🚫 Data & AI Anti-Pattern #25: Treating Model Development as a One-Time Project

Theme: Measurement & Action

Models aren't set-it-and-forget-it—they're products. And products need maintenance.

📉 Performance drifts, but no one’s watching
📉 No retraining, so relevance fades over time
📉 Bugs, bias, and blind spots go undetected

Ask yourself 👇
Do your ML/AI models have owners, monitoring, and a plan for improvement—or were they just "launched and left"?

✅ How to fix it:

📈 Monitor model performance in production
→ Track not just accuracy, but impact on decisions and outcomes
→ Set alerts for data drift, performance degradation, and unexpected behavior

🔄 Plan for retraining and iteration
→ Schedule regular check-ins tied to data freshness or business cycles
→ Treat models like code: versioned, tested, and continuously improved

👥 Assign ownership for ongoing care
→ Make sure someone is accountable for the health and relevance of each model
→ Include product managers or operational partners in success tracking

🧠 Why it matters:
When models drift or degrade, decisions can go sideways, customer experiences suffer, and trust erodes if you’re not tracking impact beyond launch, you’re flying blind on outcomes—and that’s a risk no business can afford.