Merge pull request aws#13 from awslabs/arpin_xgboostchurn_linearforecast

Arpin xgboostchurn linearforecast

Author: djarpin

README.md

@@ -1,17 +1,20 @@
-# Amazon _IRONMAN_ Notebooks
+# Amazon SageMaker Examples
 
-This repository contains example notebooks showing how to apply machine learning and deep learning in Amazon [_IRONMAN_](https://aws.amazon.com/amazon-ai/).
+This repository contains example notebooks that show how to apply machine learning and deep learning in Amazon SageMaker(https://aws.amazon.com/amazon-ai/).
 
 ## Examples
 
 ### Introduction to Applying Machine Learning
 
-- [XGBoost for Direct Marketing](xgboost_direct_marketing)
+- [XGBoost for Direct Marketing](xgboost_direct_marketing) targets potential customers that are most likely to convert based on customer and aggregate level metrics.
+- [PCA and k-means for Movie Clustering](pca_kmeans_movie_clustering) creates clusters of movies based on genre, ratings, and other characteristics.
 
 ### Amazon Algorithms - Basic Functionality
 
 ### Amazon Algorithms - Scientific Detail
 
-### Advanced _IRONMAN_ Functionality
+### Advanced Amazon SageMaker Functionality
 
-- [Installing the R Kernel](install_r_kernel)
+- [Installing the R Kernel](install_r_kernel) shows how to install the R kernel into an Amazon SageMaker Notebook Instance.
+- [Bring Your Own Model for k-means](kmeans_bring_your_own_model) shows how to take a model that's been fit elsewhere and use Amazon SageMaker containers to host.
+- [Bring Your Own Algorithm with R](r_bring_your_own) shows how to bring your own algorithm container to Amazon SageMaker using the R language.

kmeans_bring_your_own_model/README.md

@@ -0,0 +1,3 @@
+# Bring Your Own Model (k-means)
+
+*kmeans_bring_your_own_model.ipynb:* shows how to fit a k-means model in scikit-learn and then inject it into Amazon SageMaker's first party k-means container for scoring.  This addresses the use case where a customer has already trained their model outside of Amazon SageMaker, but wishes to host it for predictions within Amazon SageMaker.