Heyo, I looked a bit more at your service, since I am migrating one of our text classification models anyways right now. I decided against using it but maybe m

Heyo, I looked a bit more at your service, since I am migrating one of our text classification models anyways right now. I decided against using it but maybe my reasoning could still be helpful. (and I see a lot of potential so I want to help) What I am using instead is a combination of AWS Batch and Colab. My reasoning: # Local Development Yes it is true that ML code can be quite well separated from the rest. But then there is data. So the extract and load step. I know you have bindings to for example Postgres but I wouldn't trust you with my DB. And always moving over files could be done (we keep a backup anyways) but it would be more of a hassle. Also even for the actual ML code it is nice to have it in an actual IDe with a good debugger etc. I prefer to code your ML code locally and then just package it and send it away to be trained. Also, yes there is a cost to set up the infrastructure but I prefer to solve that with code generation/templates and libraries (to send your docker to Lambda for inference and Batch/Colab for training). It is a cost that is paid once and then never again. # Price Your gpu instance costs 1 dollar an hour which is about 3 times as much as a p2.xlarge spot instance (which I assume is the closest one). Colab of course is 10 bucks a month / free. This is ignoring AWS credits for now. (would also be good to know which exact GPU you provide)