# Amazon SageMaker Examples

### Amazon SageMaker Pre-Built Framework Containers and the Python SDK

#### Pre-Built Deep Learning Framework Containers

These examples focus on the Amazon SageMaker Python SDK which allows you to write idiomatic TensorFlow or MXNet and then train or host in pre-built containers.

- [CIFAR-10 with Chainer and ChainerMN](chainer_cifar10)
- [Sentiment Analysis with Chainer](chainer_sentiment_analysis)
- [MNIST with Chainer](chainer_mnist)
- [Sentiment Analysis with MXNet Gluon](mxnet_gluon_sentiment)
- [IRIS with Scikit-learn](scikit_learn_iris)
- [Visualize Amazon SageMaker Training Jobs with TensorBoard (CIFAR-10, TensorFlow 2.2)](tensorboard_keras)
- [Managed Spot Training on TensorFlow](managed_spot_training_tensorflow_estimator)



#### Pre-Built Machine Learning Framework Containers

These examples focus on building standard Machine Learning models powered by frameworks like Apache Spark or Scikit-learn using SageMaker Python SDK.

- [Pipeline Inference with Scikit-learn and LinearLearner](scikit_learn_inference_pipeline)