Ml Model Selection

Ml Model Selection

Overview

Use this skill to choose model candidates with explicit trade-off reasoning, not single-metric optimization.

Scope Boundaries

• Use this skill when the task matches the trigger condition described in description.
• Do not use this skill when the primary task falls outside this skill's domain.

Shared References

• Model selection trade-off rules:
  • references/model-selection-tradeoff-rules.md

Templates And Assets

• Model comparison matrix:
  • assets/model-comparison-matrix-template.csv

Inputs To Gather

• Candidate models and benchmark evidence.
• Serving constraints (latency, throughput, hardware, cost).
• Risk requirements (robustness, fairness, explainability).
• Operational ownership and rollback constraints.

Deliverables

• Candidate comparison matrix with decision rationale.
• Selected model and fallback candidate.
• Risk register and rollout recommendation.

Workflow

1. Capture candidate metrics in assets/model-comparison-matrix-template.csv.
2. Apply trade-off policy from references/model-selection-tradeoff-rules.md.
3. Validate decision against production constraints.
4. Document rejected alternatives and why.
5. Publish selection and fallback plan.

Quality Standard

• Selection criteria include accuracy + latency + cost + operability.
• Decision is evidence-backed and reproducible.
• Fallback strategy exists for failed rollout.

Failure Conditions

• Stop when selection ignores production constraints.
• Stop when alternatives are not evaluated comparably.
• Escalate when no viable candidate meets minimum requirements.