Building Faster from a Single Data and AI Development Environment with Amazon SageMaker Unified Studio

Overview

Discover how Amazon SageMaker Unified Studio enables teams to securely discover, prepare, and collaborate on data assets while building analytics and gen AI applications through a single experience.

Syllabus

• Introduction to Amazon SageMaker Unified Studio
Overview of Unified Studio
Key features and capabilities
Importance of a unified environment
• Setting Up Your SageMaker Environment
Account setup and permissions
Navigating the SageMaker Studio interface
Configuring the development environment
• Data Discovery and Preparation
Tools for data ingestion and exploration
Data wrangling and transformation techniques
Utilizing Data Wrangler for feature engineering
• Building Analytics Applications
Introduction to SageMaker notebooks
Creating and managing Jupyter notebooks
Integrating data visualization and analytics
• Development of Generative AI Applications
Overview of generative AI models
Utilizing pre-trained models and custom model training
Deploying AI models for inference
• Collaboration in SageMaker Unified Studio
Sharing and managing workspaces
Collaborative features for team projects
Implementing version control with Git integration
• Security and Compliance in Data and AI Development
Data security features in SageMaker
Managing permissions and access control
Compliance best practices
• Optimizing Workflows and Performance
Automating workflows with SageMaker Pipelines
Monitoring and debugging applications
Cost management and optimization strategies
• Case Studies and Practical Applications
Real-world applications of SageMaker
Industry-specific use cases
Success stories and lessons learned
• Conclusion and Resources
Recap of key concepts
Further learning resources
Q&A session and course feedback

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Subjects

Programming