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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.
Hands-on lab for diagnosing Amazon DocumentDB bottlenecks with CloudWatch and Performance Insights, then improving query performance through indexing and read replicas.
Hands-on lab designing DynamoDB tables and indexes around application access patterns, including overloaded and sparse global secondary indexes.
学习根据业务负载和应用需求,规划并设计涵盖关系型、NoSQL及多种专用服务的 AWS 云数据库解决方案。
Amazon Q in QuickSight のアーキテクチャと、自然言語クエリやデータストーリーを使った BI 活用の基礎を学ぶ。
透明性・説明可能性と人間中心設計に焦点を当て、責任あるAIシステムの開発原則を学ぶ。
An executive-focused course on turning generative AI into organizational value through use cases, responsible practices, and an integration plan.
AWS のツール群を使い、CI/CD、Infrastructure as Code、コンテナ、サーバーレスを統合した DevOps の自動化を実践します。
機械学習モデルの評価指標や収束問題を分析し、SageMaker のツールでモデルと実験をデバッグする方法を学びます。
Monitor, troubleshoot, rightsize, and control costs for AWS machine learning infrastructure using observability, performance, and cost-management tools.
Monitor production machine learning systems with Amazon SageMaker, detecting data drift, model quality issues, bias, and attribution drift to support remediation and retraining.
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