AI & Data
Best Practice: Document all stages of AI development for reproducibility
Sep 12, 2024
Clear documentation is crucial for ensuring that AI models can be reproduced and maintained over time. Documenting all stages of development, from data collection to model architecture and hyperparameters, provides future developers with the necessary context to understand and improve the model. This practice also enhances collaboration across teams and helps with troubleshooting.
Why Documentation Matters
- Reproducibility: Proper documentation ensures that AI models can be recreated by other team members or by future developers, facilitating collaboration and preventing knowledge loss.
- Model transparency: Documentation allows stakeholders to understand the decisions made throughout the AI lifecycle, improving transparency and accountability.
- Ease of maintenance: When AI models require updates, having detailed documentation ensures that future modifications are made with full knowledge of previous configurations, preventing errors.
- Compliance: In regulated industries, thorough documentation of AI processes is often required to meet compliance standards.
Implementing This Best Practice
- Track experiments with MLflow or Weights & Biases: Use experiment-tracking platforms like MLflow or Weights & Biases to automatically document training configurations, hyperparameters, and performance metrics. These platforms also provide version control for models, making it easy to reproduce experiments.
- Document preprocessing and feature selection: Keep detailed records of data preprocessing steps, feature engineering, and any transformations applied to the data. This helps ensure that models can be retrained or fine-tuned with the same configurations.
- Maintain shared repositories: Store all relevant documentation, including code, data pipelines, and results, in shared repositories (e.g., GitHub, Bitbucket) that are accessible to all team members. This facilitates collaboration and ensures that everyone is on the same page.
- Create structured templates for documentation: Use standardised templates to document each stage of development, including data sources, preprocessing steps, model architectures, training settings, and evaluation metrics. Structured documentation promotes consistency and completeness across teams.
Conclusion
Comprehensive documentation at every stage of AI development is key to ensuring reproducibility, collaboration, and long-term maintainability. By using experiment-tracking tools and maintaining shared repositories, organisations can preserve institutional knowledge and support future model iterations. Documentation is a foundational practice for transparent and efficient AI development.
