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>> A team of 5 ML engineers can improve an ad targeting system by 5 % and if the company is google that’s billions. Not going to pick up on your arbitrary 5%
by heavenlyblue 7y ago
>> A team of 5 ML engineers can improve an ad targeting system by 5 % and if the company is google that’s billions.
Not going to pick up on your arbitrary 5% constant here, but please elaborate how these ML engineers are any different from anyone with a quantitative background?
- bumby 7y agoI think that's the key here. It's not ML magic that's driving success, it data literacy. Steve Levitt (of Freakanomics fame) said his advice to students would be to ensure they have base knowledge in understanding data analytics regardless of their domain. ML is just the sexy subset that gets all the attention
- ghaff 7y agoAnd ML is a tiny part of the whole data science toolchain and process. Getting the data to the point where you can use it for something interesting is probably the hardest part.
- thewarrior 7y agoMy point was that cutting edge ML can add a bit more on top of what data literacy can achieve. At scale that’s worth a lot.
- thewarrior 7y agoWhat I meant was for eg applying deep neural networks on large data sets for eg can give you a few percentage points improvements on systems implemented without them. At scale it makes a big difference.