2.4 KiB
ResNet AI Model Constitution
Core Principles
Code Quality and Modularity
All code must follow PEP 8 standards, include type hints, comprehensive docstrings, and be organized into modular, reusable components. Rationale: Enhances readability, maintainability, and collaboration in complex AI projects.
Rigorous Testing Standards
Implement unit tests for all utility functions, integration tests for data pipelines, and model validation tests including cross-validation and performance benchmarks. Use pytest or equivalent frameworks. Rationale: Ensures reliability and catches errors early in AI model development where failures can have significant impacts.
Reproducibility and Versioning
Version all code, data, and models using Git and DVC. Set random seeds for reproducibility in experiments. Document all dependencies and environments. Rationale: Critical for scientific validation and debugging in machine learning.
Model Evaluation and Validation
Evaluate models on independent test sets with multiple metrics (accuracy, F1-score, AUC, etc.). Perform error analysis and bias checks. Rationale: Prevents overfitting and ensures models are fair and effective.
Continuous Integration and Quality Gates
All changes must pass linting, unit tests, and integration tests in CI/CD pipelines. Model performance regressions must be flagged. Rationale: Maintains code and model quality over time.
Additional Standards
Security: Protect sensitive data, comply with privacy regulations. Ethics: Conduct bias audits, ensure responsible AI practices. Performance: Optimize for computational efficiency and scalability.
Development Workflow
Code reviews mandatory for all PRs. Model changes require peer review of evaluation results. Use issue tracking for bugs and features.
Governance
Constitution supersedes other practices. Amendments require documentation and approval from project maintainers. Compliance verified in code reviews.
Version: 1.0.0 | Ratified: TODO(RATIFICATION_DATE): Original adoption date unknown. | Last Amended: 2025-11-04