A single metric rarely tells the truth about a model. Accuracy can improve while a critical slice gets worse. Loss can move in the right direction while generated samples degrade. A model can look fine in aggregate and fail on the examples the business actually cares about.

That is why experiment tracking needs more than scalar metrics.

Track the data profile

A training run should make it easy to inspect the data it used. Dataset versions, schema changes, missing values, class balance, summary statistics, and sample rows all matter. Without that context, a model regression turns into archaeology.

For tabular and structured data, summary statistics help catch obvious problems early: shifted distributions, new null patterns, broken joins, and unexpected category growth. For image and text workloads, logged samples help teams inspect whether preprocessing did what it was supposed to do.

Log artifacts, not just metrics

Useful model debugging often depends on artifacts:

• Prediction samples.
• Confusion matrices.
• ROC and precision-recall curves.
• Generated images.
• Feature importance reports.
• Evaluation notebooks.
• Model files and checkpoints.

Those artifacts need to be tied to the run that produced them. Saving them somewhere in object storage is not enough if nobody can answer which code, data, parameters, and environment produced the artifact.

Inspect behavior over time

For computer vision and generative workloads, logging images during training can reveal behavior that metrics hide. You can see whether predictions stabilize, whether augmentations are corrupting inputs, and whether a model is learning meaningful structure or memorizing noise.

The same idea applies outside images. Text generations, ranked examples, failed predictions, and slice reports give teams concrete evidence instead of another dashboard full of averages.

How Polyaxon fits

Polyaxon tracks metrics, logs, artifacts, lineage, and runtime metadata for each run. That gives teams one place to compare experiments, inspect outputs, understand data and model versions, and move from a failed metric to the files and logs that explain it.