---
title: "Secure ML workflows with Cloudsmith and Amazon SageMaker"
description: "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."
canonical_url: "https://cloudsmith.com/changelog/secure-ml-workflows-with-cloudsmith-and-amazon-sagemaker"
last_updated: "2025-12-26T20:10:50.990Z"
---
# 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](https://github.com/cloudsmith-io/cloudsmith-sagemaker-demo).

For detailed setup instructions, refer to the[ Implement with existing workflows](https://github.com/cloudsmith-io/cloudsmith-sagemaker-demo?tab=readme-ov-file#implement-with-existing-workflows) documentation.