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I’m responding to the idea that MLops infrastructure is heavily underestimated. In which case you need more time, expertise, infrastructure, etc. than previousl

I’m responding to the idea that MLops infrastructure is heavily underestimated. In which case you need more time, expertise, infrastructure, etc. than previously realized. AKA money I agree with everything you said for whatever it’s worth. I’m mostly just making the observation that there is a lot of machine learning endeavors that aren’t generating much value.

sitkack 4y ago
> a lot of machine learning endeavors that aren’t generating much value. I agree with this. From what I have seen, execs want something fancy, when you could give a boring-ish tool that reduces your OODA loop cycle time to 30% of what it was using unsexy techniques. Much of it having to do with tolerancing of answers, being within 5-10% is more than enough to drive the business, but someone somewhere said it had to be exact and that blows out the latency budget. When engaging in consulting gigs, it is super important to know what kind of org you dealing with before you get involved. The myopic penny pinching orgs should be steered well away from, which I think was your point.