The KitOps tool addresses issues such as model traceability and reproducibility, and facilitates collaboration by allowing anyone to participate in the model development lifecycle. It also offers features such as standards-based packaging, tamper-proofing, tagging and versioning, and automation for CI/CD. The ModelKit is an OCI compliant package that contains everything needed to integrate with a model or deploy it to production. The Kit CLI is a command line interface that performs actions on ModelKits. KitOps is designed to work with existing tools and is compatible with nearly every development and deployment tool and registry in use today.
Key takeaways:
- KitOps is an open-source DevOps tool that packages and versions AI/ML models, datasets, code, and configuration into a reproducible artifact called a ModelKit.
- ModelKits are built on existing standards, ensuring compatibility with the tools your data scientists and developers already use.
- KitOps aims to standardize packaging, reproduction, deployment, and tracking of AI/ML models, solving problems like model traceability and reproducibility, and facilitating collaboration.
- ModelKits can be used with any AI, ML, or LLM project and can be stored in any OCI-compliant registry, making them compatible with nearly every development and deployment tool and registry in use today.