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About
KitOps is an open-source DevOps tool designed to simplify the AI pipeline by packaging and versioning AI/ML models, datasets, code, and configurations into reproducible artifacts called ModelKits. This tool is particularly helpful for data scientists, developers, and DevOps teams who struggle with the complexities of model handoffs and deployment. By standardizing packaging and deployment, KitOps aims to streamline the AI development lifecycle.
Details
The key features of KitOps include:
- Standards-based packaging: ModelKits are built on existing standards, ensuring compatibility with tools already used by data scientists and developers.
- Immutable and tamper-proof: Each ModelKit package includes a SHA digest for itself and every artifact it holds, providing a secure bill-of-materials (SBOM) initiative.
- Collaboration: ModelKits can be stored in existing container registries and work with familiar tools, enabling seamless collaboration across teams.
- Versioning and tagging: ModelKits are tagged and versioned, making it easy to track changes and reproduce results.
- Automation for CI/CD: ModelKits can be packed or unpacked locally or as part of a CI/CD workflow for testing, integration, or deployment.
- Local development mode: Kit's Dev Mode allows for
