Model packaging, approval, and registry
Package models with provenance, evaluation, configuration, policy, and approval metadata.
Before you start
Module prerequisites: SAI-100. Check the exercise-specific tools and environment before running commands.
Module outcomes: Create model acceptance criteria; Record licensing and provenance evidence; Define model update and retirement controls. This lesson contributes to that sequence.
The package is the approval unit
Model weights alone do not define served behavior. Create a package that binds model and tokenizer identities to configuration, prompt or interface contract, runtime compatibility, evaluation, license, provenance, policy, limitations, and approval.
Registry record
An approved model registry should expose:
- Immutable model, tokenizer, and package identities.
- Intended and prohibited uses, owners, and approved environments.
- License review and attribution obligations.
- Evaluation datasets, conditions, results, and unresolved failures.
- Runtime and hardware compatibility claims with tested versions.
- Serving defaults, security constraints, and required controls.
- Approval, exceptions, expiry, supersession, revocation, and rollback target.
Promotion should copy the evaluated package without substituting files or rebuilding transformations. Re-verify identities after transfer.
Access and lifecycle state
Separate who may submit, evaluate, approve, promote, deploy, revoke, and administer the registry. Consumers should select only approved versions within their scope. A revoked model must become unavailable to new deployments and visible in existing-deployment response plans.
The registry is a decision system and authoritative metadata source, not merely a file browser.
Module worked example
A newer model advertises higher coding scores but changes its license and chat template.
- Apply the control
- Review the exact revision and terms, then rerun representative acceptance cases with the intended runtime.
- Expected result
- The decision records quality, operating constraints, rejected alternatives and a fallback.
- Troubleshooting
- Check tokenizer, template, quantization and parser versions before attributing a regression to model quality.
Practice and completion check
Choose one control from this lesson. Identify its actor, target, enforcement point and expected result. Describe an allowed case and a denied or failed case. For a runnable exercise, retain observed output and separate it from expected output. Record unresolved limitations before marking the lesson complete.
Your lesson progress
Completion is self-reported learning progress; it does not certify practical competence.
Status: Not marked
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