Model Versioning

What is model versioning?

Think training a model once is the end of it? In reality you run dozens or hundreds of experiments, tweak data and parameters each time, and end up with a pile of model files. Model versioning manages those models the way Git manages code — it records the model, training data, config and metrics for each version, so you can always find the one that worked best.

Why isn't a filename enough?

Filenames lie
"model_v2_final_REALfinal.pt" — we've all been there. Name files by hand and within weeks you won't know which is which.
A model is more than weights
To reproduce a model you also need the code, data, hyperparameters and environment. Miss any one and you can't get it back.

What does it usually track?

Weights and config
The model file itself plus the hyperparameters used in training.
Data and code
Which dataset and which code version trained it, so everything is traceable.
Evaluation metrics
Accuracy, latency and more, stored with each version for easy comparison.

What you get out of it

Roll back when you need to, reproduce when you want to, and compare A/B with evidence. When something breaks in production you can switch back fast; in a team, who trained what and how is a quick lookup.

Bottom line: model versioning gives AI models an "archive system" — every version traceable, and rollback always an option.