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KitOps is a CNCF open standards project for packaging, versioning, and securely sharing AI/ML projects. Built on the OCI (Open Container Initiative) standard, it integrates seamlessly with your existing AI/ML, software development, and DevOps tools.
Itβs the preferred solution for packaging, versioning, and managing assets in security-conscious enterprises, governments, and cloud operators who need to self-host AI models and agents.
KitOps is also the reference implementation of the CNCF's ModelPack specification for a vendor-neutral AI/ML interchange format.
KitOps Architecture
ModelKit
KitOps packages your project into a ModelKit β a self-contained, immutable bundle that includes everything required to reproduce, test, or deploy your AI/ML model.
ModelKits can include code, model weights, datasets, prompts, experiment run results and hyperparameters, metadata, environment configurations, and more.
ModelKits are:
Tamper-proof β Ensuring consistency and traceability
Signable β Enabling trust and verification
Compatible β Natively stored and retrieved in all major container registries
ModelKits elevate AI artifacts to first-class, governed assets β just like application code.
Kitfile
A Kitfile defines your ModelKit. Written in YAML, it maps where each artifact lives and how it fits into the project.
Kit CLI
The Kit CLI not only enables you to create, manage, run, and deploy ModelKits -- it lets you pull only the pieces you need.
π₯ Watch KitOps in Action
This video shows how KitOps streamlines collaboration between data scientists, developers, and SREs using ModelKits.
For those who prefer to build from the source, follow these steps to get the latest version from our repository.
Key Benefits
KitOps was built to bring discipline to productizing AI/ML projects, with:
π¦ Unified packaging and versioning of AI/ML assets
π Secure, signed distribution
π οΈ Toolchain compatibility via OCI
βοΈ Production-ready for enterprise ML workflows
π’ Create runnable containers for Kubernetes or docker
π Audit-ready lineage tracking
To get the most out of KitOps' ModelKits, use them with the Jozu Hub. Jozu Hub can be installed behind your firewall and use your existing OCI registry in a private cloud, datacenter, or even in an air-gapped environment.
Simplify Team Collaboration
ModelKits streamline handoffs between:
Data scientists preparing and training models
Application developers integrating models into services
SREs deploying and maintaining models in production
This ensures reliable, repeatable workflows for both development and operations.
Use KitOps to Speed Up and De-risk AI/ML Projects
KitOps supports packaging for a wide variety of models:
Large language models
Computer vision models
Multi-modal models
Predictive models
Audio models
etc...
πͺπΊ EU AI Act Compliance π
For our friends in the EU - ModelKits are the perfect way to create a library of model versions for EU AI Act compliance because they're tamper-proof, signable, and auditable.
Join KitOps Community
For support, release updates, and general KitOps discussion, please join the KitOps Discord. Follow KitOps on X for daily updates.
If you need help there are several ways to reach our community and Maintainers outlined in our support doc
Joining the KitOps Contributors
We β€οΈ our KitOps community and contributors. To learn more about the many ways you can contribute (you don't need to be a coder) and how to get started see our Contributor's Guide. Please read our Governance and our Code of Conduct before contributing.
Reporting Issues and Suggesting Features
Your insights help KitOps evolve as an open standard for AI/ML. We deeply value the issues and feature requests we get from users in our community π. To contribute your thoughts, navigate to the Issues tab and click the New Issue button.