Article
Fine-tuning adapts a foundation model to domain data when the base model does not meet predefined evaluation criteria. The process should begin with a specific performance gap rather than a general assumption that an adapted model will perform better.
Within the eomer dashboard, a team can select a training dataset, define the configuration, and start an adaptation job. The interface reports job progress and validation metrics while the model trains. These outputs help users identify failed runs, unstable validation results, or configurations that do not justify further compute.
After the job completes, the model registry stores the adapted model beside the original base model. Each entry retains the model version, training configuration, dataset reference, and evaluation record. This structure prevents an adapted model from replacing the baseline without a controlled comparison.
Both models should then run against the same holdout dataset. The comparison should use identical forecast horizons, input cut-off times, data transformations, and metrics. For probabilistic outputs, the evaluation should also test calibration and interval coverage. For production systems, the comparison should include latency and resource demand.
A deployment decision should follow the criteria defined before training. If the adapted model improves the relevant metrics without breaching calibration, latency, or cost limits, the team can promote it to the next deployment stage. Otherwise, the base model remains active and the registry retains the experiment for future reference.
This process treats fine-tuning as a testable model change rather than an automatic upgrade.
