이 과정에서는 Google Cloud에서 프로덕션 ML 시스템 배포, 평가, 모니터링, 운영을 위한 MLOps 도구와 권장사항을 소개합니다. MLOps는 프로덕션에서 ML 시스템을 배포, 테스트, 모니터링, 자동화하는 방법론입니다. 머신러닝 엔지니어링 전문가들은 배포된 모델의 지속적인 개선과 평가를 위해 도구를 사용합니다. 이들이 협력하거나 때론 그 역할을 하는 데이터 과학자는 고성능 모델을 빠르고 정밀하게 배포할 수 있도록 모델을 개발합니다.
MLOps (Machine Learning Operations) Fundamentals 한국어
MLOps (Machine Learning Operations) Fundamentals 한국어Google Cloud
About this Course
What you will learn
Identify and use core technologies required to support effective MLOps.
Configure and provision Google Cloud architectures for reliable and effective MLOps environments.
Adopt the best CI/CD practices in the context of ML systems.
Implement reliable and repeatable training and inference workflows.
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
Syllabus - What you will learn from this course
MLOps 기초 소개
이 모듈에서는 과정을 간략히 설명합니다.
MLOps를 사용하는 이유 및 조건
이 모듈에서는 머신러닝을 운영 관점에서 살펴봅니다. 문제 정의부터 해결까지 전체 시스템을 대상으로 한다는 뜻입니다.
기본 Kubernetes 구성요소의 이해(선택사항)
이 모듈은 선택사항입니다.
AI Platform 파이프라인 소개
이 모듈에서는 Google Cloud 제품인 AI Platform Pipelines를 소개합니다. Google Cloud 서비스와 함께 MLOps를 더 쉽고 원활하며 확장 가능하게 만들어 주는 제품입니다.
AI Platform의 학습, 조정, 서빙
이 모듈에서는 AI Platform에서 Jupyter 노트북으로 직접 모델을 학습, 조정, 서빙하는 방법을 다룹니다.
AI Platform에서 사용하는 Kubeflow Pipelines
이 모듈에서는 Kubeflow 파이프라인을 사용하기 전에 앞서 설명한 학습 및 조정 프로세스를 자동화할 것입니다. Jupyterlab 노트북에서 프로세스의 모든 단계를 수동으로 트리거할 필요 없이 다양한 단계를 Kubeflow 파이프라인으로 표현한 후 클릭 한 번으로 프로세스 전체를 트리거할 수 있습니다.
AI Platform에서 사용하는 Kubeflow Pipelines용 CI/CD
이 모듈에서는 Kubeflow Pipelines용 CI/CD를 다룹니다. 자동화된 Kubeflow 파이프라인을 빌드하는 방법을 알아봤습니다. 그렇다면 어떻게 이 파이프라인을 지속적 통합 스택에 통합할 수 있을까요? 목표는 새 학습 코드가 해당 저장소에 푸시될 때 파이프라인 애셋을 즉시 다시 빌드하는 것입니다.
이 모듈은 과정에서 다룬 내용을 요약합니다.
모든 모듈의 PDF 링크
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Last Updated: September 1, 2015
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