122...|[0.17904186248779...|    1.0|
->
-> | 3.0|106.0|72.0| 0.0| 0.0|25.8|0.207|27.0| 0.0|[-0.0427864193916...|[0.95721358060836...|    0.0|
->
-> | 6.0|117.0|96.0| 0.0| 0.0|28.7|0.157|30.0| 0.0|[-0.2336160838603...|[0.76638391613960...|    0.0|
->
-> | 2.0| 68.0|62.0|13.0| 15.0|20.1|0.257|23.0| 0.0|[-0.0315906107425...|[0.96840938925743...|    0.0|
->
-> | 9.0|112.0|82.0|24.0| 0.0|28.2|1.282|50.0| 1.0|[-0.7087597250938...|[0.29124027490615...|    1.0|
->
-> | 0.0|119.0| 0.0| 0.0| 0.0|32.4|0.141|24.0| 1.0|[-0.4473398327827...|[0.55266016721725...|    0.0|
-
-##### 2.3.2.3.9 Run Trusted Spark Orca Data
+##### 2.3.2.3.7 Run Trusted Spark Orca Data
 
 This example shows how to run trusted Spark Orca Data.
 
@@ -745,7 +649,7 @@ The result should contain the content look like this:
 >
 >Stopping orca context
 
-##### 2.3.2.3.10 Run Trusted Spark Orca Learn Tensorflow Basic Text Classification
+##### 2.3.2.3.8 Run Trusted Spark Orca Learn Tensorflow Basic Text Classification
 
 This example shows how to run Trusted Spark Orca learn Tensorflow basic text classification.