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Could you explain the statement, "At such places, discovering a hidden requirement for more resources is a great reason to change directions." I don't understa
by sitkack 4y ago
Could you explain the statement, "At such places, discovering a hidden requirement for more resources is a great reason to change directions."
I don't understand the argument, not feigning confusion. Everyone has to scale at some point and every solution has its limits. If they were successful with Airflow and Pandas/Numpy for a long time and then well, now the fan is spinning. They are going to call for experts that put the pieces in place. Asking for help is a sign of maturity. It really depends on the state of system when the experts arrive.
I personally think every org can use ML (it all decays to statistics, then linear algebra).
- nonethewiser 4y agoI’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.