Section
AI / ML
Stack
NLP · DistilBERT · Fine-tuning · PyTorch
Category
NLP · Model Fine-Tuning
Status
Baseline trained — evaluation and threshold tuning ongoing
01 — Challenge
The core engineering challenge
Off-the-shelf zero-shot classifiers fail on domain-specific jargon and short utterances — fine-tuning on curated in-domain data is necessary to reach production-quality precision.
03 — Decisions
Engineering decisions and what they cost
01Build the evaluation harness first, before any model training, so metrics are honest from the start.
02DistilBERT over full BERT for 40% faster inference with under 2% accuracy degradation on this task.
03Serve via FastAPI with a confidence threshold endpoint so callers can route low-confidence predictions.
04 — Stack
NLPDistilBERTFine-tuningPyTorchFastAPIHuggingFace