YouTube & Reddit Sentiment Analysis
An end-to-end MLOps pipeline that scores public sentiment and serves predictions to a Chrome extension. My focus was the Flask inference API that puts the model in production.
The problem
A trained model is only useful once it's reproducible, versioned, and reachable from a real product. The project needed an experiment-tracked pipeline and a serving layer the browser extension could query for live predictions and charts.
Approach
Working in a four-person team, we built a tracked pipeline with DVC for data versioning and MLflow for experiments, running on AWS EC2 with artifacts in S3, and a TF-IDF + LightGBM model reaching about 70% accuracy. I built the Flask REST inference API — roughly 329 lines — that loads the registered model from the MLflow registry and serves predictions and chart data to the Chrome extension.
My role
I designed and implemented the inference API and the model-loading path from the MLflow registry — the bridge between the trained model and the extension.