Amazon Web Services

This video provides a comprehensive guide on getting started with Amazon SageMaker Studio, an integrated development environment for machine learning. It walks through the process of setting up a SageMaker domain, creating user profiles with appropriate permissions, and establishing shared spaces for collaborative work. The video explains how to launch SageMaker Studio and access its various features, including data preparation tools, pre-trained models, and MLOps capabilities. It emphasizes the importance of proper domain setup and user management to enable effective team collaboration and resource sharing within SageMaker Studio.

The tutorial demonstrates how to create and manage user profiles within a SageMaker domain, highlighting the ability to customize execution roles and access permissions for each user. It also showcases the shared spaces feature, which allows multiple users to work together in real-time on notebooks and share model artifacts seamlessly. The video concludes by showing how to launch SageMaker Studio and navigate its interface, providing a foundation for users to begin their machine learning projects using AWS's powerful ML development environment.

product-information
skills-and-how-to
data
ai-ml
sagemaker
Show 2 more

Up Next

VideoThumbnail
30:23

T3-2 Amazon SageMaker Canvasで始めるノーコード機械学習 (Level 200)

Jun 27, 2025
VideoThumbnail
31:49

T2-3 AWS を使った生成 AI アプリケーション開発 (Level 300)

Jun 27, 2025
VideoThumbnail
26:05

T4-4: AWS 認定 受験準備の進め方 AWS Certified Solutions Architect – Associate 編 後半

Jun 26, 2025
VideoThumbnail
32:15

T3-1: はじめてのコンテナワークロード - AWS でのコンテナ活用の第一歩

Jun 26, 2025
VideoThumbnail
29:37

BOS-09: はじめてのサーバーレス - AWS Lambda でサーバーレスアプリケーション開発 (Level 200)

Jun 26, 2025