Almost all math graduates at my undergrad ended up going into (a) finance as quants, (b) PhD program, (c) data or SWE-related job in tech [me], or (d) some kind of K-12 math educator job.
Probably 30% PhD, 30% SWE/data in tech, 20% quant, 20% K-12 education. The people who did K-12 wanted to do that for much of their life typically, and the people who did PhD either (50%) knew that's exactly what they wanted to or, or really didn't know what they wanted to do.
Perhaps our program doesn't reflect the story on a national level though. Unsure there.
1. This is awesome. Seriously, I will use this the next time I'm prototyping an image or text model.
2. Is there a way to entirely disable telemetry? I could imagine wanting to use this for models within a company that use sensitive data, etc., but wouldn't want to or be able to use it if the model interface was published either to gradio's internal APIs or to some sharable link that wasn't on a corporate network.
1. How are you handling statistical significance questions / ensuring the A/B test has enough statistical power to be meaningful?
2. Same for seasonality.
Are you offloading much of the statistical concerns to the user or are you hoping to answer these for them?
Have to say, I don't really understand the hate on this thread having worked in this area. This seems like an awesome product that can really help sellers.
Could cohort based on geo-location instead of completely at random. Makes analysis a bit more challenging though and doesn't completely solve the problem of course.