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Automate ML deployments with MLOps, CI/CD pipelines, AWS services, deployment strategies, and model retraining mechanisms for production environments.
Learn to provision, deploy, and scale machine learning infrastructure in AWS using infrastructure as code.
Use Amazon SageMaker Studio to prepare, build, train, deploy, and monitor tabular machine learning models across the ML lifecycle.
Automatisez la gouvernance de la sécurité AWS à grande échelle grâce aux environnements multicompte, aux contrôles préventifs et détectifs et à la journalisation centralisée.
Laboratório prático para migrar um aplicativo web local para a AWS usando o Elastic Beanstalk, do preparo do código ao gerenciamento do ambiente.
Laboratorio pratico per migrare un'applicazione web on-premises su AWS Elastic Beanstalk, creando e gestendo l'ambiente cloud.
機械学習ライフサイクルの各段階でAWSサービスを活用し、モデルの評価・デプロイとMLOpsの基礎を学ぶコース。
サンプルドキュメントを使ったガイド付きチュートリアルで、Amazon Q Business のウェブエクスペリエンスを作成する入門コース。
Amazon Q DeveloperをIDEで活用し、コードの最適化・変換や新機能の実装を体験する入門コース。
Hands-on lab for orchestrating automated machine learning workflows with SageMaker Pipelines and registering trained models in the SageMaker Model Registry.
根据业务工作负载和应用需求,在 AWS 专用数据库中选择并设计合适的关系型或 NoSQL 解决方案。
Build emotional intelligence for resilience, psychological safety, trust, influence, and stronger teamwork in challenging leadership environments.
Développez des applications serverless pilotées par les événements sur AWS grâce à des ateliers pratiques, Lambda et des frameworks de déploiement.
기업 정보로 질문에 답하고 콘텐츠를 생성·요약하며 태스크를 완료하는 Amazon Q Business를 설정하고 사용하는 방법을 안내합니다.
AWS 서비스를 활용해 기계 학습 수명 주기의 각 단계를 이해하고 모델을 평가·배포하는 방법과 MLOps 기초를 익힙니다.
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