From notebook prototype to dashboard people actually use
Exploratory analysis, model training, evaluation, and lightweight Streamlit dashboards — turning your data into decisions your team can act on.
Who this is for
Teams sitting on data they suspect is valuable but haven’t been able to turn into a clear answer or recurring report. Or teams who need a second pair of eyes — a sanity check on a model their internal team has built.
How I work
I start with the decision you want to make, not the model. From there I work backwards to the metric, the data, and the technique. Most projects don’t need a transformer; they need a careful look at the data and a model that’s the right size for the problem.
I prefer to deliver a Streamlit dashboard alongside any model. A static report gets read once; a live tool people can poke at gets used for months.
What you get
- The notebook(s) with full analysis and reasoning.
- The trained model(s), pickled or exported.
- The Streamlit app, deployed to a URL you can share.
- A handover document with methodology, assumptions, and what to revisit later.
Frequently asked questions
I don't have clean data. Can you still help? +
Yes — that's most projects. A meaningful chunk of any ML engagement is data cleaning and feature engineering. I'll be upfront about how much of the budget that's likely to consume.
Do you do deep learning or just classical ML? +
Both. For tabular data I usually reach for gradient boosting first (faster, more interpretable, often wins). For text, vision, or sequence data I'll use TensorFlow or PyTorch, or a pretrained model if one fits.
Will the model run in production? +
If you want it to, yes — I can wrap the trained model in a FastAPI service and deploy it. If the goal is decision support only, the deliverable is the dashboard + writeup.
Do you sign a data NDA? +
Yes. I work under NDA by default.
Ready to start?
A 30-minute scoping call is the fastest way to find out if we're a fit.
Book a call →