ML Model Card & Validation Checklist

ML Model Card & Validation Checklist

Use this template to document machine learning models so others can evaluate, reproduce, validate, and deploy them responsibly. The card captures purpose, data provenance, training and evaluation details, performance across subgroups, limitations, failure modes, and operational guidance. The companion validation checklist helps reviewers and auditors confirm important evidence and readiness criteria.

How to use

Complete each section with concise, factual statements. Where possible, attach or link to dataset snapshots, code repositories, evaluation notebooks, and model artifacts (weights, configuration files). Preserve exact versions, seeds, and environment specifications so others can reproduce results.

Quick Checklist (one-page)

• Model name & version — identifiable label and semantic version.
• Intended use — primary intended uses and clear out-of-scope uses.
• Primary dataset(s) — names, acquisition method, provenance, licenses.
• Key performance metrics — primary metric(s) with evaluation dataset context.
• Subgroup performance — summary of performance across important cohorts.
• Limitations & failure modes — explicit, testable descriptions.
• Reproducibility artifacts — links to code, seeds, environment, and data snapshots.
• Contact & ownership — team, owner, and support channel.

Full Template (fill these sections)

1. Model identity

Model name: [e.g., cough-classifier-v1]
Version: [e.g., 2025.02.01 / semantic v1.2.0]
Short description / summary: One or two sentences describing what the model does and why it exists.

2. Intended use & users

Describe the primary intended uses, typical users (roles), and known unsuitable or high-risk use cases. Example: "For research triage of candidate compounds; not for clinical dosing decisions."

3. Primary datasets and provenance

List training, validation, and test datasets. For each include:

• Dataset name and version / snapshot identifier
• Source and acquisition method (public repository, internal collection, scraped, sensor, simulated)
• Licensing or sharing constraints
• Preprocessing steps applied (filtering, augmentation, normalization, labeling process)
• Known biases, coverage gaps, and subgroup labels (if any)

4. Training procedure

Describe architecture, hyperparameters, training recipes, compute resources, random seeds, and frameworks. Include links to training scripts and environment (Dockerfile/Conda/requirements.txt).

5. Evaluation and metrics

Primary evaluation datasets (holdouts, external benchmarks), metrics used, and how they were computed. Provide both aggregate numbers and confidence intervals / statistical significance where possible.

6. Performance across subgroups

Report results for important subpopulations (demographic groups, device types, sites, labs, experimental conditions). Include any observed disparities and their magnitude.

7. Fairness, robustness, and stress tests

Describe checks for distribution shift, adversarial examples, label noise sensitivity, calibration, and robustness to realistic perturbations. Summarize results and remediation steps taken or recommended.

8. Uncertainty and calibration

Explain how uncertainty is represented (e.g., probabilities, intervals), calibration status (e.g., reliability diagrams), and actionable thresholds for decision-making.

9. Limitations and known failure modes

Explicitly list situations where the model fails, known edge cases, expected false positives/negatives, and any operational risks.

10. Intended deployment and operational guidance

Describe recommended operating conditions, monitoring metrics, trigger thresholds for human review, update cadence, and rollback criteria.

11. Reproducibility & artifacts

Provide links and identifiers for:

• Code repository and commit hash
• Trained artifacts (weights), model export format, and storage location
• Dataset snapshots and checksums
• Environment details (OS, Python version, library versions, container image)
• Random seeds and experiment tracking IDs

12. Versioning, change log, and lineage

Record the history of model versions, dataset changes, retraining events, and responsible teams.

13. Governance, licensing & privacy

Note any data privacy constraints, export controls, licensing terms, and compliance notes (e.g., HIPAA, GDPR considerations).

14. Contact & traceability

Model owner, team, email/SLACK/contact channel, and link to model card or registry entry. Provide a clear owner for operational issues.

Validation Checklist (for reviewers)

Use this checklist when validating model readiness for a particular use/context. Mark items as Yes / No / N/A and attach evidence links.

1. Purpose and intended use are clearly stated and aligned with stakeholder needs.
2. Datasets are documented with provenance, licensing, and snapshot identifiers.
3. Training procedure is reproducible (scripts, seeds, environment, and compute described).
4. Evaluation datasets are appropriate and independent of training data.
5. Primary metrics are appropriate for the task and decision context.
6. Subgroup analyses are present for all significant cohorts; disparities quantified.
7. Robustness tests and stress scenarios have been attempted and documented.
8. Uncertainty estimates and calibration assessments are available and acceptable for use-case risk level.
9. Limitations and failure modes are explicit and actionable mitigations are provided.
10. Reproducibility artifacts (code, data snapshots, model artifacts) are accessible to reviewers with necessary permissions.
11. Licensing, privacy, and regulatory considerations are identified and satisfied or mitigated.
12. Operational monitoring plan, alert thresholds, and rollback procedures are defined.
13. Ownership, support contact, and escalation path are specified.

Reproducibility Quick-Start Checklist

• Saved training and evaluation seeds and random-state settings.
• Container or environment specification (Dockerfile/Conda) included.
• Scripted runbook to recreate evaluation metrics from artifacts.
• Checksums or immutable storage references for datasets and model binaries.
• Notebook or CI workflow demonstrating end-to-end reproduction of key results.

Examples & templates

Include small example snippets or links showing how to load the model, run inference, and compute the primary metrics. Provide a minimal example for auditors to reproduce a reported number in under an hour.

Notes & references

Link to related documentation: model registry entry, data governance policies, experiment tracking dashboard, and external benchmark definitions.