Thanks! AWS Lambda supports Docker image sizes up to 10 GB (according to their docs), so on the back end, 3-5 GB could still work.
Convect, in its current state, is still limited to small models (e.g. < 0.5MB). This is because the deployment happens by posting to a REST API, which hasn't yet been tested for large payloads.
I wrote up some tips for decoupling a large model from the data that's causing it to be large (https://convect.readme.io/docs/news-topic-model). However, it sounds like you're asking about a truly large model. Convect is still in the MVP stage right now, but we plan to handle larger models. 3-5 GB models will likely be feasible in the future.
Convect, in its current state, is still limited to small models (e.g. < 0.5MB). This is because the deployment happens by posting to a REST API, which hasn't yet been tested for large payloads.
I wrote up some tips for decoupling a large model from the data that's causing it to be large (https://convect.readme.io/docs/news-topic-model). However, it sounds like you're asking about a truly large model. Convect is still in the MVP stage right now, but we plan to handle larger models. 3-5 GB models will likely be feasible in the future.