Model versioning involves what?

Model versioning involves what?

• A. Tracking only final model.
• B. Randomizing hyperparameters.
• C. Tracking model iterations, datasets, and configurations.
• D. Ignoring training data.

Answer: (C)

Model versioning is about preserving the full lineage of a model so you can reproduce results and compare experiments. It captures not just the final model artifact, but the exact iteration of the model, the datasets it was trained on, and the configuration settings used during training, including hyperparameters, preprocessing steps, software versions, and environment details. This enables traceability, auditing, rollback to previous versions, and understanding how changes in data or settings affect performance. Tracking only the final model misses the context that produced it, while randomizing hyperparameters or ignoring training data aren’t how versioning works. By recording iterations, data, and configurations together, you create a reproducible path from development to deployment.

Model versioning is about preserving the full lineage of a model so you can reproduce results and compare experiments. It captures not just the final model artifact, but the exact iteration of the model, the datasets it was trained on, and the configuration settings used during training, including hyperparameters, preprocessing steps, software versions, and environment details. This enables traceability, auditing, rollback to previous versions, and understanding how changes in data or settings affect performance. Tracking only the final model misses the context that produced it, while randomizing hyperparameters or ignoring training data aren’t how versioning works. By recording iterations, data, and configurations together, you create a reproducible path from development to deployment.