AI quality is often buried under layers of technical debt, especially post-deployment. While we celebrate hitting accuracy targets, the complexity only begins then. Here’s what to watch for:
- Untracked Data Drift: Subtle shifts can erode model performance unnoticed.
- Edge Case Testing Oversights: Ignoring the 5% can lead to significant issues.
- Poor Feedback Loops: User complaints should drive continuous improvement.
- Undefined Performance Decay Thresholds: Know when to intervene.
- “Frankenstein” Model Updates: Quick fixes lead to instability.
These issues are hidden beneath dashboards but can turn models into liabilities. To safeguard your AI efforts, invest in:
- Continuous data validation
- Proactive monitoring
- Automated re-testing
Don’t let your project sink beneath the surface. Dive deeper into this topic to fortify your AI strategies. Share your thoughts below or tag someone who needs to hear this!
