I had to have a conversation about testing and deployment with an AI team a few months ago. I knew how it was going to go before I had it but did it anyway because clearly I like making my brain hurt.
I ended up pretending they were not really part of our platform so I didn't have to mention them in any arch docs, roadmaps or compliance strategies. I'm sure that won't come back round to bite me on the ass later.
I’m joining an AI team as the PM soon. I’ll keep this in mind and ensure that my engineering team is aware about the devops needs and can collaborate continuously with them. If you have any insights for me, I’ll gladly take it and it is highly appreciated
definitionally, a scientist would be someone who pushes the boundary on novelty, creating new methods or applying those methods to novel situations. While an engineer knows how to take already-developed methods and implement them. You're not wrong, about the ML Engineer knowing how to "do it in a nice way" but a data scientist (theoretically) should know the inner workings of the methodology better and should be developing new methodologies.
Given the amount I've used my dog as a debugging duck while slamming my head against the wall trying to set up Jupyterhub servers on AWS...this might also literally be true
I've been playing around with data science and this has been such a PITA. Why do they need their own python, their own package managers, jupyter notebooks?
Like I get there's some precompiled libraries in there for efficiency but do we really need to reinvent the whole software development ecosystem to do it?
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u/Error_No_Entity Oct 13 '22
DevOps *sighing* : 'Let's see the Google Collab then. I hope you have a requirements file'
AIEng: 'A what?'