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batch_transform
creating_marketplace_products
data_distribution_types
handling_kms_encrypted_data
inference_pipeline_sparkml_blazingtext_dbpedia
inference_pipeline_sparkml_xgboost_abalone
inference_pipeline_sparkml_xgboost_car_evaluation
install_r_kernel
kmeans_bring_your_own_model
mxnet_mnist_byom
parquet_to_recordio_protobuf
pipe_bring_your_own
pytorch_extending_our_containers
r_bring_your_own
scikit_bring_your_own
search
tensorflow_bring_your_own
tensorflow_iris_byom
using_marketplace_products
working_with_redshift_data
working_with_tfrecords
xgboost_bring_your_own_model
README.md

README.md

Amazon SageMaker Examples

Advanced Amazon SageMaker Functionality

These examples that showcase unique functionality available in Amazon SageMaker. They 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.

  • 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.
  • Encrypting Your Data shows how to use Server Side KMS encrypted data with Amazon SageMaker training. The IAM role used for S3 access needs to have permissions to encrypt and decrypt data with the KMS key.
  • Using Parquet Data shows how to bring Parquet data sitting in S3 into an Amazon SageMaker Notebook and convert it into the recordIO-protobuf format that many SageMaker algorithms consume.
  • Connecting to Redshift demonstrates how to copy data from Redshift to S3 and vice-versa without leaving Amazon SageMaker Notebooks.
  • Bring Your Own XGBoost Model shows 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.
  • Bring Your Own k-means Model shows how to take a model that's been fit elsewhere and use Amazon SageMaker Algorithms containers to host it.
  • Installing the R Kernel shows how to install the R kernel into an Amazon SageMaker Notebook Instance.
  • Bring Your Own R Algorithm shows how to bring your own algorithm container to Amazon SageMaker using the R language.
  • Bring Your Own scikit Algorithm provides a detailed walkthrough on how to package a scikit learn algorithm for training and production-ready hosting.
  • Bring Your Own MXNet Model shows how to bring a model trained anywhere using MXNet into Amazon SageMaker
  • Bring Your Own TensorFlow Model shows how to bring a model trained anywhere using TensorFlow into Amazon SageMaker
  • Inference Pipeline with SparkML and XGBoost 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 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.
  • Creating Algorithm and Model Package - Listing on AWS Marketplace provides a detailed walkthrough on how to package a scikit learn algorithm to create SageMaker Algorithm and SageMaker Model Package entities that can be used with the enhanced SageMaker Train/Transform/Hosting/Tuning APIs and listed on AWS Marketplace.
  • Using Algorithm and Model Packages - From AWS Marketplace provides a detailed walkthrough on how to use Algorithm and Model Package entities with the enhanced SageMaker Train/Transform/Hosting/Tuning APIs by choosing a canonical product listed on AWS Marketplace.