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Comment by Salgat | Hacker News Reader

Most folks don't realize that the vast majority of ML applications can be done with preexisting models, you don't need someone with a PhD to fine tune it unless you need to eek out a tiny bit of improvement. The real value is in the person preparing the data for training.

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This is not true at all unless you're only referring to commodity applications. There's no preexisting model for general regression or seq to seq, for instance. You can say, oh, that's a good fit for an LSTM, but you still need to tune the individual layers to suit the application. Although I am slightly conflating ML and DL, since you mention training which heavily implies DL.

Most folks can get away with logistic or linear regression. A tiny portion of those might need to move to random forest. And an even tinier portion may justify deep neural networks. The vast majority of ML is not deep learning, nor is it necessary. We're talking very boring typical business cases.

Yeah, this fixation with deep learning is kinda weird, given that most companies don't have vast amounts of images/text lying around and a pressing business need to understand them.

I'm a big fan of starting simple, which means linear/log regression (normally with lasso, as it does variable selection).

Then, if you can prove business value, it may make sense to start trying to use more unstructured data.

I think it is because linear regression is not science fiction enough even if that is the best that can be done on the data. We didn't hire this AI guy to do simple regressions!

Anyone's who hires an AI person unknowingly, probably doesn't know the difference.

I mean, you can call it optimization with a specially chosen sparse function if you like.

From experience, there are lots of companies looking precisely for an "AI person" who is going to use a bunch of complicated sounding buzzwords and build some overly complex thing without evaluating simpler options. This kind of person gets way more attention than someone who suggests that what the company wants to do can probably be accomplished with a simple shell script, or someone that tells them AI is not magic and in the absence of some data capturing a relationship it can't really do anything. It's a hyped up field and has attracted lots of people who care about hype. (I work in the field, not trying to denigrate it, my point is that many / most business adopters want "AI" for all the wrong reasons

Agreed. Lots of companies are more concerned with the marketing optics of using "AI".

Ran into this very thing writing software for customer support teams. There's a Data/ML team within the company and there was a lot of talk about getting them to help plan the software to add abilities to "learn" what customers wanted with their support requests. The reality of the situation is that you can just take a step back and look at the pain points for the CSRs, the highest cost calls and where the time is spent servicing the customer. In the end, it's pretty clear that we can just do a few queries up front to realize that "oh, this person ordered 2 items of the same size in their last order and haven't returned one yet, it's very likely that they want to return one" or any number of other common scenarios that were found and then prioritized based on a simple cost/benefit analysis.

Could the Data/ML team have done something cool and likely complex to show clearer patterns and maybe get something in place to continually refine that data? Probably. Would it have saved more money for the company eventually? Unknown. Would it have cost substantially more money to design and implement? Pretty sure.

To be fair, any competent data team would have done the same analysis first, before attempting to build complicated models.

As I keep saying to people, sometimes the best model is a well chosen graph.

I don't have a lot of experience working with data/ml teams, so this is good to know. Thanks. :)

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