This article explains how to use Amazon SageMaker's @remote decorator to run training jobs with private package repositories in restricted network environments.

• @remote decorator converts Python functions into SageMaker training jobs automatically
• Organizations with strict data privacy controls can use private repositories instead of public PyPI
• Solution uses AWS CodeArtifact as private PyPI repository with VPC isolation
• CloudFormation templates set up VPC with no internet access and CodeArtifact connectivity
• Configuration file specifies dependencies, instance type, VPC settings, and CodeArtifact login commands
• Training code decorated with @remote runs on SageMaker while downloading packages from private repo
• Enables production-ready ML code development in regulated industries with network restrictions

This approach allows data scientists in regulated environments to use SageMaker training capabilities while maintaining strict network isolation and compliance requirements.