I don’t know anything about the newsletter. The core of the article seems to be observing a shift in AI/ML/LLM opportunities. Where before, most people in the field were developing the base models and doing the arduous and highly complex work of training models (what the author calls ML Engineers), now the majority of this field will be people who use those pre-made and pre-trained models, tweaking and applying them for more and more specific and quantifiable uses (what the author calls AI Engineers). He drew a malleable line between the two as whether you’re interacting with the model directly or via an API.
Thanks for summing it up! I get the point of the article a bit more clearly now.
I wonder if “AI engineer” isn’t kind of superfluous in that case? It’s essentially just the new normal for software developers/engineers. Another API or tool to interact with to produce whatever product we’re building. Where does the specialist competencies come in, besides having a more intimate knowledge of the APIs and basic understand of how this tech works?
I agree with how you characterized it and the term “ai engineer” didn’t resonate with me as defined by the author. If such an engineer doesn’t need to know about the data involved (“nor do they know the difference between a Data Lake or Data Warehouse”) then I don’t think they will be able to ship an AI/ML product based on data.
New titles can be helpful for sorting out different roles with some shared skillsets such as the distinction which emerged between Data Scientist and ML Engineer at some companies to focus the latter on shipping production software using ML.
I don’t know anything about the newsletter. The core of the article seems to be observing a shift in AI/ML/LLM opportunities. Where before, most people in the field were developing the base models and doing the arduous and highly complex work of training models (what the author calls ML Engineers), now the majority of this field will be people who use those pre-made and pre-trained models, tweaking and applying them for more and more specific and quantifiable uses (what the author calls AI Engineers). He drew a malleable line between the two as whether you’re interacting with the model directly or via an API.
Thanks for summing it up! I get the point of the article a bit more clearly now.
I wonder if “AI engineer” isn’t kind of superfluous in that case? It’s essentially just the new normal for software developers/engineers. Another API or tool to interact with to produce whatever product we’re building. Where does the specialist competencies come in, besides having a more intimate knowledge of the APIs and basic understand of how this tech works?
I agree with how you characterized it and the term “ai engineer” didn’t resonate with me as defined by the author. If such an engineer doesn’t need to know about the data involved (“nor do they know the difference between a Data Lake or Data Warehouse”) then I don’t think they will be able to ship an AI/ML product based on data.
New titles can be helpful for sorting out different roles with some shared skillsets such as the distinction which emerged between Data Scientist and ML Engineer at some companies to focus the latter on shipping production software using ML.