ModelPack: Packaging AI Models the Cloud-Native Way
Shriira Press
How a cloud-native open standard lets you package, ship, and run AI models the same way you ship containers.
Welcome to ModelPack: Packaging AI Models the Cloud-Native Way.
For more than a decade, container images have given software a single, portable shape: build once, push to a registry, pull anywhere, and run. Machine-learning models never had that. They travel as loose folders of weight files, ad-hoc config, and proprietary archives scattered across vendor hubs, object stores, and homegrown scripts. ModelPack, a CNCF sandbox project, fixes that by defining a vendor-neutral open standard for packaging models as OCI artifacts — the same registries, the same content-addressable storage, the same distribution plumbing that already moves containers everywhere. This book walks you through the whole idea. It opens with the distribution problem and why OCI is the right foundation, then explains how ModelPack reuses the OCI image manifest, the precise media types it assigns to weights, code, datasets, and docs, and the rich model config that carries architecture, parameter size, precision, and capability metadata. From there it turns practical, building and pushing a real artifact with the modctl CLI and its Modelfile, and closes by placing ModelPack in the wider ecosystem of registries, Kubernetes, and AI tooling.
This title is part of the ShriIra library and is free to read in full, right here — our small contribution to making world-class knowledge easy to reach.
A note on reading it: open the Contents menu at the top of the reader to jump between chapters, use the Aa menu to set a comfortable text size, theme (light, sepia, or night), and single- or two-page layout. Your place is saved automatically, so you can always pick up where you left off.
We hope it serves you well.
— Shriira Press