Secure ML workflows with Cloudsmith and Amazon SageMaker

Cloudsmith now provides a reference implementation guide for Amazon SageMaker. This resource demonstrates how to utilize Cloudsmith as a secure, central hub for the Hugging Face models, Docker images, and Python packages required for machine learning workflows.

AI/ML supply chains are often fragmented; relying on unmanaged public sources for models and dependencies introduces security risks and build instability. This guide provides a blueprint for moving ML artifacts into a private, governed environment without disrupting the SageMaker development experience.

How it works

This release provides a reusable set of instructions and scripts designed to be embedded directly into your existing SageMaker workflows.

The integration enables:

  • Secured model access: Use Cloudsmith to proxy and cache Hugging Face models, ensuring consistent availability for SageMaker training jobs.
  • Unified dependencies: Consolidate Python packages and Docker containers within Cloudsmith to create a single source of truth for SageMaker environments.
  • Workflow integration: Implementation examples cover how to configure SageMaker to authenticate and fetch artifacts securely during execution.

Getting started

The implementation guide and code samples are available in our public repository: Cloudsmith SageMaker Demo on GitHub.

For detailed setup instructions, refer to the Implement with existing workflows documentation.

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