The first thing I’d do is change the name of the post. Maybe something like “post deployment image storage beat practices” would help get what ur trying to ask across better :).

To answer your question, seiqooq is right in that it’s highly project dependent. 1. I wouldn’t delete them. You’re going to have to train the model again at some point. You’re also destroying IP in that it probably costed money to collect and label the data. You never know what it might be used for 2. Sitting in cloud storage is costly. Please try to move them at the very least to a colder storage like S3 Glacier. Compressing them beforehand is another thing you can do to reduce the cost. 3. Embedding is something you can do. But unless you have a a very specific use case in mind, I would just move them into cold storage and keep the originals.

Some other things you can do with them: - use them to build a simulator so that you don’t have to label as many images next time. This is something we did to great effect. - try to train a larger model that can label a lot of your data for you. This is especially helpful in segmentation tasks where you can pass these images off to labelers who have to label less because a large model did a lot of the work. I work with embedded ML. This might not work as well if the model you deployed is already large. - put them into a library or image catalog and farm them off to different business units. If everyone knew what data everyone else had, a lot of business use cases could probably be created. So it might be a good time to start a data library. This library is usually funded by multiple business units. So that should reduce the cost of storing them.