Audit Trails and Reproducibility
Reconstruct any past prediction on demand.
Audit Trails and Reproducibility is a free MLOps Academy lesson on CoddyKit — lesson 3 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the MLOps Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
What an Audit Trail Is
An audit trail is a tamper-resistant record of who did what, when, so any model decision can be explained long after it happened. 🧾
Reconstruct Any Prediction
The goal is simple but strict: given a prediction from months ago, you must rebuild the exact model and inputs that produced it.
Log Every Prediction
To audit later, capture the request now. Save the input features, the output, and the model version on every call.
log = {"ts": now(), "model_version": "3",
"inputs": features, "prediction": y}
prediction_store.write(log)Pin the Model Version
A bare prediction is useless for audits. Always store the model version beside it so you know exactly which model answered.
From Version to Build
Lineage connects that version back to its build. The registry version points to an MLflow run holding the data and code that made it.
Reproduce the Run
With the run id you can rebuild the world: check out the logged commit, restore the pinned data, and rerun training.
run = mlflow.get_run(run_id)
commit = run.data.tags["git_commit"]
subprocess.run(["git", "checkout", commit])Make Logs Immutable
An audit log you can edit proves nothing. Write to append-only storage so records can be added but never quietly changed.
Keep a Retention Policy
Decide how long records must live. Regulated fields often demand retention of years, so set it deliberately, not by accident.
Record the Environment
Code alone is not enough. Log the locked dependencies too, since a different library version can change a result silently.
mlflow.log_artifact("requirements.txt")Audit-Ready by Default
The trick is to make trails automatic. When logging happens on every run and request, you are always ready for an audit, not scrambling. ✅
Replay to Verify
To prove a result, feed the logged inputs back into the rebuilt model. A matching output confirms the trail is genuinely reproducible.
Quick Check
Think about what you must log to reconstruct an old prediction.
Recap
You saw how audit trails log every prediction with its version, write immutably, and use lineage to fully reproduce any past result. ✅
Frequently asked questions
Is the “Audit Trails and Reproducibility” lesson free?
Yes — the full text of “Audit Trails and Reproducibility” is free to read here on the web, and the MLOps Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the MLOps Academy course, upgrade to CoddyKit PRO.
What will I learn in “Audit Trails and Reproducibility”?
Reconstruct any past prediction on demand. You practise MLOps Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start MLOps Academy?
No prior experience is required. MLOps Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 3 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Audit Trails and Reproducibility” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this MLOps Academy lesson?
Yes. Every MLOps Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Trace Data-to-Model Lineage
- Write Model Cards
- Audit Trails and Reproducibility
- Access Control and Compliance