Approved!

Yeah, its a good point. I'm going to assign myself to this task.

@klausman You have the greenlight to start. Talk to @LNguyen about getting the credentials.

I approve

We've created a ticket on our team's board to explore how we might migrate the modeling functionality of the application to Lift Wing (T308165).

Thanks for highlighting this Strainu!

These are good points, but we should definitely keep in mind that if folks want to use hit the inference/prediction API a lot, one option is to take advantage of Lift Wing being k8s and buy more boxes.

Awesome, thanks for this. I'll loop in the growth team.

The Product Dept reached out to me about enabling ORES in Hindi wikipedia's Recent Changes. They have two issues they need resolved:

Might need some quick review to see if we deployed.

This is for a new hire on my team.

@Htriedman lets talk about this next meeting

Some thoughts about this:

Thanks for this Luca. I thought about it yesterday and came to a similar conclusion (score cache >> online feature store) but for slightly different reasons. Mainly, I think that you have done well exposing is that a feature store (online/offline) is a big project, with lots of unknowns, and requiring lots of experimentation. My worry now with the feature store is that if Revscoring models are currently too slow without either a feature store or score cache, taking on a big, complicated, never-tried before solution (feature store) rather than a well understood solution (score cache) seems like a bad decision.