alt Hacker NewsFor some kinds of forecasting it doesn't need to be exactly accurate - eg say you are forecasting traffic for how to scale a load balancer, it can be a bit off it will just cost you a little and you can adjust after - a lot of feedback systems dont need full accuracy. You wouldnt use this to forecast whether to buy shares. It basically replaces various traditional curve fitting tools. If you need a really good forecast yes you are going to build a domain model. At least this is my understanding...
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btchashpoint • today at 3:12 PM
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> say you are forecasting traffic for how to scale a load balancer, it can be a bit off
Problem is you really don't even need ML for the curve fitting tools. Just ARIMA or Holt Winters is enough and likely faster. Heck, Kubernetes HPA doesn't even implement full PID, just P.
It seems like the main challenge is going to be finding domains where accuracy is important enough to spend inference token money on, but not so important that you'd hire a team to build a custom model. Or maybe that team borrows Timestral weights to fine tune to the domain, but they probably already have a different model customized to the problem?