Open source Kubernetes AI stacks: what are teams missing?

TL;DR: Open source Kubernetes AI stacks typically require separate projects for notebooks, scheduling, distributed compute, serving, packaging and policy, according to Jozu, because no single project covers the full enterprise workflow. The governance gap is not model code alone, but the missing control plane for versioned artifacts, identity and admission decisions.

Editorial analysis by NHI Mgmt Group, based on content published by Jozu: “Open Source Kubernetes Solutions for Enterprise AI: A Practical Map”.

Key questions

Q: What breaks when AI model evaluations are not versioned?

A: Without versioned datasets and prompts, score changes become hard to trust because the difference may come from the test setup rather than the model.

Q: Why does AI governance depend on signed provenance as much as cluster policy?

A: Cluster policy can only enforce rules on what it can recognise, so it cannot replace trust in the underlying artefact.

Q: How should security teams govern AI in the security stack?

A: Security teams should treat AI as a governed decision aid, not an autonomous authority.

Practitioner guidance

• Map the AI stack as one governed lifecycle Document where notebooks, training jobs, schedulers, serving endpoints, registries and policy decisions live, then assign an owner for each transition between them.
• Package models as versioned artefacts before promotion Require the model, datasets, code, prompts and configuration to move together as a single approved unit so lineage and rollback stay intact.
• Bind deployment approval to signed provenance Use signature verification on OCI-stored model artefacts and refuse promotion when provenance, attestation or ownership cannot be verified.

Bottom line: The article’s central point is that enterprise AI on Kubernetes is usually a stitched-together stack, so governance has to span development, scheduling, serving, packaging and policy.

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