Notebooks & Tracking
Kitchen table
Scientists keep lab diaries: what I tried, what happened, what I changed. Machine-learning people need the same thing, because they run hundreds of experiments and the good one is easy to lose. A notebook is where you think out loud with code – a bit of writing, a bit of maths, a chart, all on one page. The tracker is the diary that remembers every run, so when someone asks 'which settings made the good one?', you can actually answer.
Whiteboard
Jupyter for exploration, MLflow or Weights & Biases for runs, parameters, metrics and artefacts. The rule that keeps teams sane: notebooks are for thinking, not for production. They hide state, defeat diff tools and encourage out-of-order execution, so the path from promising notebook to reviewed, tested module has to be a deliberate step, not a copy-paste. Log everything about a run automatically. The experiment you cannot reproduce is an anecdote wearing a lab coat.
Boardroom
This shelf-corner is about one word: reproducibility. If nobody can say which data and which settings produced last quarter's model, you do not have an asset, you have an anecdote, and when a regulator, auditor or acquirer asks how a decision-making system was built, 'we are not sure' is an expensive answer. The tooling is cheap. The habit is the investment. Insist that model results presented to you come with their lineage attached.
Related concepts