---
title: "Model training with no experiment tracking | Metacenta"
description: "When a trained model misbehaves, the first question is what produced it. How we spot training libraries with no experiment tracker declared, and when that is fine."
url: "https://metacenta.com/rules/ai-training-without-tracking"
updated: "2026-10-14"
tokens: 500
---

AI lifecycle · rule ai-training-without-tracking

# Model training with no experiment tracking

A Metacenta review checks this under the rule **Model training records what produced which model**. Everything below applies whether or not you ever commission one.

## What this rule checks

Where a project declares a model-training library, this rule checks that an experiment-tracking library such as MLflow or Weights & Biases is declared alongside it. It reports once for the project.

## Why it matters

When a model in production behaves oddly, the first question is what data and which settings produced it. Without a record, the honest answer is usually that the run cannot be reproduced.

## How to fix it

**Record which run produced which model.** Add an experiment tracker around the training loop, recording the data version, parameters and resulting model for every run.

## When it is fine to leave

Runs tracked somewhere the manifest cannot show, such as a hosted MLflow server, a separate repository or your own harness, are tracked in practice. We report this at medium confidence for that reason.

## What we need to check it

The dependency manifests in your repository. We reason from what they declare, never from what the team does, so the finding is medium confidence at most.

[Ask about a review](https://metacenta.com/enquire?src=rule-ai-training-without-tracking&via=md)

## Related pages

- [Unpinned AI and ML library versions](https://metacenta.com/rules/ai-dependency-unpinned)
- [LLM apps with no evaluation suite](https://metacenta.com/rules/ai-llm-without-evaluation)
- [LLM calls with no tracing or cost tracking](https://metacenta.com/rules/ai-llm-without-observability)
- [Jupyter notebooks committed with outputs](https://metacenta.com/rules/ai-notebook-outputs-committed)
- [A sample review, as a client reads it](https://metacenta.com/sample)
- [All published rules](https://metacenta.com/rules)
- [How a review is assessed](https://metacenta.com/standard)