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The barriers to AI engineering are crumbling fast

(blog.helix.ml)
250 points lewq | 3 comments | 14 Nov 24 14:56 UTC | HN request time: 0.634s | source
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ein0p ◴[14 Nov 24 17:11 UTC] No.42138370[source]▶
An AI engineer with some experience today can easily pull down 700K-1M TC a year at a bigtech. They must be unaware that the "barriers are coming down fast". In reality it's a full time job to just _keep up with research_. And another full time job to try and do something meaningful with it. So yeah, you can all be AI engineers, but don't expect an easy ride.
replies(1): >>42141546 #
actusual ◴[14 Nov 24 21:42 UTC] No.42141546[source]▶
I run an ML team in fintech, and am currently hiring. If a resumè came across my desk with this "skill set" I'd laugh my ass off. My job and my team's jobs are extremely stressful because we ship models that impact people's finances. If we mess up our customers lose their goddamn minds.

Most of the ML candidates I see now are all "working with LLMs". Most of the ML engineers I know in the industry who are actually shipping valuable models, are not.

Cool, you made a chatbot that annoys your users.

Let me know when you've shipped a fraud model that requires four 9's, 100ms latency, with 50,000 calls an hour, 80% recall and 50% precision.

replies(2): >>42142040 #>>42144234 #
1. btdmaster ◴[14 Nov 24 22:48 UTC] No.42142040[source]▶
What does 50% precision mean in this case? I know 50% accuracy might mean P(fraud_predicted | fraud) = 50%, but I don't understand what you mean by precision?
replies(1): >>42142377 #
2. chychiu ◴[14 Nov 24 23:24 UTC] No.42142377[source]▶
Precision = True Positive / (True Positive + False Positive) = 1 - False Positive Rate

On that note, I'm surprised the precision / recall for fin models are 80% / 50%

replies(1): >>42145938 #
3. disgruntledphd2 ◴[15 Nov 24 11:26 UTC] No.42145938[source]▶
They are obviously relatively low stakes, otherwise I'd be super worried.