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Bring trained artifacts into the release path

InferCrane does not schedule training jobs. MLflow, Kubeflow, SkyPilot, a managed fine-tuning system, or your existing pipeline owns data, execution, and checkpoint storage. The pipeline hands InferCrane a signed immutable artifact identity for release qualification.

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Create a signing key

Keep this key in the training system’s secret manager:
The private key file is created with mode 0600 and is never uploaded to InferCrane.

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Sign immutable provenance

Use the checkpoint digest produced by your registry or artifact store. The handoff contains no dataset rows, prompts, outputs, logs, credentials, or checkpoint bytes.

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Verify and attach

Attachment fails closed when:
  • the signature or payload was modified;
  • the path deployment does not match the signed deployment;
  • the revision belongs to another tenant or does not exist;
  • the revision already references a different immutable artifact;
  • the repository contains credentials, query parameters, or a mutable/unsafe location.

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Promotion remains separate

Artifact provenance is not evidence that a candidate should receive traffic. Continue with:
Release Guard decides from compatible persisted policy and evidence. An external training system never promotes a revision by merely attaching a checkpoint.

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Console workflow

Workload detail displays signed training lineage and accepts an already signed handoff JSON file. Signing remains a CLI/CI operation so private training keys never enter browser code.