data-science-on-aws/data-science-on-aws resource

Health v2 · maintenance only

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3433 stars · 1084 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Companion Jupyter Notebook repository for the O'Reilly book 'Data Science on AWS', containing end-to-end AI/ML pipeline examples using Amazon SageMaker, Kubeflow, and Amazon EKS. It covers data ingestion, BERT-based NLP model training, model deployment, MLOps pipelines, and streaming analytics on AWS.

Use cases

• learn machine learning on aws with sagemaker
• build end-to-end ml pipelines with kubeflow and sagemaker
• train and deploy a bert text classifier
• learn mlops workflows like model registry and a/b testing
• follow a data science book with hands-on notebooks
• set up streaming analytics with kinesis and sagemaker

When to choose

• you want hands-on, notebook-based learning for AWS ML services like SageMaker, EKS, and Kubeflow
• you are following the O'Reilly 'Data Science on AWS' book and want runnable examples
• you want to learn MLOps concepts such as pipelines, feature stores, and model deployment on AWS

When to avoid

• you need production-ready software rather than educational example code
• you work outside the AWS ecosystem or prefer cloud-agnostic tooling
• you need actively maintained, up-to-date examples - the latest release is mid-2024

Facets

learning-resource · maturity maintenance

machine-learning data-science etl llm-training rag machine-learning data-science cloud-computing large-language-models python cloud jvm aws sagemaker kubeflow jupyter-notebooks oreilly-book mlops bert tutorial natural-language-processing docker kubernetes

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