@tyd Could you maybe do a blog post on these alternatives that are working for people? My situation is a voice-based language practice app that has 20+ different models. Each of these represents a different domain (ordering at restaurant, job interview, exchanging currency, etc.). Spinning up 20 Rasa servers and then figuring out how to scale individual higher-demand scenario servers seems like a pretty tough approach.
I’m wondering if it’s feasible to jam everything into one giant model and then pass in the current scenario context to custom NLU pipeline components that will only predict intents from the scenario context (e.g., all intents are prefixed by scenario code like job_greet, job_deny, … restaurant_greet, restaurant_deny…).
Then if context:‘job’ is sent in, the pipeline will discard all non job_* intents in its prediction.
Or is that too naive?