บันทึกการตรวจสอบและการทำซ้ำได้

An audit trail is a tamper-resistant record of who did what, when, so any model decision can be explained long after it happened. 🧾

The goal is simple but strict: given a prediction from months ago, you must rebuild the exact model and inputs that produced it.

To audit later, capture the request now. Save the input features, the output, and the model version on every call.

A bare prediction is useless for audits. Always store the model version beside it so you know exactly which model answered.

Lineage connects that version back to its build. The registry version points to an MLflow run holding the data and code that made it.

With the run id you can rebuild the world: check out the logged commit, restore the pinned data, and rerun training.

An audit log you can edit proves nothing. Write to append-only storage so records can be added but never quietly changed.

Decide how long records must live. Regulated fields often demand retention of years, so set it deliberately, not by accident.

Code alone is not enough. Log the locked dependencies too, since a different library version can change a result silently.

The trick is to make trails automatic. When logging happens on every run and request, you are always ready for an audit, not scrambling. ✅

To prove a result, feed the logged inputs back into the rebuilt model. A matching output confirms the trail is genuinely reproducible.

Think about what you must log to reconstruct an old prediction.

You saw how audit trails log every prediction with its version, write immutably, and use lineage to fully reproduce any past result. ✅