SVMs in big data were already well liked back in 2010 as they could be scaled via map-reduce.

An issue in industry was that companies would not allow training on their data across customers. Understandably so, as SVM dimensions are readily interpretable given some thought. General purpose datasets were ill-suited too depending on the task.

So you'd start from scratch every time, annotate features like crazy, and often didn't have enough training data overall.

LLMs "solved" that by just ignoring the previous rules and training on everything, which lets the necessary world knowledge seep back in.

I'm sure we'll see many combinations of LLMs and regular machine intelligence in the near future.

Yeah, its pretty wild! I think one of the benefits, but also potential risks, with LLMs, is you can just point them at anything without a lot of expertise.

Building an SVM isn't a tricky job, but you probably need a data scientist involved, and to think carefully about a labeled dataset.

I think we're seeing the modelling equivalent of something like electron apps. Where they waste a tonne of resources, but make an easier developement experience.

Will be interesting to see how much or little this changes with LLM costs.

I've seen a few people comment along the lines of "yes but LLMs dont need lot of data nor competence". So with our classifier "Linnaeus", the data came from JIRA tickets plus additional signals. To get up and running we didn't use a lot of data (a few dozen tickets across approximately 20 classes to begin with - eventually expanded to 120+ classes). There was very little to no manual data labelling - mostly it was devs selecting "gold standard" JIRA tickets for us to ingest. The model was retrained daily, and we had extra signals like thumbs up/down with comments that would be used for re-training.

What has changed is that expensive models can be deployed instantly and generate training data until you swap over to training your own cheap models.

Your model only took minutes to train if you don't count the data collection and labelling work

So technically once you have training data via an expensive classifier on NL data, there is no reason one cannot reduce the footprint of the OP classifier to a very small one. I imagine they could just automate feature selection in a SVM(cross validate on CPU!) using an evaluator to train an optimal one.

It's pretty interesting how a very broad field of machine learning got reduced to only LLMs for most consumers. Probably it's due to marketing (and of course how powerful these models are), but their lack of capability in many tasks already forces people, even amateurs, to use LLMs to write deterministic code instead. I wonder if the sudden appearance of viral classifiers predicts more common use of smaller ML models as the next step?

I think this kind of change will pop up more often.

In software engineering, we learned a long time ago that giant monoliths are not the answer. You want smaller, but specialized units that work together. They perform better at their specific task, are more maintainable, you can just upgrade one part of the system without having to impact the others, ....

I don't know where the focus on building a single model that can do everything comes from.

I have replaced a few of my LLM pipelines with gemma4 embedding and a classifier/regression head now. LLMs are actually really good to gather the data and label it with only prompting, but after a while it is just more economic to train your own model.

Thanks for sharing, looks amazing. We have examples of this as well internally.

On my previous team at work, the data science team was using Open it's API for tagging of some natural language. The DS team spent weeks working on the prompt plus the outline of the pipeline before I was involved. It took maybe eight to 12 hours to run everything, and it was prone to API failures.

When I asked why we don't build some NLP tagging which would be cheaper and run in a fraction of time, the lead told me, "What if we need to use more unstructured data? GPT can handle that without any extra work."

The team has yet to make any other changes to the pipeline, but in the two-ish years since then, they've spent a ton of money and wasted countless hours on delayed processing because of it. Some day, maybe, they'll learn that traditional ML techniques are superior in virtually every way to just throwing data at an LLM to see what sticks.

It seems you had enough labels to train, that's not always the case.

Interesting! I had the idea to compute embeddings of log lines to look for “black swans” in my logs. A small model running locally would do. Then PCA, SVM - any old school method should work, I guess. Haven’t seen it done but it must be tried by someone somewhere.

The tech industry is not that far off from the fashion industry

Svaka cast :)

This is just the AI grift and uneducated hypebros. There is zero reason to use an LLM as a classifier. But AIbros with zero technical knowledge will push for it anyways even if it is 1000x slower and 1000x more expensive to run.

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