ML Documentation & Knowledge Transfer
Write model cards, datasheets, and runbooks that outlast their authors
The Documentation Debt Problem
Every ML team accumulates documentation debt. The model that took six months to train and tune is put into production with a one-paragraph README. Six months later, the original engineer has left, and no one knows why the preprocessing does that strange thing with the ZIP codes, or what the threshold in production was set to and why. The solution isn't to write comprehensive documentation after the fact. It's to document decisions in the moment they're made: in pull request descriptions, in experiment notes, in short inline comments. Then aggregate that into structured artifacts (model cards, datasheets, runbooks) before handoff.
The four artifacts that matter most
- **Model card**: Intended use, performance benchmarks, limitations, bias evaluation. Publish for every production model. - **Datasheet for dataset**: How the training data was collected, preprocessed, and labeled. Who annotated it? What are the known biases? - **Runbook**: Step-by-step guide for common operational tasks: retraining, rollback, threshold adjustment. Written for someone woken up at 2 AM. - **Architecture decision record (ADR)**: Short document recording a significant technical decision, the alternatives considered, and the reason for the choice.