ML That Ships, Not Just Experiments
Most machine learning work never makes it to production. It lives in a Jupyter notebook, performs well on test data, and then sits unused because no one figured out how to deploy it into a real product. I work end-to-end — from data to deployed model to the API your frontend calls.
What I Build
Recommendation engines that suggest products, content, or connections. Predictive models that flag churn risk, estimate delivery times, or forecast demand. NLP pipelines that categorise support tickets, extract information from documents, or analyse sentiment in reviews. Computer vision systems that detect objects, read text from images, or verify identity documents.
Starting From Your Data
I begin by understanding what data you have, what quality it's in, and what questions you're actually trying to answer. Good ML depends on clean data and clear problem framing — I spend real time on this before building anything.
Production-Ready
A model that works on your laptop is not the same as a model that works reliably at scale. I handle model serving, versioning, monitoring for drift, and retraining pipelines so your models stay accurate as data changes over time.
Transparency
I explain what the model is doing and why it makes the predictions it does. If your use case requires explainability — for compliance, for user trust, or just for your own understanding — I build that in.