Supercharge your AI workflows by connecting to SageMaker Studio from Visual Studio Code

This course was developed by members of AWS Technical Field Communities (TFC), an AWS community of technical experts. The content is intended to complement our standard training curriculum and augment your AWS learning journey.

• Configuring necessary IAM permissions, roles, and policies to enable secure remote connections from local VS Code to SageMaker Studio spaces.
• Setting up and establishing the remote connection using SageMaker's local IDE support features (e.g., via extensions or configurations for VS Code to access SageMaker-managed compute).
• Performing AI/ML development tasks (e.g., editing notebooks/code, running training jobs, debugging) directly from local VS Code while executing on SageMaker's scalable infrastructure.
• Applying best practices for hybrid local-cloud workflows, including handling authentication, resource management, and integration with SageMaker tools (e.g., experiments, pipelines, or generative AI capabilities).

• Understand how remote access from VS Code to SageMaker Studio bridges local customization (e.g., extensions, AI tools like GitHub Copilot) with SageMaker's fully managed execution environment for faster, more efficient AI development.
• Gain practical ability to set up and use the connection, reducing context-switching and enabling developers to work in their preferred IDE while benefiting from cloud-scale resources.
• Be prepared to implement this workflow in real-world AI/ML projects, improving productivity, collaboration, and iteration speed for data scientists and developers using SageMaker.

• 1 hour 30 minute digital course content with step-by-step guidance, configuration demonstrations, and practical examples of connecting VS Code to SageMaker Studio.
• Intermediate-level training in the Artificial Intelligence domain, aligned with recent SageMaker AI / Unified Studio innovations for local IDE remote access.
• Coverage of key concepts, setup processes, security considerations, and use cases for enhancing AI workflows with preferred tools and managed compute.
• Certificate of completion issued.