이 과정에서는 Google Cloud에서 최신 ML 파이프라인 개발을 담당하는 ML 엔지니어와 트레이너로부터 유익한 지식을 배웁니다. 초반에 진행되는 몇 개 모듈에서는 Google의 TensorFlow 기반 프로덕션 머신러닝 플랫폼으로서 ML 파이프라인과 메타데이터를 관리할 수 있는 TensorFlow Extended(TFX)에 대해 다룹니다. 파이프라인 구성요소와 TFX를 사용한 파이프라인 조정을 알아봅니다. 지속적 통합과 지속적 배포를 통해 파이프라인을 자동화하는 방법과 ML 메타데이터를 관리하는 방법도 배웁니다. 그런 다음 주제를 전환하여 TensorFlow, PyTorch, scikit-learn, xgboost 등 여러 ML 프레임워크에서 ML 파이프라인을 자동화하고 재사용하는 방법을 설명합니다. 또한 Google Cloud의 또 다른 도구인 Cloud Composer를 사용하여 지속적 학습 파이프라인을 조정하는 방법도 알아봅니다. 마지막으로 MLflow를 사용하여 머신러닝의 전체 수명 주기를 관리하는 방법을 살펴봅니다.
ML Pipelines on Google Cloud - 한국어
ML Pipelines on Google Cloud - 한국어Google Cloud
About this Course
What you will learn
TFX 표준 파이프라인 구성요소를 개괄적으로 이해합니다.
TFX 대화형 컨텍스트를 사용하여 TFX 파이프라인의 프로토타입을 개발하는 방법을 알아봅니다.
KubeFlow 및 AI Platform Pipelines로 TensorFlow, PyTorch, XGBoost, Scikit Learn 모델의 지속적 학습
Composer 및 MLFlow로 지속적 학습 수행
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
이 모듈에서 제공하는 온라인 과정:
TFX 파이프라인 소개
이 모듈에서는 TensorFlow Extended(TFX)를 소개하고 TFX 개념과 구성요소를 설명합니다.
TFX를 사용한 파이프라인 조정
이 모듈에서 다루는 내용:
커스텀 구성요소 및 TFX 파이프라인용 CI/CD
이 모듈에서 다루는 내용:
이 모듈에서는 TFX 메타데이터를 사용한 아티팩트 관리에 대해 설명합니다.
다양한 SDK, KubeFlow, AI Platform Pipelines로 지속적 학습
이 모듈에서는 다양한 SDK, KubeFlow, AI Platform Pipelines를 사용한 지속적 학습에 대해 설명합니다.
Cloud Composer를 사용한 지속적 학습 파이프라인
이 모듈에서는 Cloud Composer를 이용한 지속적 학습에 대해 설명합니다.
Mlflow를 사용한 ML 파이프라인
이 모듈에서는 MLflow와 해당 구성요소를 소개합니다.
이 모듈에서는 과정 내용을 요약합니다.
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