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.
Machine Learning Operations (MLOps): Getting Started - 한국어
Machine Learning Operations (MLOps): Getting Started - 한국어
Instructor: Google Cloud Training
Sponsored by Louisiana Workforce Commission
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
3 hours to complete
3 weeks at 1 hour a week
Flexible schedule
Learn at your own pace
What you'll learn
효과적인 MLOps를 지원하는 데 필요한 핵심 기술을 파악하고 사용합니다
ML 시스템의 맥락에서 최고의 CI/CD 방식을 채택합니다
안정적이고 효과적인 MLOps 환경을 위한 Google Cloud 아키텍처를 구성하고 프로비저닝합니다
반복 가능한 안정적인 학습 및 추론 워크플로를 구현합니다
Details to know
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Assessments
2 assignments
Taught in Korean
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There are 4 modules in this course
이 모듈에서는 과정을 간략히 설명합니다
What's included
1 video
ML 실무자의 고충 ML의 DevOps 개념 세 가지 ML 수명 주기 단계 ML 프로세스 자동화
What's included
5 videos1 reading1 assignment
Vertex AI란 무엇이며 통합 플랫폼이 중요한 이유는 무엇인가요? Vertex AI에서 MLOps 소개 Vertex AI는 MLOps 워크플로를 어떻게 지원하나요? 1부 Vertex AI는 MLOps 워크플로를 어떻게 지원하나요? 2부
What's included
5 videos1 reading1 assignment1 app item
요약
What's included
1 video1 reading
Instructor
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