Version control for AI models

Training data quality directly determines your system's output and user experience quality. When models evolve through updates, maintaining data quality becomes even more critical. Document your data collection plan to avoid quality issues. Include what data you're collecting, how often it's refreshed, and what preprocessing steps you apply. This documentation helps future teams understand why certain choices were made. When updating models, consider your data maintenance plan:

• Preventive maintenance stops problems before they occur.
• Adaptive maintenance preserves your dataset while the real world changes.
• Corrective maintenance fixes errors that arise from data cascades.

Keep detailed logs of everything you change in datasets. Problems can occur from unforeseen issues and human error. Having comprehensive records helps diagnose issues when user complaints arise after updates. Split your data carefully between training and test sets for each model version. The split depends on factors like example count and data distribution. A typical split might be 60% training and 40% testing, but this varies by use case.