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Productionizing AI at LinkedIn
Practical AI: Machine Learning, Data Science, LLM
How to Transform Bleeding Edge AI Technology Into Practical Use
AI in some sense is an engineering discipline. But I think we are very much far away and it comes for a reason, right? It's just that the inputs are not as deterministic as some of these other systems. So whenever you have a change in your data distribution or something happens, then how do you put in what the right unit and regression test to make sure that you're going to be able to catch some of these errors? How do you keep track of the number of people? And of course, many, many simple things that we possibly haven't spent enough time on in terms of feature lineage when something goes haywire.
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