Amazon SageMaker JumpStart adds fine-tuning support for models in a private model hub

Amazon SageMaker JumpStart has enhanced its private model hub feature, providing organizations with more control and flexibility in managing machine learning models.

• Fine-tuning support for models in the private hub
• Ability to add and manage custom-trained models
• Deep linking capabilities for associated notebooks
• Improved model version management
• Enhanced security and governance for enterprise ML assets

Key capabilities include:

• Creating a private hub with curated models
• Configuring access controls
• Fine-tuning models through SageMaker Python SDK and Studio UI
• Updating models using new API calls

This update enables enterprises to create a centralized repository of trusted ML models while allowing team-specific customization and optimization.