LLM, meet ML pipeline. ML pipeline, meet your new build step
Integrating a large language model (LLM) into a traditional machine learning (ML) pipeline can significantly enhance the speed and efficiency of feature extraction. The LLM acts as a preprocessing assistant, transforming messy inputs into structured data that the existing model can utilize effectively. This shift allows teams to experiment more freely, reducing the time spent on feature engineering and improving overall productivity.
- ▪The LLM was added to an existing ML pipeline to improve feature extraction from messy text inputs.
- ▪Initial experiments showed that the LLM could extract structure from text more effectively than traditional methods.
- ▪The integration of the LLM allowed for faster experimentation and iteration, changing the economics of the ML process.
Opening excerpt (first ~120 words) tap to expand
AI LLM, meet ML pipeline. ML pipeline, meet your new build step. So we put an LLM next to a perfectly ordinary ML pipeline, and of course things got weird Matthias Kainer · 2026-05-27 · 11 min read ai llm pipeline ml There is a very specific kind of disappointment that only machine learning can produce. It is the moment when you have finally cleaned the dataset enough that it no longer looks like it was assembled by raccoons fighting in a spreadsheet, you trained a model that is not embarrassing, the metrics are decent enough to stop people from asking whether this was all a waste of money, and then you realise that the thing is still not particularly good. Not bad. Just aggressively, stubbornly average.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Matthias-kainer.
Discussion
0 commentsMore from Matthias-kainer
