monitoring

customer-churn-xgboost-neo.md

@@ -398,7 +398,3 @@ sagemaker.Session().delete_endpoint(xgb_predictor.endpoint)
 ```
 
 [< Home](./readme.md)
-
-Appendix:
-https://aws.amazon.com/blogs/machine-learning/predicting-customer-churn-with-amazon-machine-learning/
-https://github.com/awslabs/amazon-sagemaker-examples/blob/master/introduction_to_applying_machine_learning/xgboost_customer_churn/xgboost_customer_churn.ipynb

feature-engineering.md

@@ -390,15 +390,4 @@ the `preprocessed_train` variable.
 
 ![abalone.csv.out](./images/02-lab/abalone.csv.out.png)
 
-## Step 7: Head over to SageMaker console
-
-### Training > Training jobs
-
-### Inference > Models
-
-### Inference > Batch transform jobs
-
 [< Home](./readme.md)
-
-Appendix
-https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker_processing/scikit_learn_data_processing_and_model_evaluation/scikit_learn_data_processing_and_model_evaluation.ipynb

hyperparameter-tuning.md

@@ -368,10 +368,3 @@ g = g.map(plt.scatter, "alpha", "lambda", alpha=0.6)
 ```
 
 [< Home](./readme.md)
-
-Appendix
-https://github.com/awslabs/amazon-sagemaker-examples/blob/master/hyperparameter_tuning/xgboost_direct_marketing/hpo_xgboost_direct_marketing_sagemaker_python_sdk.ipynb
-
-https://github.com/awslabs/amazon-sagemaker-examples/blob/master/hyperparameter_tuning/xgboost_random_log/hpo_xgboost_random_log.ipynb
-
-https://github.com/awslabs/amazon-sagemaker-examples/blob/master/autopilot/sagemaker_autopilot_direct_marketing.ipynb