How you guys compare with sagemaker? It also lets you bring in custom containers as training and (batch/real-time) inference phase.
It’s fairly painful to productionize a model on Sagemaker — they make you think about a lot of things and fit into AWS primitives. Besides the code for the model, we don’t force users to think about anything. Our focus is helping engineers get models into production, not reading documentation.
Using our tool, you can fork a model and deploy it to production right away — there’s no time spent battling AWS primitives. We’re focused on developer experience above everything else - which means we enforce sandboxes on our platform to be consistent and reproducible.