delete notebooks + remove references from README and rst files (aws#3104)

Co-authored-by: EC2 Default User <[email protected]>

Author: atqy

README.md

@@ -170,7 +170,6 @@ These examples that showcase unique functionality available in Amazon SageMaker.
 - [Bring Your Own scikit Algorithm](advanced_functionality/scikit_bring_your_own) provides a detailed walkthrough on how to package a scikit learn algorithm for training and production-ready hosting.
 - [Bring Your Own MXNet Model](advanced_functionality/mxnet_mnist_byom) shows how to bring a model trained anywhere using MXNet into Amazon SageMaker.
 - [Bring Your Own TensorFlow Model](advanced_functionality/tensorflow_iris_byom) shows how to bring a model trained anywhere using TensorFlow into Amazon SageMaker.
-- [Inference Pipeline with SparkML and XGBoost](advanced_functionality/inference_pipeline_sparkml_xgboost_abalone) shows how to deploy an Inference Pipeline with SparkML for data pre-processing and XGBoost for training on the Abalone dataset. The pre-processing code is written once and used between training and inference.
 - [Experiment Management Capabilities with Search](advanced_functionality/search) shows how to organize Training Jobs into projects, and track relationships between Models, Endpoints, and Training Jobs.
 - [Host Multiple Models with Your Own Algorithm](advanced_functionality/multi_model_bring_your_own) shows how to deploy multiple models to a realtime hosted endpoint with your own custom algorithm.
 - [Host Multiple Models with XGBoost](advanced_functionality/multi_model_xgboost_home_value) shows how to deploy multiple models to a realtime hosted endpoint using a multi-model enabled XGBoost container.

advanced_functionality/README.md

@@ -17,4 +17,3 @@ These examples that showcase unique functionality available in Amazon SageMaker.
 - [Bring Your Own scikit Algorithm](scikit_bring_your_own) provides a detailed walkthrough on how to package a scikit learn algorithm for training and production-ready hosting.
 - [Bring Your Own MXNet Model](mxnet_mnist_byom) shows how to bring a model trained anywhere using MXNet into Amazon SageMaker
 - [Bring Your Own TensorFlow Model](tensorflow_iris_byom) shows how to bring a model trained anywhere using TensorFlow into Amazon SageMaker
-- [Inference Pipeline with SparkML and XGBoost](inference_pipeline_sparkml_xgboost_abalone) shows how to deploy an Inference Pipeline with SparkML for data pre-processing and XGBoost for training on the Abalone dataset. The pre-processing code is written once and used between training and inference.