Process
From hypothesis
to handover.
Every project runs the same loop: define the metric, beat a baseline, test on data the model has never seen, then hand over everything. Here's a typical four-week sprint.
Fig. 01 — Momentum SGD on a non-convex loss surface · step 0000
Scope
A free 30-minute call. We agree on the problem, the metric and what "good enough" means before any code is written.
- Problem statement
- Success metric
- Data audit
Baseline
The simplest model that could work, so every later gain is measured against something real.
- Baseline model
- Reference score
Iterate
Tracked experiments, shared weekly. You see the dead ends as well as the wins.
- Experiment log
- Weekly updates
Evaluate
Held-out testing, failure analysis and robustness checks, written up in plain language.
- Held-out results
- Failure analysis
- Robustness checks
Ship
Code, weights, documentation and a handover call. You own everything.
- Code & weights
- Evaluation report
- Handover call
What you get
Everything you need.
Nothing you can't run.
At the end of a project you receive one folder. It reproduces every number in the report, runs on your machines, and doesn't depend on us. Hover over any file to see what it's for.
- 48h
- Reply to every enquiry
- 100%
- Of code and weights handed over
- 0
- Lock-in or hosted dependencies
- client-handover/
- ├── README.mdstart here
- ├── model/
- │ ├── train.py
- │ ├── predict.py
- │ └── weights.ptfinal checkpoint
- ├── notebooks/
- │ ├── 01_baseline.ipynb
- │ ├── 02_experiments.ipynb
- │ └── 03_evaluation.ipynb
- ├── api/
- │ ├── app.pyFastAPI
- │ └── Dockerfile
- ├── report/
- │ └── evaluation.pdfplain language
- └── data/
- └── splits.jsontrain · val · held-out
↳ One folder, yours to keep. Nothing hosted on our side.