Bringing Container-Native Simplicity to AI/ML
summary
Claims that 80% never reach production and offers a concrete toolset: an OCI‑compliant model kit plus a kit CLI (kit version, kit login, kit pack, kit unpack, kit push). It packages model code data and metadata into a single image that can be signed, pushed to registries (Docker/registry providers) and pulled into CI/CD (GitHub Actions Jenkins Dagger) workflows. Demo shows pack unpack push succeeding and selective unpack of datasets for team workflows.
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chapters (8)
- 0:00Introduction to the Talk
- 0:30Challenges in AI Model Deployment
- 1:50Packaging and Versioning in MLOps
- 6:34Introduction to KitOps
- 7:52KitOps Model Kit and CLI
- 10:37KitOps Pipeline and Demo
- 17:48Automating ML Lifecycle with CI/CD
- 22:15Conclusion and Resources
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