Comment by tuyguntn | Hacker News Reader

Comment by tuyguntn | Hacker News Reader

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question to experts of HN in ML/AI. Could you please share the beginner resources you think would worth for a person who wants to switch their domain from CRUD/backend APIs to ML/AI. There seems to be many branches of this domain, not sure where to start.

Is my understanding correct?

    * ML engineer -> engineer who builds ML models with pytorch (or similar frameworks)
    * AI engineer -> engineer who builds applications on top of AI solutions (prompt engineering, OpenAI, Claude APIs,....)
    * ML ops -> people who help with deploying, serving models

None of these terms have a formal definition. The only association rule you need is:

* Fancy Title -> Whatever the company wants it to be.

All of the above could realistically span from "does bleeding-edge work" to "has once opened a CSV".

85% of your ML project time will be spent on Data Quality and a little bit of Domain Feature Engineering.

If you want to make an impact, become excellent at those, you will be able to use these skills, for domains like Systems Integration and Business Analytics. Let the people who do Research bring you the Algorithms and nowadays even the trained Models.

I'd call the "AI Engineer" an Application Engineer, albeit one that specialises in integrating ML into software.

Kaggle is a good start

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