171–180 of 233 posts
Highly paid cleaning lady. With dirty data you get no proper results. BTW: perl is much better than python on this. Highly paid motherboard troubleshooter, because those all those H100's really get hot, even with watercooling, and we have no dedicated HW guy. Fighting misbehaving third-party deps, as everyone else.
In a given week, I usually do the following: * 15% of my time in technical discussion meetings or 1:1's. Usually discussing ideas around a model, planning, or ML product support * 40% ML development. In the early phase of the project, I'm understanding product requirements. I discuss an ML model or algorithm that might be helpful to achieve product/business goals with my team. Then I gather existing datasets from ana…
How do you select what papers to read? How often does that research become relevant to your job?
The research becomes relevant immediately because my team is always looking to incorporate it into our production models right away. Of course it does take some planning (3-6 months) before it's fully rolled out in production.
Do people feel like they are more or less in demand with the hyper around genai?
pip install pytorch Environment broken Spend 4 hours fixing python environment pip install Pillow Something something incorrect cpu architecture for your Macbook Spend another 4 hours reinstalling everything from scratch after nuking every single mention of python pip install … oh time to go home!
Python's dominance is holding us back. We need a stack with a more principled approach to environments and native dependencies.
13 years ago when I was trying to explore the field R seemed to be the most popular, but looks like not anymore. (I didn't get into the field, and do just a regular SWE, so I'm not aware of the trends).
There is also a lot of development in Elixir ecosystem around the subject [1].
[1](https://dashbit.co/blog/elixir-ml-s1-2024-mlir-arrow-instruc...)
Highly paid cleaning lady. With dirty data you get no proper results. BTW: perl is much better than python on this. Highly paid motherboard troubleshooter, because those all those H100's really get hot, even with watercooling, and we have no dedicated HW guy. Fighting misbehaving third-party deps, as everyone else.
Could you talk more about “BTW: perl is much better than python on this.”?
$variable = something() if sanity_check()
do_something() unless $dont_do_thatEarlier quoted context omitted.
You could learn how to use Python. Just spend one of those 4 hours actually learning. Imagine just getting into a car and pressing controls until something happened. This wouldn't be allowed to happen in any other industry.
Could you be a bit more specific about what you mean by "You could learn how to use python"? What resources would you recommend to learn how to work around problems the OP has? What basic procedures/resources can you recommend to "learn python"? I work as a software developer alongside my studies and often face the same problems as OP that I would like to avoid. Very grateful for any tips!
Getting my models dunked on by people who can't open MS Outlook more than 3 tries out of 5, however, have a remarkable depth and insight into their chosen domain of expertise. It's rather humbling. Collaborating with nontechnical people is oddly my favorite part of doing MLE work right now. It wasn't the case when I did basic web/db stuff. They see me as a magician. I see them as voodoo priests and priestesses. When…
> I tease insights out of a monstrous data lake of claims, Rx, doctor notes, vital signs I'm curious to know the tech stack behind converting unstructured to structured data(for reporting and analysis)
I've been doing machine learning since the mid 2000s. About half of my time is spent keeping data pipelines running to get data into shape for training and using in models. The other half is spent doing tech support for the bunch of recently hired "AI scientists" who can barely code, and who spend their days copy/pasting stuff into various chatbot services. Stuff like telling them how to install python packages and u…
There are companies where applied scientists are required to code well. Just ask how they are hired before joining (that should be a positive feature).
I’m a regular software dev but I’ve had to do ML stuff by necessity. I wonder how “real” ML people deal with the stochastic/gradient results and people’s expectations. If I do ordinary software work the thing either works or it doesn’t, and if it doesn’t I can explain why and hopefully fix it. Now with ML I get asked “why did this text classifier not classify this text correctly?” and all I can say is “it was 0.004 p…
Not all ML is built on neural nets. Genetic programming and symbolic regression is fun because the resulting model is just code, and software devs know how to read code.
At least with symbolic regression you can treat the model as an analyzable entity from first principles theories. But that's not really particularly relevant to most failure modes in practice, which usually boil down to either missing some qualitative change such as a bifurcation or else just parameters being off by a bit. Or a little bit of A and a little bit of B.
I’m a regular software dev but I’ve had to do ML stuff by necessity. I wonder how “real” ML people deal with the stochastic/gradient results and people’s expectations. If I do ordinary software work the thing either works or it doesn’t, and if it doesn’t I can explain why and hopefully fix it. Now with ML I get asked “why did this text classifier not classify this text correctly?” and all I can say is “it was 0.004 p…
This seems to be the absolute worst of all worlds: the burden of software engineering with the tools of an English Language undergrad.