일정 기반 재학습과 트리거 기반 재학습

The world keeps moving after you ship, so a model slowly drifts out of date. Retraining is how you refresh it on new data before quality slips. 🔄

You can retrain on a clock or in response to a signal. The two big patterns are scheduled retraining and trigger-based retraining.

Scheduled retraining runs on a fixed cadence, like every night or every week. It is simple, predictable, and easy to reason about.

A cron expression encodes that cadence in five fields. This cron line means run at 02:00 every day, a classic retraining slot.

Schedules can waste compute when nothing changed, or react too slowly when things change fast. The cadence is a guess, not a measurement.

Trigger-based retraining fires only when a real condition is met, such as drift crossing a threshold. You retrain because you need to, not because the clock said so.

Common triggers are data drift, a drop in live accuracy, or a fresh batch of labeled data landing. Each one signals the model may be falling behind.

A trigger is often just a comparison against a threshold. Here a drift score above 0.2 kicks off a retrain.

Triggers only work if you are already measuring something live, like drift or error rate. Without monitoring, there is no signal to trigger on.

Most mature teams use both: a safety-net schedule plus drift triggers. The schedule guarantees freshness, the trigger catches sudden shifts.

Stable, slow-moving data favors a simple schedule. Volatile data with good monitoring favors triggers. Match the cadence to how fast your world changes.

When does trigger-based retraining run?

You learned the two retraining patterns: scheduled runs on a cron cadence, trigger-based runs on a live signal. Many teams blend both for safety and speed. ✅