쿠버네티스와 쿠브플로는 놀라운 데브옵스 도구입니다.이는 별도의 책으로 다뤄야 할 분량이기에 여기서는
자세히 소개하지 못했습니다. 두 도구를 자세히 알아보려면 다음 책을 읽어보세요.
●
『
Kubernetes
:
Up
and
Running
,
2e
』(오라일리,
2019
)
●
『
Kubeflow
Operations
Guide
』 (오라일리,
2020
)
●
『
Kubeflow
for
Machine
Learning
』(오라일리,
2020
)
9.7
요약
이 장에서는 원격 클라우드 스토리지 버킷을 통해 모델을 배포하여 데이터 과학과 데브옵스 배
포 생애 주기를 분할하고, 모델을 최적화하여 예측 지연 시간과 모델 메모리 설치 공간을 줄이
거나 배포를 확장하는 방법과 같은 고급 배포 시나리오를 다뤘습니다.
다음 장에서는 모든 개별 파이프라인 구성 요소를 단일 머신러닝 파이프라인으로 결합하여 재
현 가능한 머신러닝 워크플로를 제공하겠습니다.
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