Merge branch 'master' into lda_topic_modeling

Author: Chris Swierczewski

README.md

@@ -9,13 +9,23 @@ This repository contains example notebooks that show how to apply machine learni
 - [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
+### Introduction to Amazon Algorithms
 
-### Amazon Algorithms - Scientific Detail
+### Scientific Details of Algorithms
 
 ### Advanced Amazon SageMaker Functionality
 
 - [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.
 - [Bring Your Own Tensorflow Model](sagemaker-python-sdk/tensorflow_iris_byom) shows how to bring a model trained anywhere into Amazon SageMaker
+
+## FAQ
+
+*Will these example work outside of Amazon SageMaker?*
+
+- Although most examples utilize key Amazon SageMaker functionality like distributed, managed training or real-time hosted endpoints, these notebooks can be run outside of Amazon SageMaker Notebook Instances with minimal modification (updating IAM role definition and installing the necessary libraries).
+
+*How do I contribute my own example notebook?"
+
+- Although we're extremely excited to receive contributions from the community, we're still working on the best mechanism to take in examples from and external source.  Please bear will us in the short-term if pull requests take longer than expected or are closed.

advanced_functionality/README.md

@@ -0,0 +1,9 @@
+# Advanced Functionality
+
+This directory includes examples which showcase unique functionality available in Amazon SageMaker.  Examples cover a broad range of topics and will utilize a variety of methods, but aim to provide the user with sufficient insight or inspiration to develop within Amazon SageMaker.
+
+Example Notebooks include:
+- *data_distribution_types*: Showcases the difference between two methods for sending data from S3 to Amazon SageMaker Training instances.  This has particular implication for scalability and accuracy of distributed training.
+- *install_r_kernel*: A quick introduction to getting R installed and running within Amazon SageMaker Notebook Instances.
+- *kmeans_bring_your_own_model*: How to use Amazon SageMaker Algorithms containers to bring a pre-trained model to a realtime hosted endpoint without ever needing to think about REST APIs.
+- *r_bring_your_own*: How to containerize an R algorithm using Docker and plumber for hosting so that it can be used in Amazon SageMaker's managed training and realtime hosting.

breast_cancer_prediction/AmazonAIAlgorithmsIO_pb2.py renamed to advanced_functionality/data_distribution_types/AmazonAIAlgorithmsIO_pb2.py

@@ -0,0 +1,2 @@
+### Bring Your Own Model (XGboost)
+`sagemaker-xgboost-bring-your-own-model.ipynb` shows how to train and Xgboost model in scikit-learn and then inject it into Amazon SageMaker's first party XGboost 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.