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Navigating Amazon SageMaker Unified Studio - Amazon SageMaker Unified Studio
Navigation PanelJump into your data and modelsBuild with sample dataChange the display mode

Navigating Amazon SageMaker Unified Studio

Amazon SageMaker Unified Studio provides a comprehensive integrated development environment for machine learning (ML) and data science workflows. For SageMaker Unified Studio domains configured with IAM roles, you will be able to access the following components from the project overview page.

The left sidebar contains hierarchical navigation to access various Amazon SageMaker Unified Studio interfaces organized by:

Overview

  • Files: Browser interface for local file system storage and S3 buckets.

  • Data: Browser interface for catalog asset management

  • Connections: Centralized view for all compute and data connections

  • Notebooks: Serverless notebook interface

  • Workflows: Orchestrate jobs and tasks

Data analytics

  • Query Editor: Dedicated SQL interface.

  • Visual ETL: Visual interface for Extract, Transform, Load operations

  • Data processing jobs: View and manage job execution

AI/ML

  • Models: Jump start into available models – foundation and registered.

  • MLflow: Manage machine learning lifecycles

  • Training jobs: Managing model training processes

  • Inference endpoints: Deployment and endpoint management

Integrated development environments (IDEs)

  • JupyterLab: Managed JupyterLab integrated development environment

  • Editor for VS Code: Visual Studio Code integrated development environment

  • Code spaces: Create and manage multiple individually configured development environments. For more information, see Code spaces in Amazon SageMaker Unified Studio.

Domain Management

For IAM roles with administrator privileges to access the admin interface

Jump into your data and models

This top section provides quick access to common actions:

  • Explore your data - Explore and analyze data using SQL

  • Build in the notebook - Prepare data for analytics or to train and deploy machine learning models

  • Discover ML models – Discover, deploy and manage models

Build with sample data

This section middle section offers pre-configured example projects:

  • Customer usage analysis - SQL-based customer retention analysis

  • Customer segmentation - PySpark and AWS Glue analysis

  • Customer churn prediction - Random Forest implementation with feature engineering

  • Retail sales forecasting - End-to-end retail sales analysis using Amazon SageMaker Unified Studio AI

Change the display mode

You can switch between light mode and dark mode to suit your viewing preference.

To change the display mode

  1. In the upper-right corner of the console, choose the Account icon.

  2. Choose Customize appearance.

  3. Select Light mode or Dark mode.

Your preference is saved automatically and applied across sessions.