It's odd that OP didn't seem to consider applying the nearest cached value for any given slider stop.
The Gaussian frequency was a cool idea, however.
Also, I would speculate that projected sales would likely be a continuous function in most cases, so I'm curious why they didn't try fitting a function based on initial results.
Ah, good point. To be honest, interpolation didn't even cross my mind.
The model output wasn't just one number, it was a messy JSON with a 12-week forecast. Trying to average two of those felt like a whole other task, and with the deadline, my brain was just stuck on how to pick the right numbers to cache.
But yeah, it's a really great idea. Will definitely keep it in mind for the next demo.
I had a 7-day compute problem, 3 days to solve it, and no extra hardware. Here's what worked.
It's odd that OP didn't seem to consider applying the nearest cached value for any given slider stop.
The Gaussian frequency was a cool idea, however.
Also, I would speculate that projected sales would likely be a continuous function in most cases, so I'm curious why they didn't try fitting a function based on initial results.
OP here,
Ah, good point. To be honest, interpolation didn't even cross my mind.
The model output wasn't just one number, it was a messy JSON with a 12-week forecast. Trying to average two of those felt like a whole other task, and with the deadline, my brain was just stuck on how to pick the right numbers to cache.
But yeah, it's a really great idea. Will definitely keep it in mind for the next demo.