Configure a training run and follow its progress. Compare validation results and inspect the examples that still need attention.
A training run should test an assumption: whether more varied examples help, whether a different model fits the task, or whether certain classes need better labels. Keep the dataset and configuration connected to that question.
Use the training workspace to inspect the run and its validation results. Loss curves can reveal progress, but they do not tell the whole story. Review example outputs with the people who understand the consequences of a wrong prediction.
Follow training and validation trends.
A completed run is a decision point. You might need different labels, a better data sample, or another model configuration. Bring the evidence back to the team before promoting a result to a deployment candidate.
Review results with your domain experts.
Training workspace is part of the connected workflow: prepare, annotate, select, train, and deploy. Your team can revisit an earlier step when evaluation reveals something new. The product illustrations explain the workflow without uploading a dataset.