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AWS의 SageMaker와 Bedrock 사전 구축 솔루션을 비즈니스 요구 사항에 맞게 선택하는 모델링 접근 방식을 익힙니다.
ML 솔루션을 위해 AWS CDK와 CloudFormation으로 인프라를 코드화하고, SageMaker 모델 배포와 컨테이너 오토 스케일링을 다룹니다.
AWS ML 엔지니어링 수명 주기의 비즈니스 문제 공식화부터 Amazon SageMaker를 활용한 모델 개발까지 살펴봅니다.
Troubleshoot Amazon EBS performance, I/O timeout, volume attachment, modification, and snapshot issues using AWS tools and support resources.
AWS ML 엔지니어링에서 모델 훈련 전 데이터의 품질·편향·보안을 검증하고 데이터셋을 준비하는 방법을 다룹니다.
ML 요구 사항에 따라 AWS 스토리지와 데이터 형식을 선택하고 여러 소스의 데이터를 수집·병합하는 방법을 다룹니다.
大規模な AWS マルチアカウント環境で、Control Tower、IAM、ガードレール、集中ロギングを活用し、セキュリティガバナンスを自動化する方法を学ぶ。
Learn AWS FinOps fundamentals for optimizing, allocating, and planning cloud costs through cost-aware practices, pricing strategies, KPIs, and sustainability.
Set up the FreeRTOS Windows device simulator with AWS IoT Core, from logging and project configuration to creating an IoT thing and visualizing results.
AWS ML Engineer Associate 교육 과정의 핵심 개념을 복습하고 기계 학습 학습을 이어 갈 리소스를 안내합니다.
总结 AWS 机器学习工程师学习路径,回顾课程概念并获取后续学习资源。
以最低权限、网络隔离和 SageMaker 安全合规功能保护 AWS 机器学习资源。
Introducción práctica a la IA responsable, desde sus dimensiones y riesgos hasta la selección de modelos, preparación de datos y explicabilidad.
Aprende a optimizar modelos fundacionales mediante RAG y ajuste preciso, evaluando su rendimiento con métricas empresariales.
เรียนรู้พื้นฐานการออกแบบและปรับแต่งพรอมต์ รวมถึงเทคนิค Zero-Shot, Few-Shot และ Chain-of-Thought พร้อมความเสี่ยงที่พบบ่อย
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