Update README

Author: Michael Pham

README.md

@@ -109,6 +109,9 @@ These examples that showcase unique functionality available in 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.
 - [Inference Pipeline with SparkML and BlazingText](advanced_functionality/inference_pipeline_sparkml_blazingtext_dbpedia) shows how to deploy an Inference Pipeline with SparkML for data pre-processing and BlazingText for training on the DBPedia 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.
+- [Host Multiple Models with SKLearn](advanced_functionality/multi_model_sklearn_home_value) shows how to deploy multiple models to a realtime hosted endpoint using a multi-model enabled SKLearn container.
 
 ### Amazon SageMaker Neo Compilation Jobs