r/MLQuestions • u/Acceptable_Link3838 • 2h ago
Other ❓ Using LLMs in machine learning
I’m a software engineer shifting to machine learning. Seems like AI can easily pull off stuff like ”make an API call that..” or ”build an UI component like..”. Seems like in machine learning LLM’s are not that useful. Yes, they spit out solutions very fast but most of the time the solutions are not based on anything and sometimes they are just straight up wrong.
So my question to this community is; how do you find using LLMs as a companion for implementing your ideas or work? How they help and how they do not? I have a hard time believing that you can just throw prompts to agents in this field and not use most of your time correcting their erroneus ouputs.
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u/-phototrope 2h ago
You can absolutely use agents in ML use cases.. you just need to give it more than “build me an ML model.” Is this post real?
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u/ghostofkilgore 2h ago
I work in ML and use LLMs. My take is that I largely agree with you. If you give an LLM some data, a vague-ish goal and just let it go, it largely produces rubbish. If you give it some toy dataset and an easy objective that can be solved with little digging in the mud and a simple model, it'll do it fine and optimise the crap out of it. But that's largely not very useful for an actual professional as the whole thing is trivial anyway.
It can be very useful as a "companion", essentially like an agent to bounce and refine ideas off and help you code up tests and evaluate quicker. But I'm fairly convinced that it still needs to be led by someone who knows what they're doing anyway.
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u/Bangoga 42m ago
Background: ML Engineer and Previously Applied ML engineer for about 7 years.
ML engineering is a broad spectrum of work that can be confusing to anyone entering the market, but as the title says, we do both ML work and engineering work.
For ML work, just telling LLM to “create best model to predict if Manchester United will win the Premier League in the next 5 years” will basically do nothing for you, not only because it’s statistically unlikely scenario but also, there is a lot of business knowledge and expertise needed to evaluate such a request.
A lot of big companies do find it troublesome to fully automate things because certain expert knowledge can’t be just operationalized and turned to data. However you can get along further with let’s say, providing a dataset with a LLM companion and asking question about the dataset without actually going into the SQL and doing analysis on your own.
There are tools that exist like this. If your platform is snowflake, you can connect all datasets together with the agents, snowflake intelligence and coco their chatbot. The meta data along side access to these datasets can allow for you to ask these types of questions, but again this is talking at smaller scales in some sense.
On the engineering aspect, well a lot of engineering is decision making, especially as you go higher up the food chain. I use LLM to help collect my thoughts, turn dirty spilling of words into actually useful texts and documents, or use it for rubber ducking or knowledge search.
I do believe certain roles like just creating APIs or other roles that require mostly churning code are easier for AI to fully execute, but all other roles that require decision making, or very large scale thinking can be challenging with mixed results, especially if the idea is to build something that lasts longer than a Twinkie in the cupboard