Fine-Tuning BERT for the Unstructured Data You Actually Have

Most fine-tuning attention goes to generative LLMs, but a large share of production NLP still runs on BERT-family encoders. They are small, fast, and cheap to serve, and on the tasks where most real data lives (classifying support tickets, extracting fields from documents, routing emails, semantic search) a fine-tuned BERT often matches or beats a prompted frontier model at a fraction of the cost and latency. ​In this hands-on workshop, we'll fine-tune an open-weight BERT model on a custom text dataset and deploy it behind a simple UI. Base BERT is small enough that full fine-tuning runs comfortably on a single GPU. The whole pipeline runs on Flyte 2/Union, so data prep is cached, runs are reproducible and recoverable, and the same…