- 각주1, 초매개변수(Hyperparameter) : https://en.m.wikipedia.org/wiki/Hyperparameter
- 각주3, Google Colaboratory(colab) : https://colab.research.google.com/notebooks/welcome.ipynb?hl=ko
- AWS 접속 : https://aws.amazon.com/ko/
- 각주4, AWS요금제 : https://aws.amazon.com/ko/pricing/
- 각주5, AWS SES : https://docs.aws.amazon.com/ko_kr/ses/latest/dg/create-shared-credentials-file.html
- 캐글 가입 및 데이터 다운로드 : https://www.kaggle.com/
- 강아지 데이터 다운로드 : https://www.kaggle.com/c/dog-breed-identification/data
- 고양이 데이터 다운로드 : https://www.kaggle.com/ma7555/cat-breeds-dataset
- 코랩 사용법 : https://colab.research.google.com/notebooks/welcome.ipynb?hl=ko
- 각주6, 코랩 : https://colab.research.google.com/notebooks/intro.ipynb
- 각주8, BASE64 : https://ko.wikipedia.org/wiki/베이스64
- 각주9, 사용시간제한 : https://research.google.com/colaboratory/faq.html#idle-timeouts
- 각주10, AI를 개념화한 그림 출처 : https://live.lge.co.kr/live_with_ai_01/
- AWS 이미지인식 : https://aws.amazon.com/ko/rekognition
- AWS 프리티어 : https://aws.amazon.com/ko/rekognition/pricing/?loc=ft#Free_Tier
- AWS Rekognition : https://lnkd.in/gQh7zJkw
- 구글 Open API : https://cloud.google.com/vision?hl=ko
- 구글 이용료 : https://cloud.google.com/vision?hl=ko#pricing
- 각주11, AWS 콘솔 : https://ap-northeast-2.console.aws.amazon.com/rekognition/home?region=ap-northeast-2#/label-detection
- 각주12, Open API 문서 링크 : https://boto3.amazonaws.com/v1/documentation/api/1.9.42/reference/services/rekognition.html
- 각주13, Open API 문서 링크 : https://docs.aws.amazon.com/rekognition/latest/dg/API_DetectLabels.html
- 각주1, 지도학습 : https://ko.m.wikipedia.org/wiki/지도_학습
- 소스코드 경로: https://github.com/roadbookgit/DLService
- 각주2, 추론 : https://ko.m.wikipedia.org/wiki/추론
- 각주3, Teachable Machine : https://teachablemachine.withgoogle.com/
- 각주4, Teachable Machine 홈페이지 : https://teachablemachine.withgoogle.com/
- 각주5, Teachable Machine 사이트 : https://teachablemachine.withgoogle.com/
- 각주1, fastapi : https://fastapi.tiangolo.com/ko/
- 각주2, uvicorn : http://www.uvicorn.org/
- Rest API : https://ko.wikipedia.org/wiki/REST
- 각주3, curl : https://curl.se/
- 각주4, GET : https://datatracker.ietf.org/doc/html/rfc2616#section-9.3
- 각주5, JSON : https://datatracker.ietf.org/doc/html/rfc7159
- 각주6, FastAPI 공식 문서: https://fastapi.tiangolo.com/ko/
- 각주7, swagger : https://swagger.io
- 각주8, Streamlit : https://streamlit.io/about
- 각주9. Google X : https://x.company/projects/
- 각주10, Streamlit 사용법 : https://docs.streamlit.io/library/get-started/main-concepts
- 여기서 잠깐 (예): $ streamlit run https://raw.githubusercontent.com/streamlit/demo-uber-nyc-pickups/ master/streamlit_app.py
- 각주11, 코드 스니펫 Code Snippet : 재사용 가능한 소스코드, 기계어, 텍스트의 작은 부분을 일컫는 프로그래밍 용어입니다(https://ko.wikipedia.org/wiki/스니펫).
- 각주1, EC2 프리티어 : https://aws.amazon.com/ko/free/
- 각주3, 인스턴스 유형 : https://aws.amazon.com/ko/ec2/instance-types/
- putty 설치 : https://www.chiark.greenend.org.uk/~sgtatham/putty/latest.html
- 아마존 VPC : https://docs.aws.amazon.com/ko_kr/vpc/latest/userguide/what-is-amazon-vpc.html
- 방화벽 : https://docs.aws.amazon.com/ko_kr/AWSEC2/latest/UserGuide/authorizing-access-to-an-instance.html
- 각주5, S3 객체 스토리지 서비스 : https://docs.aws.amazon.com/ko_kr/AmazonS3/latest/userguide/Welcome.html
- 각주6, S3 프리 티어 : https://aws.amazon.com/ko/free/
- 각주7, S3 생성 : https://docs.aws.amazon.com/ko_kr/AmazonS3/latest/userguide/creating-bucket.html
- 각주8, 버킷 이름 지정 규칙 : https://docs.aws.amazon.com/ko_kr/AmazonS3/latest/userguide/bucketnamingrules.html
- 각주9, S3 삭제 : https://docs.aws.amazon.com/ko_kr/AmazonS3/latest/userguide/deleting-object-bucket.html
- 각주1, 그림 5-1 출처 : https://www.linkedin.com/pulse/pipelines-production-ml-systems-ivelin-angelov?articleId=6628821083674030080
- 각주9, 여러 스텝을 사용하여 pipeline 구성 : https://docs.aws.amazon.com/sagemaker/latest/dg/build-and-manage-steps.html
- 각주10, pipeline실행 간으한 소스코드 링크 : https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker-pipelines/tabular/abalone_build_train_deploy/sagemaker-pipelines-preprocess-train-evaluate-batch-transform.ipynb
- 각주11, 매직 명령어 : https://ipython.readthedocs.io/en/stable/interactive/magics.html
- 각주12, XGBoost 알고리즘 : https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html
- 각주13, Estimator: https://sagemaker.readthedocs.io/en/stable/api/training/estimators.html
- 각주14, GPU 지원 가속 타입 : https://aws.amazon.com/ko/machine-learning/elastic-inference/
- 각주1, 기울기 소실 : https://en.wikipedia.org/wiki/Vanishing_gradient_problem
- AWS SageMaker 요금: https://aws.amazon.com/ko/sagemaker/pricing/
- AWS 서비스 할당량 : https://docs.aws.amazon.com/ko_kr/general/latest/gr/sagemaker.html
- 각주2, cache_config : https://docs.aws.amazon.com/sagemaker/latest/dg/pipelines-caching.html
- 각주4, Lambda 생성 : https://aws.amazon.com/ko/blogs/machine-learning/use-a-sagemaker-pipeline-lambda-step-for-lightweight-model-deployments/
- 각주5, Pipeline 실행 : https://docs.aws.amazon.com/sagemaker/latest/dg/run-pipeline.html
- Anaconda 설치 : https://www.anaconda.com/products/individual#windows
- Anaconda 설치 : https://www.anaconda.com/downloads#macos