I am using such a company for my day to day, and in my experience it is less about providing resources and more about managing everything on them.<p>Model versioning, training artifacts versioning, training code versioning, training and test data versioning, providing developer environment on a gpu machine, serving a model and some more tasks are very costly for a company to implement on their own using open source tools.<p>A manager platform that does all that certainly is valuable and I will recommend it to any new ML team.<p>If these use cases don't click with you I suggest thinking through working with ML models long term on a team with a big customer base. What if you introduce new data and your model does better but then later does worse than the original? what if you tested your model and it had great performance but after 3 months in production it sucks, now you want to go back to your test data at the time and see why. what if you personal machine does not have gpus? what if you need a custom dev environment? what if different customers need your model at different versions of your training data.<p>I think it is rightfully a nascent niche.