# Navigating Amazon SageMaker Unified Studio
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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.

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

### Overview
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+ 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
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+ Query Editor: Dedicated SQL interface.
+ Visual ETL: Visual interface for Extract, Transform, Load operations
+ Data processing jobs: View and manage job execution

### AI/ML
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+ 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)
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+ 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](ide-spaces.md).

### Domain Management
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For IAM roles with administrator privileges to access the admin interface

## Jump into your data and models
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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
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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
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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.

1. Choose Customize appearance.

1. Select Light mode or Dark mode.

Your preference is saved automatically and applied across sessions.