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Course Outline
소개
MLOps 개요
- MLOps란 무엇인가요?
- MLOps Azure Machine Learning 아키텍처
MLOps 환경 준비
- 설정 Azure Machine Learning
모델 재현성
- Azure Machine Learning 파이프라인 작업
- 파이프라인으로 Machine Learning 프로세스 브리징
컨테이너 및 배포
- 모델을 컨테이너에 패키징
- 컨테이너 배포
- 모델 검증
운영 자동화
- Azure Machine Learning 및 GitHub을 사용한 작업 자동화
- 모델 재훈련 및 테스트
- 새로운 모델 출시
Go버넌스와 제어
- 감사 추적 만들기
- 모델 관리 및 모니터링
요약 및 결론
Requirements
- Azure Machine Learning의 경험
청중
- 데이터 과학자
14 Hours
회원 평가 (5)
I've got to try out resources that I've never used before.
Daniel - INIT GmbH
Course - Architecting Microsoft Azure Solutions
매우 친절하고 도움이 됨
Aktar Hossain - Unit4
Course - Building Microservices with Microsoft Azure Service Fabric (ASF)
Machine Translated
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
Course - MLflow
The practical part, I was able to perform exercises and to test the Microsoft Azure features
Alex Bela - Continental Automotive Romania SRL
Course - Programming for IoT with Azure
I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.
