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A collection of localized (Korean) AWS AI/ML workshop materials for hands-on labs.

License: MIT No Attribution

Jupyter Notebook 95.96% Python 3.89% Shell 0.08% JavaScript 0.04% Dockerfile 0.02% Makefile 0.01%
aws sagemaker ai ml workshop hands-on algorithms deep-learning machine-learning

aws-ai-ml-workshop-kr's Introduction

AWS AI/ML Workshop - Korea

AWS AIML 한글 워크샵 & 예제 모음


디렉토리 구조

본 리포지토리의 예제 코드는 아래 4가지 카테고리로 나뉘어 있습니다. 각 디렉토리별 Readme 파일을 참고하십시오.

  • AI services : Amazon Rekognition, Amazon Textract 등 학습없이 사용 가능한 AIML 서비스 활용 예제
  • Applied AI : Amazon Personalize, Amazon Forecast 등 사용자 데이터를 이용한 커스텀 ML 모델 생성/추론 서비스
  • SageMaker : End-to-end 머신러닝/딥러닝 플랫폼 SageMaker 활용 예제
  • Integration : Greengrass, EMR 등 다른 서비스와의 융합 및 응용사례
  • Generative AI : Generative AI와 관련된 예제 및 관련 서비스 응용사례
  • AWS Neuron : AWS Neuron (Inferentia, Inferentia2, Tranium )의 관련된 예제 및 활용

SageMaker 셀프스터디

다음은 SageMaker를 Self study로 학습하고자 할 때 유용한 정보들입니다.


License

This library is licensed under the Apache 2.0 License. For more details, please take a look at the LICENSE file.


Contributing

Although we're extremely excited to receive contributions from the community, we're still working on the best mechanism to take in examples from external sources. Please bear with us in the short-term if pull requests take longer than expected or are closed. Please read our contributing guidelines if you'd like to open an issue or submit a pull request.


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