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Updated: README to include video games, TensorFlow BYOM, and basic setup #144

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Updated: README to include video games, TensorFlow BYOM, and basic setup
djarpin Dec 13, 2017
9dfc3ea
Fixed: grammar on video games description
djarpin Dec 13, 2017
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11 changes: 10 additions & 1 deletion README.md
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Expand Up @@ -13,6 +13,7 @@ These examples provide a gentle introduction to machine learning concepts as the
- [Time-series Forecasting](introduction_to_applying_machine_learning/linear_time_series_forecast) generates a forecast for topline product demand using Amazon SageMaker's Linear Learner algorithm.
- [Cancer Prediction](introduction_to_applying_machine_learning/breast_cancer_prediction) predicts Breast Cancer based on features derived from images, using SageMaker's Linear Learner.
- [Ensembling](introduction_to_applying_machine_learning/ensemble_modeling) predicts income using two Amazon SageMaker models to show the advantages in ensembling.
- [Video Game Sales](introduction_to_applying_machine_learning/video_game_sales) develops a binary prediction model for the success of video games based on review scores.

### Introduction to Amazon Algorithms

Expand Down Expand Up @@ -48,6 +49,7 @@ These examples that showcase unique functionality available in Amazon SageMaker.
- [Bring Your Own R Algorithm](advanced_functionality/r_bring_your_own) shows how to bring your own algorithm container to Amazon SageMaker using the R language.
- [Installing the R Kernel](advanced_functionality/install_r_kernel) shows how to install the R kernel into an Amazon SageMaker Notebook Instance.
- [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 TensorFlow Model](advanced_functionality/tensorflow_iris_byom) shows how to bring a model trained anywhere using TensorFlow into Amazon SageMaker

### Amazon SageMaker TensorFlow and MXNet Pre-Built Containers and the Python SDK

Expand All @@ -68,10 +70,17 @@ These examples focus on the Amazon SageMaker Python SDK which allows you to writ
These Amazon SageMaker examples fully illustrate a concept, but may require some additional configuration on the users part to complete.

- [Bring Your Own MXNet Model](under_development/mxnet_mnist_byom) shows how to bring a model trained anywhere using MXNet into Amazon SageMaker
- [Bring Your Own TensorFlow Model](under_development/tensorflow_iris_byom) shows how to bring a model trained anywhere using TensorFlow into Amazon SageMaker

## FAQ

*What do I need in order to get started?*

- The quickest setup to run example notebooks includes:
- An [AWS account](http://docs.aws.amazon.com/sagemaker/latest/dg/gs-account.html)
- Proper [IAM User and Role](http://docs.aws.amazon.com/sagemaker/latest/dg/authentication-and-access-control.html) setup
- An [Amazon SageMaker Notebook Instance](http://docs.aws.amazon.com/sagemaker/latest/dg/gs-setup-working-env.html)
- An [S3 bucket](http://docs.aws.amazon.com/sagemaker/latest/dg/gs-config-permissions.html)

*Will these examples work outside of Amazon SageMaker Notebook Instances?*

- 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).
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1 change: 1 addition & 0 deletions advanced_functionality/README.md
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Expand Up @@ -13,3 +13,4 @@ These examples that showcase unique functionality available in Amazon SageMaker.
- [Installing the R Kernel](install_r_kernel) shows how to install the R kernel into an Amazon SageMaker Notebook Instance.
- [Bring Your Own R Algorithm](r_bring_your_own) shows how to bring your own algorithm container to Amazon SageMaker using the R language.
- [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 TensorFlow Model](tensorflow_iris_byom) shows how to bring a model trained anywhere using TensorFlow into Amazon SageMaker
1 change: 1 addition & 0 deletions introduction_to_applying_machine_learning/README.md
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Expand Up @@ -9,3 +9,4 @@ These examples provide a gentle introduction to machine learning concepts as the
- [Time-series Forecasting](linear_time_series_forecast) generates a forecast for topline product demand using Amazon SageMaker's Linear Learner algorithm.
- [Cancer Prediction](breast_cancer_prediction) predicts Breast Cancer based on features derived from images, using SageMaker's Linear Learner.
- [Ensembling](ensemble_modeling) predicts income using two Amazon SageMaker models to show the advantages in ensembling.
- [Video Game Sales](video_game_sales) develops a binary prediction model for the success of video games based on review scores.