KitOps

Key features

• Packages models, datasets, code, and configurations into a single artifact for reproducibility.
• Supports cryptographic signing and tamper-evident packaging for security and compliance.
• Works with existing container registries such as Docker Hub, ECR, GCR, and Artifactory.
• CLI interface mirrors Docker commands for ease of adoption.
• Python SDK available for integration with Python-based workflows.
• Selective unpacking allows users to retrieve only needed components from a ModelKit.
• Automated compliance features generate AI Bill of Materials and map to regulatory requirements.

Use cases

• Versioning and packaging AI/ML models and related assets for reproducible deployment.
• Sharing AI/ML project artifacts securely across teams using existing registries.
• Selective retrieval of project components for local testing or integration.
• Automating packaging and deployment in CI/CD pipelines.
• Tracking and auditing changes to AI/ML assets for compliance.

Pros

• Open source and free to use under Apache 2.0 license.
• No proprietary registry required; works with existing infrastructure.
• Supports Windows, Mac, and Linux platforms.

Screenshots