engineering · 1 min read
Retraining and Fine-Tuning Cadence for Production AI
How often should you retrain or refresh fine-tunes? It depends on signal, not gut feel. Here is the cadence framework Bhogar AI uses.
BABhogar AI TeamProduct & Engineering
How often to retrain or re-fine-tune is the wrong first question. The right first question is: what signal triggers a retrain?
Why it matters
Triggers we use: drift exceeds budget, eval-quality drops, new data category appears at meaningful volume, base model deprecates. Calendar-based retraining without trigger discipline burns money.
How Bhogar AI approaches it
Bhogar AI ships trigger-based retraining: evaluator + drift detector + base-model lifecycle monitor decide when to refresh; retraining workflows are versioned and reproducible.
- Trigger-based retraining (drift, eval drop, new data, base lifecycle)
- Versioned, reproducible retraining workflows
- Per-trigger telemetry
- Cost forecast per retrain
- Compatible with major fine-tuning APIs and self-hosted training
What you get
Customers retrain when the data says to, not when the calendar says to - saving compute and improving quality outcomes.