KitOps

Moving a model from a Jupyter notebook to an ML tool or development server, then to a production server like Kubernetes is difficult because each tool uses its own packaging mechanism, and requires engineers to repackage the model multiple times. This slows down development and introduces risk. Kit is an open source MLOps project built to standardize packaging, reproduction, deployment, and tracking of AI / ML models, so it can be run anywhere, just like application code. Kit solves two big problems: Collaboration– By building ModelKits on industry standards, anyone (not just data scientists) can participate in the model development lifecycle whether they’re integrating models with their application, experimenting with them locally, or deploying them to production. ModelKits and Kitfiles work with the tools your team is already using, so you can use the same deployment pipelines and endpoints you’ve hardened with your application development process. Model traceability and reproducibility– Unlike Dockerfiles, Kitfiles are a modular package - pull just a part of the ModelKit, like the model or dataset, or pull the whole package with one simple command. Storing ModelKits in your organization’s registry provides a history of meaningful state changes for auditing. ModelKits are immutable so are perfect for a secure bill-of-materials (SBOM) initiative.