Case study 07
Question Answering with BERT on Azure
A fine-tuned BERT QA system, benchmarked against classical baselines and deployed for real-time inference on Azure ML.
Python · BERT · Hugging Face · Azure ML · SQuAD
problem
Matching a user's question to the sentence that actually answers it is deceptively hard. Lexical overlap fails on paraphrase, and deep models are easy to demo but hard to serve with acceptable latency.
approach
I fine-tuned a BERT-based question-answering system on SQuAD to match queries against candidate answer sentences, using semantic similarity over sentence embeddings, and benchmarked it against Jaccard and TF-IDF baselines to prove the deep model earned its cost. The model was deployed on Azure Machine Learning with a scoring pipeline optimized for both latency and accuracy.
outcome
Full-stack ML deployment, demonstrated end to end: fine-tuning, packaging, serving, and real-time inference. The baseline-comparison discipline, measure the simple thing before shipping the complex one, is a habit I've carried into every system since.