Introduction to model lifecycle management
Manage a model as a versioned governed asset from discovery through retirement.
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.
A model is a governed asset
Model choice affects behavior, licensing, data handling, runtime, capacity, security, portability, and evidence. Selecting a model from a leaderboard or familiar brand leaves these consequences unresolved.
SAI-210 manages the lifecycle from requirements and candidate discovery through acquisition, evaluation, selection, packaging, approval, deployment, monitoring, update, revocation, and retirement.
Decision principles
- Begin with an approved purpose and measurable acceptance criteria.
- Compare candidates under representative conditions.
- Preserve model, tokenizer, configuration, license, and provenance.
- Scope approval to workload, data, users, boundary, runtime, and limitations.
- Treat quantization, prompt, runtime, or serving changes as possible behavior changes.
- Maintain rollback and an explicit retirement path.
Module outcome
You will produce a requirements and acceptance record, candidate comparison, license and provenance review, approved model package, release decision, and monitoring/update/retirement plan.
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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