# data-science-on-aws/data-science-on-aws

AI and Machine Learning with Kubeflow, Amazon EKS, and SageMaker

Repository: https://github.com/data-science-on-aws/data-science-on-aws
Canonical: https://ross.abutalabs.com/products/data-science-on-aws
Homepage: https://datascienceonaws.com
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Last push: 2024-07-31T02:44:53+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2377, "days_push": 763, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3433, forks 1084 (observed 2026-08-28T04:08:04.240733+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science, etl, llm-training, rag
- domain: machine-learning, data-science, cloud-computing, large-language-models
- platform: python, cloud, jvm
- tags: aws, sagemaker, kubeflow, jupyter-notebooks, oreilly-book, mlops, bert, tutorial, natural-language-processing, docker, kubernetes

## Member repositories
- data-science-on-aws/data-science-on-aws (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:04.240733+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:37:44.083796+00:00, confidence not recorded.
  - readme: https://github.com/data-science-on-aws/data-science-on-aws (fetched 2026-08-28T04:08:04.240733+00:00, sha 784080a97736)
  - homepage: https://datascienceonaws.com (fetched 2026-08-29T09:32:00.675351+00:00, sha 418b4e25e562)
- Data as of 2026-08-30T08:39:29.467469+00:00.