ModelKit Overview
ModelKit is a standardized, OCI-compliant packaging format for AI/ML projects.
It bundles everything your model needs — datasets, training code, config files, documentation, and the model itself — into a single shareable artifact.
Use ModelKits to version, share, and deploy AI models across teams and environments using familiar DevOps tools like DockerHub, GitHub Packages, or private registries.
➡️ Get started with ModelKits in under 15 minutes ➡️ See how security-focused teams use ModelKits
🔑 Key Features
OCI-compliant and tool-friendly Store, tag, and version ModelKits in any container registry — no custom infrastructure needed.
Selective unpacking Unpack only the parts you need (e.g. just the dataset or model weights) to speed up pipelines and reduce compute overhead.
No duplication for shared assets Reuse datasets or configs across multiple kits without bloating storage.
Familiar versioning and tagging Use registry-native tags (e.g.
:latest,:prod,:rollback) to track model state and history.Built for ML workflows Supports AI-specific needs like serialized model handling, reproducible training snapshots, and data lineage.
Streamlined collaboration Teams can pull, inspect, and repack models just like container images — making it easier to collaborate across roles and environments.
⚡ Why It Matters
ModelKit simplifies the messy handoff between data scientists, engineers, and operations. It gives teams a common, versioned package that works across clouds, registries, and deployment setups — without reinventing storage or delivery.
It’s more than a format — it’s a building block for secure, reproducible AI.
Have feedback or questions? Open an issue on GitHub or join us on Discord.