Archive experiment

You can archive an experiment if you don’t want to use it at the moment. The experiment will not be deleted but will not show up in the Evaluation view, and you cannot create deployments using the experiment.

Deep learning is an iterative process. Often you have to try many experiments with different approaches before you find the best performing model. This means that the Evaluation view can become cluttered, and it becomes hard to find the best experiment. That’s why you can archive experiments you’re not interested in.

If you archive an experiment, it will not be shown in the Loss and metrics curve or the Experiment ranking list.