Welcome to Amazon SageMaker. This site highlights example Jupyter notebooks for a variety of machine learning use cases that you can run in SageMaker.
This site is based on the SageMaker Examples repository on GitHub. To run these notebooks, you will need a SageMaker Notebook Instance or SageMaker Studio. Refer to the SageMaker developer guide's Get Started page to get one of these set up.
On a Notebook Instance, the examples are pre-installed and available from the examples menu item in JupyterLab. On SageMaker Studio, you will need to open a terminal, go to your home folder, then clone the repo with the following:
git clone https://github.com/aws/amazon-sagemaker-examples.gitintro.rst
We recommend the following notebooks as a broad introduction to the capabilities that SageMaker offers. To explore in even more depth, we provide additional notebooks covering even more use cases and frameworks.
introduction_to_applying_machine_learning/xgboost_customer_churn/xgboost_customer_churn_outputs
sagemaker-datawrangler/index sagemaker_processing/spark_distributed_data_processing/sagemaker-spark-processing_outputs sagemaker_processing/basic_sagemaker_data_processing/basic_sagemaker_processing_outputs
hyperparameter_tuning/tensorflow2_mnist/hpo_tensorflow2_mnist_outputs sagemaker-script-mode/sklearn/sklearn_byom_outputs sagemaker-experiments/mnist-handwritten-digits-classification-experiment/mnist-handwritten-digits-classification-experiment_outputs
sagemaker-script-mode/pytorch_bert/deploy_bert_outputs sagemaker_neo_compilation_jobs/pytorch_torchvision/pytorch_torchvision_neo_outputs sagemaker_batch_transform/pytorch_mnist_batch_transform/pytorch-mnist-batch-transform_outputs
sagemaker-lineage/sagemaker-lineage-multihop-queries_outputs sagemaker_model_monitor/introduction/SageMaker-ModelMonitoring_outputs sagemaker-clarify/fairness_and_explainability/fairness_and_explainability_outputs
sagemaker-pipelines/tabular/abalone_build_train_deploy/sagemaker-pipelines-preprocess-train-evaluate-batch-transform_outputs sagemaker-pipelines/tabular/lambda-step/sagemaker-pipelines-lambda-step_outputs
introduction_to_amazon_algorithms/xgboost_abalone/xgboost_abalone_dist_script_mode_outputs introduction_to_applying_machine_learning/huggingface_sentiment_classification/huggingface_sentiment_outputs sagemaker-python-sdk/scikit_learn_iris/scikit_learn_estimator_example_with_batch_transform_outputs sagemaker-python-sdk/mxnet_gluon_mnist/mxnet_mnist_with_gluon_outputs frameworks/tensorflow/get_started_mnist_train_outputs frameworks/pytorch/get_started_mnist_train_outputs introduction_to_applying_machine_learning/mixtral_tune_and_deploy/mixtral-8x7b
aws_sagemaker_studio/index sagemaker-lineage/index
introduction_to_amazon_algorithms/index
end_to_end/fraud_detection/index end_to_end/music_recommendation/index end_to_end/nlp_mlops_company_sentiment/index
patterns/ml_gateway/index
use-cases/index use-cases/examples_by_problem_type
autopilot/index
ingest_data/index
label_data/index
prep_data/index
sagemaker-featurestore/index
training/frameworks
training/algorithms reinforcement_learning/index sagemaker-experiments/index sagemaker-debugger/index training/tuning training/distributed_training/index sagemaker-training-compiler/index sagemaker-script-mode/index training/bring_your_own_container training/management training/heterogeneous-clusters/index
inference/index model-governance/index sagemaker-shadow-variant/index
sagemaker-pipelines/index sagemaker_processing/index sagemaker-spark/index step-functions-data-science-sdk/index sagemaker-notebook-jobs/index
advanced_functionality/index serverless-inference/index
sagemaker-clarify/index scientific_details_of_algorithms/index aws_marketplace/index sagemaker-geospatial/index
contrib/index