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예측형 AI와 생성형 AI의 모델 평가가 무엇이 다른지 이해하고 MLOps 수명 주기에서 모델 평가의 중요한 역할을 인식합니다.
다양한 생성형 AI 작업에 적합한 평가 측정항목을 파악하고 적용합니다.
계산 기반 방법과 모델 기반 방법을 비롯해 Vertex AI의 다양한 평가 서비스를 사용하여 효과적으로 생성형 AI를 평가합니다.
프로덕션 환경에서 강력하고 안정적으로 모델을 배포할 수 있도록 LLM 평가 권장사항을 실현합니다.
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이 과정은 머신러닝 실무자에게 생성형 AI 모델과 예측형 AI 모델을 평가하는 데 필요한 도구, 기술, 권장사항을 제공합니다. 모델 평가는 프로덕션 단계의 ML 시스템이 안정적이고 정확하고 성능이 우수한 결과를 제공할 수 있게 하는 중요한 분야입니다.
강의 참가자는 다양한 평가 측정항목, 방법, 각각 다른 모델 유형과 작업에 적합한 애플리케이션에 대해 깊이 있게 이해할 수 있습니다. 이 과정에서는 생성형 AI 모델의 고유한 문제를 강조하고 이를 효과적으로 해결하기 위한 전략을 소개합니다. 강의 참가자는 Google Cloud의 Vertex AI Platform을 활용해 모델 선택, 최적화, 지속적인 모니터링을 위한 견고한 평가 프로세스를 구현하는 방법을 알아볼 수 있습니다.
이 모듈에서는 과정의 목표를 설명하고 과정 구조의 개요를 제공합니다.
이 모듈에서는 모델 평가의 문제와 Vertex AI에서 제공하는 솔루션을 소개합니다.
3 videos1 reading1 assignment
이 모듈에서는 생성형 AI 작업 평가의 문제와 이러한 문제를 해결하기 위한 권장사항을 설명합니다. 또한 Vertex AI에서 사용할 수 있는 다양한 유형의 모델 평가 서비스를 살펴보고 Vertex AI의 자동 측정항목, 자동 정렬(AutoSxS), 안전 편향 평가 서비스를 소개합니다.
7 videos1 reading1 assignment
이 모듈에서는 가장 중요한 개념, 도구, 기술, 제품을 살펴보면서 전체 과정을 요약합니다.
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