JFrog & Amazon SageMaker
By leveraging JFrog Artifactory and Amazon SageMaker together, ML models can be delivered alongside all other software development components in a modern DevSecOps workflow, making each model immutable, traceable, secure, and validated as it matures for release.
Amazon SageMaker Studio Classic in a private VPC
JFrog’s Amazon SageMaker integration allows organizations to:
- Maintain a single source of truth for data scientists and developers, ensuring all models are readily accessible, traceable, and tamper-proof.
- Bring ML closer to the software development and production lifecycle workflows, protecting models from deletion or modification.
- Develop, train, secure and deploy ML models.
- Detect and block the use of malicious ML models across the organization.
- Scan ML model licenses to ensure compliance with company policies and regulatory requirements.
- Store home-grown or internally augmented ML models with robust access controls and versioning history for greater transparency.
- Bundle and distribute ML models as part of any software release.
- Bring ML closer to the software development and production lifecycle workflows, protecting models from deletion or modification.
- Develop, train, secure and deploy ML models.
- Detect and block the use of malicious ML models across the organization.
- Scan ML model licenses to ensure compliance with company policies and regulatory requirements.
- Store home-grown or internally augmented ML models with robust access controls and versioning history for greater transparency.
- Bundle and distribute ML models as part of any software release.
