Although I studied machine learning and was originally hired for that role, the company pivoted and is now working with LLMs, so I spend most of my day working on figuring out how different LLMs work, what parameters work best for them, how to do RAG, how to integrate them with other bots.
Would you not consider LLMs as a part of machine learning?
Ask HN: Machine learning engineers, what do you do at work?
101–110 of 233 posts
Re: Ask HN: Machine learning engineers, what do you do at work?
#102Do people feel like they are more or less in demand with the hyper around genai?
Re: Ask HN: Machine learning engineers, what do you do at work?
#103- Collaboration with stakeholders & TPMs and analyzing data to develop hypotheses to solve business problems with high priority
- Framing business problems as ML problems and creating suitable metrics for ML models and business problems
- Building PoCs and prototypes to validate the technical feasibility of the new features and ideas
- Creating design docs for architecture and technical decisions
- Collaborating with the platform teams to set up and maintain the data pipelines based on the needs of new and exiting ML projects
- Building, deploying, and maintaining ML microservices for inference
- Writing design docs for running A/B tests and performing post-test analyses
- Setting up pipelines for retraining of ML models
Re: Ask HN: Machine learning engineers, what do you do at work?
#104Earlier quoted context omitted.
Maybe pip should not work by default (but python -m venv then pip install should)
Legends say there were times when you'd have a program.c file and just run cc program.c, and then could just execute the compiled result. Funny that programmer's job is highly automatable, yet we invent ourselves tons of intermediate layers which we absolutely have to deal with manually.
Re: Ask HN: Machine learning engineers, what do you do at work?
#105Re: Ask HN: Machine learning engineers, what do you do at work?
#106pip 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!
Although I think the UX of poetry is stupid and I do not agree with some design decisions, I have not had any dependency conflicts since I used it.
Re: Ask HN: Machine learning engineers, what do you do at work?
#107pip 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!
Re: Ask HN: Machine learning engineers, what do you do at work?
#108pip 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!
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.
Re: Ask HN: Machine learning engineers, what do you do at work?
#109Earlier quoted context omitted.
> HIPAA my left foot. That was my experience as well - training documentation for fresh college grads (i.e. me) directed new engineers to just... send SQL queries to production to learn. There was a process for gaining permissions, there were audit logs, but the only sign-off you needed was your manager, permission lasted 12 months, and the managers just rubber-stamped everyone. That was ten years ago. Every time I t…
... you didn't have a UAT environment?
Re: Ask HN: Machine learning engineers, what do you do at work?
#110I'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…